就业能力 AI素养

No abstract available

Artificial Intelligence (AI) Literacy has become an indispensable competency for citizens in the digital age. The definition of AI literacy, AI literacy education and the approaches to implementing AI literacy education have increasingly become the focus of researchers. Meanwhile, the challenges that emerged in those studies involved unclear AI literacy definitions, a shortage or incapacity of personnel in AI literacy education, and the ethical use of AI. The purpose of this study is to assess the AI literacy level of pre-service elementary school teachers and whether their AI literacy moderates the impact of their attitudes toward the rule of AI in teaching on their employability. One hundred forty-four preservice elementary school teachers take part in the study. The results show that the $\mathbf{A I}$ literacy of pre-service elementary school teachers is notably high, exceeding anticipated levels. Pre-service teachers’ attitudes towards the role of AI in education and their AI Literacy are significant predictors of employability. However, their AI Literacy does not moderate the relationship between their attitudes toward AI and employability. The findings have both theoretical and practical significance for policymakers to be aware of the AI literacy of pre-service teachers and design and implement AI literacy education accordingly.

In English for Academic Purposes (EAP) courses developed for engineering students, employability is a popular theme. To address the challenges posed by Generative AI to employability, this study explores how both the teacher and the students can build AI agents to facilitate the development of L2 job interview skills. AI Interest Group (AIG) (N=12) used and built AI agents with teacher guidance, Group A (N=14) used AI informally, and Group B (N=11) did not use AI in preparation for an L2 job interview. Mixed methods were used to examine students’ job interview performance and anxiety change, their perceived benefits and the relevance of AI literacy incorporation in an EAP course. One-way ANOVA analysis showed no significant difference in performance scores between the AIG group and the other two, but a significant difference between Groups A and B. Mixed ANOVA analysis showed a significant drop in anxiety levels across all groups, with a notably larger decrease among the AIG students. Thematic analysis from the interview data endorsed AI’s role in scaffolding, explaining the drop in anxiety level. All AIG students successfully built AI agents. They perceived this incorporation to be extremely relevant and expressed a strong need for AI literacy development. The study implies it is possible and might also be necessary to incorporate AI literacy development in EAP courses. Future research could explore larger sample sizes and examine more controlled teaching and learning practices once institutional policies have matured.

PurposeArtificial intelligence (AI) has been used in the workplace for years. There are compulsory or elective AI courses in some universities for students to enroll in. With the wide adoption of AI in many industries now, employers also expect students to make good use of AI to enhance their work productivity and efficiency. Hence, the purpose of this study is to examine the relationship between AI literacy and perceived student employability.Design/methodology/approachThe purposive sampling method was used in a higher educational institution in Hong Kong. About 208 valid responses were obtained from university students. Partial least square structured equation modeling was used in the analysis.FindingsIt was found that acceptance of using AI could be determined by perceived usefulness of AI and trust in AI. Attitude toward AI could be influenced by acceptance of using AI. Attitude toward AI is also found to be associated with AI literacy, which affects internal and external employability. Students with more perceived AI knowledge could find it easier to get jobs.Originality/valueThis study contributes to a better understanding of the relationship between perceived AI literacy and employability. Higher education should also consider how to prepare students for a new generation of jobs with AI competence as requirements by reassessing their curricula.

Transformative potential of the knowledge economy of the XXI century, establishment of networked society, emergency digitization due to the pandemic and wartime measures have imposed elaborate interdisciplinary and interoperable demands on the marketability of Liberal Arts skills and competences, upon entering the workforce. This study examines the gap between transdisciplinary future skіlls hіghlіghted іn the World Economіc Forum's (WEF) "Future of Jobs" reports and those sought by learners іn Coursera's Global Skіlls іndex. The emphasis lies on the role of Core skills combined with the Inner Development Goals (IDGs) framework in bridging these gaps. The proposed strategy roadmap links IDGs with the demands for future skills and Humanity-focused Higher Education (HiEd), besides, it provides actionable recommendations for HiEd staff, business schools and policymakers. By combining Inner Development with Leadership Skills and Digital Skills Programs in HiEd we may have a hope to stimulate employability for the AI age both for individuals' inner growth and collaboration/co-creation skills in teams and larger communities in a turbulent job market of 2025-2050. The study results disclose the comprehensive review of dynamics of the digital skills development and application to construe interdisciplinary, AI-interoperable competencies of students and educators in Europe through the span of educational activities in the time-frame of the pandemic emergency digitization measures of 2020-2021 and wartime emergency digitization measures of 2022-2024 in Ukraine (including AI-enhanced communication as a staple of transdisciplinary education as of 2023). The paper introduces a model of AI-interoperable digital skills for education and professional application in different social spheres. The survey analysis is used to evaluate the dimensions of interdisciplinarity, informed by the interoperability of soft skills, professional communication skills, and digital skills across contrasting frameworks of e-competence, Inner Development Goals, professional digital communication, and professional training.

AI literacy is increasingly important in college students’ academic achievement, daily life, and future employability. However, current research predominantly overlooks the heterogeneity in students’ AI literacy, especially how individual psychological characteristics and features of AI technology contribute to this variation. This oversight limits the formulation of tailored strategies to meet the students’ various demands in an era shaped by rapid AI advancement. This study aims to adopt an individual-centered approach to identify distinct AI literacy profiles among college students. In addition, it investigates, based on affordance theory, how positive emotions, instrumental motivation, perceived ease of use, and psychological anthropomorphism predict assignment to different profiles. A total of 808 Chinese college students participated in this survey. Latent profile analysis (LPA) was employed to classify students into distinct AI literacy profiles. Multinomial logistic regression was conducted to examine how psychological and technological factors predict profile classification. This study identified four distinct AI literacy profiles among college students: preliminary contact type, ethical orientation type, balanced development type, and behavioral conservatism type. These profiles showed significant differences in positive emotions, instrumental motivation, perceived ease of use, and psychological anthropomorphism, highlighting diverse psychological and technological characteristics inherent to each group. This study underscores the heterogeneity of AI literacy within the college student population and detects four distinct AI literacy profiles with unique psychological and technological traits. The findings indicate that students’ AI literacy is profoundly affected by emotional tendencies, motivational drives, and technological variables, highlighting the need for tailored educational strategies that address the distinct psychological and technological drivers of each literacy profile.

This analytical study examines the awareness and usage of Artificial Intelligence (AI) tools among commerce graduates and analyses their impact on employability skills. With the increasing integration of AI in education and business environments, commerce students are adopting AI tools for learning, research, communication, and skill enhancement. The study aims to assess the level of awareness of AI tools, frequency of usage, skills developed through AI applications, and challenges faced while using such tools. Primary data was collected using a structured questionnaire from commerce graduates. Statistical tools such as Chi-square test, and Ranking method were used for analysis. The findings reveal that AI tools significantly contribute to improving communication skills, analytical ability, productivity, and digital literacy. However, issues such as lack of technical knowledge, over-dependence on AI, and data privacy concerns were identified as major challenges. The study provides insights into how AI tools can be effectively utilized for enhancing employability skills among commerce graduates.

No abstract available

In Indonesia, rapid AI adoption and digital transformation in various sectors have created new demands for graduates to possess both technological literacy and employability skills. Many fresh graduates face a skills mismatch, as traditional curricula lag behind industry needs, requiring not only technical knowledge but also critical thinking, problem-solving, and adaptability. This study aims to analyze the effects of AI Knowledge and Job Market Awareness on Fresh Graduates’ Quality with Employability Skills as a mediating variable. This research adopts a quantitative approach using a survey method. Data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) to examine the relationships among the research variables. This study finds that AI Knowledge and Job Market Awareness positively and significantly influence Employability Skills and Fresh Graduates’ Quality. Employability Skills have a direct effect on graduate quality and also mediate the effects of AI Knowledge and Job Market Awareness, indicating that skill development is the main pathway through which technological understanding and labor market awareness enhance graduates’ work readiness and overall quality. Based on the findings, students are encouraged to strengthen their critical evaluation skills when using AI tools, develop a more realistic understanding of job market conditions, enhance their ability to assess the accuracy and relevance of digital information, and consistently demonstrate ethical and responsible behavior in academic and professional activities. Future research is recommended to involve broader samples and additional variables to improve generalizability and to further examine factors influencing fresh graduates’ quality in AI-driven labor markets.

Preparing graduates for employability has become a critical focus for higher education institutions in response to the rapidly changing global workforce influenced by technological advancements and artificial intelligence (AI). This study examines the employability of graduates from Sultan Idris Education University (UPSI), Malaysia, using a quantitative approach to analyze the factors affecting employability in today’s digital and AI-driven job market. The research examines the connection between academic performance, essential soft skills such as communication, teamwork, problem-solving, digital literacy, adaptability to AI technologies, co-curricular activities, and work experience, which includes internships. The findings emphasize the growing significance of digital competencies and AI literacy in addition to traditional academic qualifications. While academic performance continues to be an important factor, soft skills, and practical experience are recognized as crucial elements for obtaining meaningful employment. Graduates who possess both technical expertise and interpersonal skills are better positioned to succeed in a competitive job market. The study provides well-considered recommendations for enhancing curriculum design, integrating AI tools into career services, and advancing digital skill development within UPSI’s programs. It offers significant insights into the evolving landscape of graduate employability in relation to AI and technological disruption. Furthermore, the study presents practical guidance for institutions seeking to equip graduates with the essential skills required for success in future job markets.

ABSTRACT This viewpoint article explores the impact of fast-paced advancements in artificial intelligence (AI) technologies on the hospitality industry’s workforce, focusing on the employability and career sustainability of today’s hospitality education graduates. AI technologies reshape traditional job roles and introduce new demands for resilience, adaptability and emotional intelligence. Hospitality graduates must develop human-centric skills and technical proficiency to ensure career sustainability. The article recommends revising hospitality curricula to incorporate digital literacy, emotional intelligence and lifelong learning, enabling graduates to thrive in a technology-driven future. Collaboration between educators and industry stakeholders is critical for preparing a resilient workforce to navigate ongoing technological disruptions.

The rapid expansion of Artificial Intelligence (AI) and Machine Learning (ML) technologies is reshaping higher education, creating new expectations for digital competence and employability. This study explores university students’ perceptions of AI integration into education and its perceived impact on their career development. Data were collected through an online survey administered to students from several universities in Western Romania, focusing on three key dimensions: awareness of AI concepts, understanding of ethical implications, and readiness for AI-driven workplaces. The findings reveal a predominantly positive attitude toward AI and ML, with most respondents acknowledging their transformative potential in enhancing learning efficiency, creativity, and professional growth. Over 85% of students agree that AI literacy and basic ML knowledge should be integrated into academic curricula across disciplines. However, the results also highlight a gap in students’ understanding of ethical and moral aspects related to AI applications, such as data privacy, bias, and algorithmic accountability. The study underscores the need for higher education institutions to promote both technical and ethical AI literacy, combining computational thinking with digital responsibility. By aligning educational practices with the evolving demands of the labour market, universities can better prepare students for careers in a data-driven and automated society.

This study investigates the implications of ChatGPT, an AI-powered language model, for students and universities by examining the perceptions of scholars and students. The responses of seven scholars and 14 PhD students from four countries – Turkey, Sweden, Canada and Australia – are analysed using a thematic content analysis approach. Nine key themes emerge from the findings. According to their frequency of recurrence, these themes are: “Evolution of learning and education systems”, “changing role of educators”, “impact on assessment and evaluation”, “ethical and social considerations”, “future of work and employability”, “personalized learning”, “digital literacy and AI integration”, “AI as an extension of the human brain”, and “importance of human characteristics”. The potential benefits of AI in education as well as the challenges and barriers that may arise from its integration are discussed in the context of existing literature. Based on these findings, suggestions for future research include further exploration of the ethical implications of AI for education, the development of strategies to manage privacy concerns, and the investigation of how educational institutions can best prepare for the integration of AI technologies. The paper concludes by emphasizing the importance of understanding the potential opportunities and challenges associated with AI in higher education and the need for continued research in this area.

This paper investigates graduate skill gaps from a work-based learning (WBL) perspective by examining triadic differences in how students, lecturers and employers perceive employability skills in an applied university context. A convergent mixed-methods design was employed, integrating survey data from 537 stakeholders (341 students, 102 lecturers and 94 employers) with thematic analysis of qualitative responses. Significant misalignments exist: lecturers rated students' technical skills highest, while students overestimated their soft skills. While quantitative data showed no significant gap in digital skills, qualitative insights revealed a “digital-readiness paradox” where employers expressed deep concerns about graduates' ability to handle data-driven and AI-integrated workflows. The study is limited to a single applied institution. Future research should involve longitudinal tracking of graduates to measure the actual performance-perception gap. Universities must shift from generic digital literacy to “AI-readiness” and strengthen WBL through industry-led simulations and authentic assessments. This study advances the employability literature by adopting a unique triadic analytical framework to contrast the perceptions of students, lecturers and employers. While prior research (e.g. Nguyen, 2024) has focused on individual agency, this paper unearths the “digital-AI readiness gap” – a latent misalignment in data-driven workflows often missed by standard surveys. By integrating mixed-methods data within an emerging Southeast Asian context, the study provides a strategic roadmap for recalibrating work-based learning to meet the shifting technological demands of the 2025–2030 labor market.

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The article presents an intelligent information system (IIS) developed to automate the formation of competencies and learning outcomes based on professional standards. A distinctive feature of the IIS is the integration of professional standards with the Atlas of New Professions, which allows adapting educational programs to the dynamically changing requirements of the labor market and technological transformations. The key functional capabilities of the system are described, including user authentication, generation of competencies and learning outcomes for the design of educational programs. The implementation of the system includes an interactive JavaScript interface with support for asynchronous sending of requests to the server using AJAX technologies. OpenAI generative models are used to automatically generate competencies and learning outcomes. The presented system has a wide range of potential applications: from designing curricula based on competencies to creating career guidance systems, analyzing and forecasting changes in the labor market, as well as adapting educational programs to the requirements of high-tech industries. Thus, the developed model contributes to the digitalization of education, improving its quality and ensuring that educational standards meet the modern challenges of the knowledge economy.

Islamic boarding schools (pesantren) have long sustained moral formation and classical Islamic scholarship, yet rapid digital transformation and shifting labor-market demands require santri to develop 21st‑century competencies alongside religious mastery. This study aims to analyze strategies for integrating 21st‑century skills into the pesantren curriculum without eroding pesantren identity, using Pesantren Al‑Yasini as a case. A qualitative case-study design was employed, drawing on participant observation, in-depth interviews with pesantren leaders, teachers, students, and alumni, and analysis of curriculum and policy documents. The findings show a shared recognition of the urgency of strengthening critical thinking, communication, collaboration, creativity, and digital literacy. Integration is most feasible when these competencies are mapped into learning outcomes, content, pedagogy, and assessment, and operationalized through value-guided technology use, hybrid learning routines, and project-based activities (e.g., sharia entrepreneurship and leadership projects). Key enabling factors include leadership commitment, teacher readiness, and curriculum flexibility, while constraints include uneven infrastructure and resistance to pedagogical change. The study concludes that a staged, value-based integration model can enhance santri competitiveness while preserving the pesantren’s core mission. This research contributes an empirically grounded framework to inform curriculum development and policy support for pesantren in the digital era.

This study examines demand for electronics vocational teachers in Bangkok and surrounding metropolitan areas and identifies qualifications required in the digital workforce era. A mixed-methods design was employed. Quantitative data were collected via structured questionnaires from 200 respondents across enterprises and educational institutions; analyses included descriptive statistics (mean, SD), one-way ANOVA, and multiple regression. Qualitative data were gathered through in-depth interviews with 17 purposively selected informants and analyzed using content analysis. Results show enterprises most value skills in Artificial Intelligence (AI) and the Internet of Things (IoT) (Mean = 4.30, SD = 0.82), while institutions report shortages in automation-system expertise (Mean = 4.10, SD = 0.78). Perspectives significantly differ between enterprises and institutions (ANOVA, p < 0.05). Regression indicates industrial technology trends (β = 0.45, p = 0.001), AI/IoT skills (β = 0.42, p = 0.002), and practical teaching ability (β = 0.38, p = 0.003) as the strongest predictors of required qualifications. The study recommends reforming electronics curricula to integrate automation, AI, IoT, and embedded systems; emphasizing project-based and work-integrated learning; and strengthening entrepreneurial competencies. Certified teacher upskilling and sustained industry collaboration are essential to ensure relevance. The research offers an actionable framework to align vocational education with industry needs and develop future-ready electronics teachers in Thailand.

Artificial Intelligence (AI) is transforming the global workforce by automating routine tasks, augmenting human capabilities, and creating new job opportunities. This paper explores the multifaceted impact of AI on the future of work, considering both its potential to disrupt and enhance various industries. The paper examines the accelerating adoption of AI across sectors such as healthcare, transportation, finance, education, and defence, highlighting its role in improving productivity, decision-making, and strategic tasks. While AI presents opportunities for increased efficiency, it also raises significant concerns, particularly regarding job displacement and the need for reskilling. The analysis distinguishes between the effects of AI on blue-collar and white-collar roles, noting that repetitive, low-skilled jobs are at greater risk of automation, while higher-skilled, strategic roles may be augmented rather than replaced by AI. Furthermore, the paper discusses the emerging job categories resulting from AI advancements, such as AI ethics officers and data scientists, and the growing importance of hybrid teams combining human and AI strengths. Additionally, the paper emphasizes the role of proactive adaptation through upskilling, reskilling, and strategic workforce planning in mitigating the risks posed by AI, ensuring a more inclusive and adaptable workforce. Ultimately, the paper argues that AI is a tool whose impact depends on how it is managed, and that lifelong learning will be critical in preparing workers for the future of work

This study explores how emerging technologies, particularly Artificial Intelligence (AI) and automation, are reshaping skill demands for India’s entry-level workforce. Given the country’s large youth population and growing service economy, the impact is especially significant. Through secondary data analysis and stakeholder insights, the paper examines how technological change disrupts traditional employment models, redefines workforce skills, and necessitates policy reforms in education and skilling. It highlights both the challenges and opportunities facing early-career professionals in India’s rapidly evolving labour market.

The integration of Artificial Intelligence (AI) tools into university curricula is crucial for addressing the skills gap in the workforce driven by the Fourth Industrial Revolution (4IR). However, challenges such as curriculum misalignment, faculty expertise, and ethical concerns hinder widespread adoption. This study aimed to explore the key factors influencing the integration of AI tools into higher education, using the Technology-Organisation-Environment (TOE) framework. A qualitative literature review was conducted to analyze current research and identify both barriers and enablers of AI adoption in academic programs. Findings revealed that AI has the potential to enhance student performance and employability but is constrained by resource limitations, faculty training needs, and ethical considerations surrounding data privacy and AI usage. Six critical success factors were identified: curriculum alignment with industry needs, faculty training, ethics education, access to AI tools, infrastructure, and resource allocation. The study also highlighted the importance of university-industry partnerships to ensure curriculum relevance and practical experience. The research provides actionable recommendations for educators and policymakers, emphasizing continuous curriculum development, strategic resource investment, and ethical frameworks. Future research should examine the long-term impact of AI integration on workforce readiness and academic outcomes, advancing the understanding of educational innovation in an AI-driven world.

The rapid digital transformation of the electronics industry has fundamentally reshaped workforce competency requirements, creating new challenges for vocational teacher education. This study investigates labor-market demand for electronics vocational teachers in Bangkok and its metropolitan area, with particular attention to the skills and qualifications required in the digital workforce era. A mixed-methods research design was employed. Quantitative data were collected from 200 respondents representing enterprises and educational institutions and were analyzed using descriptive statistics, one-way ANOVA, and multiple regression. Qualitative data were obtained through in-depth interviews with 17 key informants and analyzed using content analysis. The findings indicate that enterprises place the highest value on competencies in Artificial Intelligence (AI) and the Internet of Things (IoT). In contrast, educational institutions report a critical shortage of teachers with expertise in automation systems. Statistically significant differences were identified between industry and academic perspectives regarding both teacher demand and required competencies (p < 0.05). Regression analysis reveals that industrial technology trends, AI and IoT skills, and practical teaching competence are the strongest predictors of electronics teacher qualification requirements. Based on these findings, the study proposes a future-oriented curriculum framework for electronics teacher education that integrates automation, AI, IoT, embedded systems, work-integrated learning, and entrepreneurial skill development. The study provides empirical evidence supporting curriculum reform in vocational teacher education. It provides practical guidance on aligning educational provision with the evolving needs of Thailand's electronics industry within the digital economy.  

This study applies a qualitative case study methodology to explore how artificial intelligence (AI) training influences workforce competency at a Hungarian IT services company. Through structured interviews with AI trainers and managers, the research identifies specific barriers to effective training, such as rapid technological change, employee resistance, and outdated learning content. The findings demonstrate that AI training enhances adaptability, fosters essential digital skills, and supports organizational growth. Key success factors include the use of tailored learning paths, real-time feedback systems, and foundational AI education. These components not only address misconceptions about AI but also prepare employees to thrive in digitally transformed environments. The study contributes practical insights to developing training programs that support long-term workforce resilience.

Specific industries are evolving at a faster pace driven by digitalization, automation and artificial intelligence, and have further increased the demand for a workforce now significantly agile, data savvy and skilled with advanced talent sets. Higher education is facing mounting pressure to deliver job-ready graduates through curricula aligned to skills requirements in industry. But now, data driven predictive analytics powered with AI have started to emerge as a disruptive force to enhance workforce readiness. This article provides an overview of predictive analytics application in higher education and implementation of AI and big data to improve student performance, career outcome and reduce disparity between academia and industry. Predictive Analytics is a predictive approach that looks at the past and current trends, behaviors, and outlooks based on previous trends. In the context of tertiary education, the predictive models driven by AI can be utilized to tap this abundance of data in the form of academic performance, learning management systems, online interactions and employer feedback to demonstrate patterns that define curriculum, skill development programs, and career guidance. Institutions have been able to use this opening-for-use of technologies to deliver student success and retention, customized learning solutions and satisfying workforce needs. And thus, curriculum optimization is one of the most prominent uses of predictive analytics in the context of the workforce readiness. Insights on AI are also used to decide whether programs are capable of following new trends in the industry. Based on statistics on job market demand and employer patterns in hiring and in-demand skills, the universities can design courses to incorporate in-demand technical and soft skills. This proactive way of thinking also makes graduates of the school leave behind them, skill sets that make them more employable or set them in a fast-tracked career. Moreover, mainframe predictive analytics assists with the monitoring of student performance and the generation of intervention plans. AI models research the attendance, the submission rates of assignments, and activities with course materials with the aim of identifying students who may soon fall into academic distress. This information can then be used by HEIs to implement interventions - whether tutoring, mentoring or adaptive learning tools - that are particular to the needs of the concerned students with the intent of yielding better results.

The hospitality industry faces significant workforce challenges, increasingly turning to Artificial Intelligence (AI) to reshape talent management. However, the rise of automation poses questions for an industry reliant on human connection. This short paper explores the tension between AI adoption and preserving the "human touch," drawing on research into Gen Z workforce expectations, scenario planning for AI's future role in hospitality work, the impact of algorithms on HR decisions, and the need for evolving hospitality education. While AI offers benefits like administrative efficiency and personalized learning, its deployment risks depersonalization and ethical issues if not carefully managed. The core message emphasizes that AI should support and enhance, not replace, the human element central to hospitality, requiring professionals equipped with AI literacy and critical thinking skills.

Purpose: This paper explores how Artificial Intelligence (AI) reshapes the job market and redefines the skill requirements for the contemporary workforce. It highlights the widening gap between traditional academic training and the competencies demanded by an AI-driven economy, especially in relation to Generation Z learners. Design/Approach/Methods: The study reviewed current literature, examined successful case studies of university-industry collaborations, and analyzed trends in workforce readiness. It proposed an educational framework that aligned with the learning preferences of Generation Z—emphasizing active, experiential, and interdisciplinary approaches integrated with AI tools and ethics. Findings: The research identified a pressing need for curriculum innovation that fostered both technical and soft skills, such as creativity, adaptability, and ethical decision-making. It supported the integration of AI-centric internships, industry-led workshops, and collaborative laboratories to provide students with practical exposure. Evidence suggested that these strategies enhanced employability and readiness for dynamic work environments. Originality/Value: This paper contributed a forward-looking perspective on curriculum development for the AI era, with a particular focus on the role of management education in driving this transition. It offered practical recommendations to bridge academia and industry, ensuring that Generation Z graduates are equipped to thrive in a rapidly evolving professional landscape.

Software development is undergoing a revolutionary transformation, fueled by remarkable advancements in Large Language Models (LLMs). This wave of innovation is reshaping the entire landscape and holds the promise of streamlining the development process, leading to increased productivity and efficiency. By providing text prompts, developers can now receive entirely generated code outputs, representing a fundamental shift in how software is built. This paradigm change can accelerate development cycles and unlock new levels of creativity and ingenuity, resulting in the realization of novel applications and business outcomes. However, this paradigm shift also brings new challenges and necessitates acquiring additional skills for software developers to fully harness the capabilities of LLM-powered tools. These skills include prompt engineering for software development, structural complexity management, debugging of AI errors, and compliance with ethical guidelines and principles. The special session will introduce our NSF-sponsored 3-year project, which aims to integrate LLMs into the standard CS curriculum. To the best of our knowledge, this project is among the first department-level initiatives to renovate CS curriculum, rather than individual courses, with the new developments of LLMs. Our project focuses on (a) enhancing students' problem-solving and programming skills by leveraging LLMs as a learning tool in core programming courses, (b) improving students' software development skills by integrating LLM-powered tools into the software engineering course sequence, and (c) educating students on ethical and responsible AI practices. The special session will discuss the objectives and methods of our project, as well as the current results and lessons learned.This NSF-supported project aims to integrate LLMs into the standard CS curriculum. The revolutionized computer science education will cultivate a new generation of AI-powered responsible developers. The objectives are to enhance student programming, software development, and problem-solving skills; educate students on ethical and responsible AI practices; and develop faculty development materials and workshops. Our presentation will discuss the objectives and methods of our project, currently in year 1 of a 3-year timeline.

We propose that metacognitive skills and metacognitive thinking will become increasingly important for effective use of AI (Artificial Intelligence) systems. As the collaborative capability of AI systems improves, humans will spend more of their time working with AI. This is expected to uniquely influence the human decision-making process. We identify four characteristics that differentiate human-AI interactions from human-human interaction, each of which is likely to affect our thinking and decisions. These are (1) the accuracy of our cognitive heuristics for predicting the behaviour of AI systems, (2) AI’s limited capability when dealing with novel and ill-defined problems, (3) the lack of a natural, reciprocal feedback mechanism in AI systems and (4) the inability of AI systems to engage in metacognition. Drawing upon the dual-process theory of human thought process, we argue that these characteristics will diminish the efficacy of the system one mode of human thinking, making metacognitive thinking skills important to ensure effective use of AI systems. We conclude by describing how this need can be addressed through training and AI design.

The use of AI has surged in most aspects of our lives, and the education and skill development era is no different. As we look to a future where AI will enhance the workforce, ensuring that the next generation has the skills it needs to adapt to this rapidly changing job market is essential. Education AI can leverage education to personalize learning and provide real-time feedback, making the learning experience more streamlined and efficient. Finally, applying AI to skill development programs may enable more reliable assessments and job-relevant skills, giving individuals a competitive edge in the job market. In summary, AI promises to help the future workforce be ready by providing them with the skills they will need in a world powered by technology..

This study investigates the current state of Artificial Intelligence (AI) integration in Information Technology (IT) education, including the adoption of AI-powered tools, the development of AI-related curricula, and the impact of AI on student learning outcomes. Employing a mixed-methods approach, the research combines quantitative and qualitative data to analyze AI integration in IT education. Findings reveal a significant gap between current AI integration in IT curricula and the skills demanded by AI-driven industries. While respondents demonstrated improved critical thinking and problem-solving skills when engaged in AI-powered learning, challenges such as insufficient faculty training, inequitable access to AI tools, and inadequate emphasis on ethical considerations were identified. The study recommends expanding AI-related topics in curricula, incorporating hands-on learning opportunities, and equipping educators with skills for effective AI instruction. By addressing these gaps, this study contributes to the achievement of SDG 4 (Quality Education) by improving the quality and accessibility of IT education and equipping students with the necessary skills for the 21st-century workforce. Moreover, it supports SDG 8 (Decent Work and Economic Growth) by preparing students for in-demand AI-related careers and contributing to economic development.

The integration of Artificial Intelligence (AI) and gamification into higher education is reshaping educational practices by personalizing learning and fostering essential workforce skills. This study critically examines the effectiveness of these technologies, their impact on student engagement, and the factors influencing students’ acceptance.A systematic literature review complemented by Topic Modeling using Latent Dirichlet Allocation (LDA) identified key research themes. Subsequently, predictive modeling with machine learning algorithms, hyperparameter optimization, and Local Interpretable Model-Agnostic Explanations (LIME) were applied to classify academic documents and interpret influential factors.Findings indicate that AI effectively customizes educational pathways, enhancing engagement and academic performance. Gamification notably supports soft skill development, providing more interactive assessments than traditional approaches. However, challenges related to data privacy and technological accessibility remain significant, particularly affecting international students and institutions with limited resources.AI and gamification demonstrate considerable potential for transforming higher education through personalized learning and interactive skill assessments. Nevertheless, widespread adoption depends on addressing data privacy concerns and ensuring technological equity. Future research should investigate the long-term implications of these technologies in developing students’ adaptability within a dynamic global workforce.

This perspective piece addresses the rapid integration of generative artificial intelligence (AI) in higher education and the imperative to move beyond a purely technical understanding towards fostering critical AI literacy among students. Despite the benefits of AI in enhancing learning experiences and preparing students for a tech-driven workforce, concerns exist regarding misinformation, diminished critical thinking, ethical dilemmas, and a lack of regulatory frameworks. This perspective piece proposes a circular pedagogical framework comprising contextual preparation, guided engagement, and collective critical reflection, drawing on Vygotsky’s sociocultural theory, Freire’s critical consciousness, and Mackey and Jacobson’s metaliteracies framework. The framework aims to address three critical competency gaps: AI tool assessment, critical AI evaluation skills, and AI information literacy. The paper highlights the importance of discipline-specific AI integration and scaffolded learning, supported by student reflection and metacognition, as demonstrated in the geography seminar courses discussed in the paper. Recognizing the need for instructor AI literacy, the paper concludes by emphasizing the necessity of institutional support through targeted training and interdisciplinary collaboration to ensure AI enhances learning effectively.

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Because the world is becoming very digitized rapidly, this research paper studies how artificial intelligence can contribute to filling the skills gap between education HEIs and industry needs. The biggest disparities revealed in the study were the differences in skills produced by traditional academic frameworks versus skills demanded by employers, including digital literacy, artificial intelligence, and soft skills. The COVID-19 pandemic has sped up the digital revolution of education exposing critical gaps in technological readiness across different parts of the world. Integrated use of AI-powered tools embeds the ability to customize learning experiences, improve student engagement, and tailor curricula to market needs that can all contribute to improving workforce readiness. The paper includes that internships, apprenticeships, and hands-on projects are needed to allow for collaborative partnerships between academia and industry to enable students to gain relevant skills. The research also identifies barriers to AI adoption such as resistance to change, a worry over future data privacy, and standards around the ethics of using AI in educational practice. Finally, the findings indicate that education and talent acquisition can be narrowed by relying on AI for growth, innovation, and continuous learning. Finally, strategic investments in faculty development and technology infrastructure are needed to support this integration and to prepare students for future labor market challenges, the study concludes.

The Fourth Industrial Revolution, in which artificial intelligence (AI) affects the economic and social systems of our world, is a game changer for labour markets across the globe. With its large and youthful human capital, India is at a crossroads. In this study, we explore the implications of AI for the Indian workforce, considering its potential both as a force of disruption and as a source of unprecedented growth. This work is about taking the discussion beyond the old automation lens towards how AI is changing the essential skills and competencies taxonomy of the future. Using a mixed-method approach, combining secondary data analysis of contemporary industry vision papers (NASSCOM, WEF, McKinsey) and government initiatives, this study identifies critical emergent skill clusters—AI Literacy, Cognitive Flexibility, Socio-Emotional Intelligence, and Digital Dexterity. The paper suggests that India can capitalise on this demographic dividend only through a complementary approach of large-scale, multi-sectoral skilling, educational syllabus transformation, and proactive policy interventions in creating a habit of lifelong learning. The conclusion provides a strategic roadmap for India to both address the challenges posed by job displacement due to AI and gain the leading edge in the upcoming AI-driven economy.

Abstract: Technical abilities alone are no longer enough in the changing environment of the world job market. Employers are progressively valuing soft abilities including cooperation, emotional intelligence, adaptability, and communication. The possibilities of artificial intelligence-driven educational systems for developing these core abilities are investigated in this research. This study uses a secondary data-based approach to combine current scholarly literature, institutional reports, and global policy documents to assess how artificial intelligence improves students' preparedness for job through soft skills development. The results point to an increasing integration of artificial intelligence instruments in the development of human-centric abilities, therefore closing the divide between academic education and job requirements. Keywords: soft skills, employability, AI in education, digital learning, adaptive learning, workforce readiness, secondary data

This study explores the impending impacts of Artificial Intelligence (AI) on work environments, forecasting significant shifts in the skill sets of emerging talents across various industries over the next decade. Despite AI's technological strides, industry's lags in the data management and utilization capabilities. A significant obstacle to innovation is the shortage of a skilled workforce. This paper highlights the necessity to develop comprehensive skill sets across all organizational tiers to unlock data utility and to fully leverage AI's potential to the products, processes and services. Drawing on datasets from Finland, the paper elucidates the current state and needs of the manufacturing industry and identifies pressing skill requirements. It argues that companies aiming for a leading position in the AI era must focus on updating skills and consider collaboration with universities to synchronize educational curricula with forthcoming industry requirements. For this purpose, paper addresses views of business and ICT lecturers regarding AI skills development. The paper continues to propose examples of learning environments supporting reskilling and upskilling initiatives, ensuring a smooth industry transition to meet the evolving exigencies of the AI landscape.

The rapid adoption of Artificial Intelligence (AI) in various industries has significantly impacted the development of digital talent, particularly in enhancing problem-solving skills. This study aims to examine the impact of AI on the development of problem-solving abilities in digital talent. A quantitative research approach was used, with participants undergoing AI-assisted training designed to improve their problem-solving capabilities. Pre-test and post-test evaluations were conducted to measure changes in participants' problem-solving skills. The data were analyzed using statistical methods to identify significant differences before and after the AI intervention. The findings indicate that the use of AI positively contributes to the improvement of problem-solving skills among digital talent, providing insights into how AI can be leveraged to better prepare the workforce for the demands of the digital economy.

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Transformative potential of the knowledge economy of the XXI century, establishment of networked society, emergency digitization due to the pandemic and wartime measures have imposed elaborate interdisciplinary and transdisciplinary demands on the marketability of Liberal Arts university graduates' skills and competences, upon entering the workforce. The study is focused on the in-depth diagnostics of the development of multipurpose orientation, universality and interdisciplinarity of skillsets for students of European (English, Spanish, French, Italian, German) and Oriental (Mandarin Chinese, Japanese) Languages major programs through the span of educational activities in the time-frame of sustainable and emergency digitization measures of 2020-2024 in Ukraine. A computational framework of foreign languages education interdisciplinarity is introduced in the study. The survey analysis is used to evaluate the dimensions of interdisciplinarity, universality and transdiciplinarity, informed by the interoperability of soft skills and digital communication skills for Foreign Languages Education across contrasting timeframes and stages of foreign languages acquisition and early career training.

Following the rise of generative artificial intelligence technology, the educational industry has gradually become more innovative. But opportunities always coexist with challenges. The way to collaborate Human-AI relation in education needs to be concentrated on. This paper explores the practical application and effects of GenAI in tiered teaching implementation and student English capability development for higher vocational students upgrading to undergraduate colleges by using a mixed-methods approach. The study observed measurable improvements in students' language proficiency, application ability, and critical thinking skills. Specifically, instant feedback from AI allowed them to refine the grammar, translation, writing and other language skills when they finish personalized learning and human-AI collaborative tasks. However, the survey also reveals technology dependency and insufficient evaluation criteria for AI-enabled tasks. To solve these problems, the author suggested to implement “human-AI collaboration” mode to concentrate on proper evaluation criteria, ethical education, and dynamic monitoring mechanisms. By doing so, it will be worthy data in English education and verify the unique value of GenAI in balancing college education with personalized cultivation.

The rapid evolution of technology has transformed the relationship between humans and intelligent systems, shifting from basic automation to highly interactive and adaptive collaboration. Intelligent Adaptive Technologies (IAT) represent this new phase, where AI systems are designed to learn from human behavior, adjust to changing tasks, and provide timely support that strengthens decision-making and workplace efficiency. Rather than replacing human capability, these systems work alongside individuals, helping to improve accuracy, productivity, and innovation in everyday operations. This research explores how Human–AI collaboration through adaptive technologies influences organizational performance, particularly in the sectors of education, healthcare, and business services. A quantitative study was carried out with a sample of 210 participants, and data was analyzed using descriptive statistics, chi-square analysis, regression methods, and Structural Equation Modeling (SEM). The findings indicate that intelligent adaptive systems have a strong positive impact on employee productivity (β = 0.62, p < 0.001), accuracy in decisions (β = 0.54, p < 0.001), and overall user satisfaction (β = 0.47, p < 0.01). The results highlight that the future of work will be driven not by full automation, but by augmentation—where technology amplifies human strengths and reduces operational burdens. The study proposes a conceptual model for achieving effective Human–AI collaboration and offers practical recommendations for building organizational readiness through trust, transparency, ethical design, and employee training. These insights open pathways for further research and strategic implementation of collaborative intelligence in rapidly changing digital environments.

Integrating generative artificial intelligence (GenAI) in higher education (HE) requires educators to develop new competencies. However, while GenAI holds transformative potential for education, research on the competencies needed for its responsible and effective use remains limited. This study employs a mixed framework analysis method, combining quantitative and qualitative analysis to identify key competencies essential for HE teachers. The research began with a bibliometric analysis of 1,737 documents from Scopus and proceeded with an in-depth analysis of 14 peer-reviewed articles. Using a chain-of-thought (CoT) prompting approach, the analysis integrates a human-GenAI collaboration to identify patterns in existing competency frameworks and empirical publications, aiming to classify and define competencies. The findings reveal that while AI literacy and ethical awareness are frequently mentioned, there is no unified competency framework addressing the pedagogical and technical dimensions of GenAI integration. The FAM process resulted in the identification of three key domains of competencies and a set of 16 competencies. The results highlight the need for a structured, yet flexible competency model tailored to educators. Future research should focus on empirical validation and the development of professional development programs to bridge the identified gaps.

Application of artificial intelligence (AI) in education contributes significantly to improving the quality of human resources (HR). This study aims to analyze the relationship between AI technology and HR quality from an economic perspective. Using a qualitative approach based on literature studies, this study explores the contribution of AI in supporting interactive learning, operational efficiency, and improving critical skills. The results show that AI accelerates the learning process, personalizes learning experiences, and prepares students to face the challenges of the digital job market. At the company level, AI implementation improves productivity, efficiency, and data-driven decision-making. This study recommends the development of a curriculum that supports technological literacy and collaboration between educational institutions, the government, and the private sector to ensure HR readiness to face the demands of the digital era. Practical implications include sustainable training strategies and AI integration to support adaptive digital transformation. This study emphasizes the importance of investing in education and training to optimize technological potential, improve business performance, and support sustainable economic growth

To address the misalignment between generative AI’s restructuring of the interior design industry and its insufficient integration into pedagogical practices—and in response to the digital transformation demands of New Engineering and vocational education—this study examines four key issues in current teaching methods: the fragmented use of AI tools, disconnections in human–AI collaboration, an imbalance among technical, creative, and ethical competencies, and outdated evaluation mechanisms. Drawing on research in interdisciplinary collaboration and AI tool application, we propose strategies such as an integrated AI toolchain for the entire design process, interdisciplinary fusion, phased human–AI collaboration, three-dimensional capability development, and diversified quantitative assessment. These strategies are validated through case studies. The study also suggests future optimization pathways across four dimensions, including technological integration. By constructing an adaptive framework and providing actionable teaching solutions, this research aims to support the cultivation of interdisciplinary talents skilled in “AI + Interior Design.”

The rapid advancement of artificial intelligence has fundamentally transformed how knowledge is created, disseminated, and applied in problem-solving, presenting new challenges for educational models. This study introduces Intelligent Problem-Solving Learning (IPSL)—a capability-based instructional design framework aimed at cultivating learners’ adaptability, creativity, and meta-learning in AI-enhanced environments. Grounded in connectivism, extended mind theory, and the concept of augmented intelligence, IPSL places human–AI collaboration at the core of instructional design. Using a design and development research (DDR) methodology, the study constructs a conceptual model comprising three main categories and eight subcategories, supported by eighteen instructional design principles. The model’s clarity, theoretical coherence, and educational relevance were validated through two rounds of expert review using the Content Validity Index (CVI) and Inter-Rater Agreement (IRA). IPSL emphasizes differentiated task roles—those exclusive to humans, suitable for human–AI collaboration, or fully delegable to AI—alongside meta-learning strategies that empower learners to navigate complex and unpredictable problems. This framework offers both theoretical and practical guidance for building future-oriented education systems, positioning AI as a learning partner while upholding essential human qualities such as ethical judgment, creativity, and agency. It equips educators with actionable principles to harmonize technological integration with human-centered learning in an age of rapid transformation.

Abstract This article examines the impact of generative artificial intelligence (GAI) on higher education, emphasizing its effects in the broader educational contexts. As AI continues to reshape the landscape of teaching and learning, it is imperative for higher education institutions to adapt rapidly to equip graduates for the challenges of a progressively automated global workforce. However, a critical question emerges: will GAI lead to a more inclusive future of learning, or will it deepen existing divides and create a future where educational access and success are increasingly unequal? This study employs both theoretical and empirical approaches to explore the transformative potential of GAI. Drawing upon the literature on AI and education, we establish a framework that categorizes the essential knowledge and skills needed by graduates in the GAI era. This framework includes four key capability sets: AI ethics, AI literacy (focusing on human-replacement technologies), human–AI collaboration (emphasizing human augmentation), and human-distinctive capacities (highlighting unique human intelligence). Our empirical analysis involves scrutinizing GAI policy documents and the core curricula mandated for all graduates across leading Asian universities. Contrary to expectations of a uniform AI-driven educational transformation, our findings expose significant disparities in AI readiness and implementation among these institutions. These disparities, shaped by national and institutional specifics, are likely to exacerbate existing inequalities in educational outcomes, leading to divergent futures for individuals and universities alike in the age of GAI. Thus, this article not only maps the current landscape but also forecasts the widening educational gaps that GAI might engender.

Purpose: To suggest how business schools can respond when generative AI automates routine, entry-level tasks and erodes early-career opportunities. The paper addresses a focused question: What can a business school do when graduates’ entry-level jobs are replaced or reconfigured by AI?Approach: This is a perspective article that synthesises recent empirical studies, labour-market evidence, and international policy guidance. Drawing on this integrative review, the paper develops a practical institutional blueprint for programme design, governance, and university-industry collaboration.Findings: The existing literature indicates that traditional “first-rung” roles are thinning in AI-exposed occupations while expectations for day-one fluency with AI-augmented workflows rise. To bridge this capability gap, the paper proposes a coordinated blueprint: (1) reframe curricula around human-AI complementarity; (2) redesign assessment to evaluate judgment, verification, and communication; (3) build experiential pipelines that replicate the developmental function of first jobs; (4) co-design early-career roles through university-industry collaboration; (5) invest in student well-being and ethical governance; (6) sustain staff development; and (7) address common concerns (academic integrity, equity of access). Collectively, these actions enable business schools to restore apprenticeship-style learning within and immediately after degree programmes.Originality: The paper links near-term labour-market disruption from generative AI to concrete, institution-level strategies in business education. It offers an actionable, literature-informed blueprint that moves schools beyond placement facilitation to co-creation of AI-era entry pathways, showing how higher education can rebuild the apprenticeship-like learning once provided by traditional entry-level jobs.

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ABSTRACT Artificial Intelligence (AI) has increasingly influenced higher education, notably in academic writing where AI-powered assisting tools offer both opportunities and challenges. Recently, the rapid growth of generative AI (GAI) has brought its impacts into sharper focus, yet the dynamics of its utilisation in academic writing remain largely unexplored. This paper focuses on examining the nature of human-AI interactions in academic writing, specifically investigating the strategies doctoral students employ when collaborating with a GAI-powered assisting tool. This study involves 626 recorded activities on how ten doctoral students interact with GAI-powered assisting tool during academic writing. AI-driven learning analytics approach was adopted for three layered analyses: (1) data pre-processing and analysis with quantitative content analysis, (2) sequence analysis with Hidden Markov Model (HMM) and hierarchical sequence clustering, and (3) pattern analysis with process mining. Findings indicate that doctoral students engaging in iterative, highly interactive processes with the GAI-powered assisting tool generally achieve better performance in the writing task. In contrast, those who use GAI merely as a supplementary information source, maintaining a linear writing approach, tend to get lower writing performance. This study points to the need for further investigations into human-AI collaboration in learning in higher education, with implications for tailored educational strategies and solutions.

[This paper is part of the Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research.] Recent research in science education has largely focused on using ChatGPT to solve problems and evaluating its accuracy and problem-solving features. However, as artificial intelligence (AI) is becoming an important tool for human development, effective strategies for learning and education using human-AI collaboration (HAI) learning remain underexplored. HAI is emerging as a powerful tool for enhancing educational outcomes, especially with the integration of advanced natural language AI systems such as ChatGPT-4o. This study explores the role of HAI in solving scientific problems, comparing its efficacy with human-human collaboration (HHC) among high school students. We employed two different groups: one using HAI with ChatGPT-4o, and the other using HHC, focusing on their performance in scientific knowledge and scientific thinking tasks. Results showed that while both collaboration methods significantly improved students’ performance in solving scientific problems, HHC exhibited a greater effect size compared to HAI. The use of ChatGPT-4o was particularly noted for its ability to provide interactive learning experiences and guide students through problem solving; however, challenges such as inconsistencies in responses and image recognition hindered its effectiveness. Additionally, students tended to use ChatGPT-4o as a tool for obtaining answers rather than engaging in deeper collaborative explorations. This study underscores the importance of developing targeted training for students in effectively collaborating with AI tools. The results can also provide useful information for future exploration on methods to strengthen HHC by integrating AI tools, leveraging the strengths of both approaches to enhance education outcomes. Published by the American Physical Society 2025

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As human-AI collaboration becomes increasingly prevalent in educational contexts, understanding and measuring the extent and nature of such interactions pose significant challenges. This research investigates the use of authorship verification (AV) techniques not as a punitive measure, but as a means to quantify AI assistance in academic writing, with a focus on promoting transparency, interpretability, and student development. Building on prior work, we structured our investigation into three stages: dataset selection and expansion, AV method development, and systematic evaluation. Using three datasets - including a public dataset (PAN-14) and two from University of Melbourne students from various courses - we expanded the data to include LLM-generated texts, totalling 1,889 documents and 540 authorship problems from 506 students. We developed an adapted Feature Vector Difference AV methodology to construct robust academic writing profiles for students, designed to capture meaningful, individual characteristics of their writing. The method's effectiveness was evaluated across multiple scenarios, including distinguishing between student-authored and LLM-generated texts and testing resilience against LLMs'attempts to mimic student writing styles. Results demonstrate the enhanced AV classifier's ability to identify stylometric discrepancies and measure human-AI collaboration at word and sentence levels while providing educators with a transparent tool to support academic integrity investigations. This work advances AV technology, offering actionable insights into the dynamics of academic writing in an AI-driven era.

This research investigates the characteristics of student essays written with and without generative AI assistance, using stylometric analysis and deep learning techniques to explore human-AI collaboration in academic writing. To address three research questions, the study examines: (1) patterns in vocabulary diversity, sentence structure, and readability in AI-generated versus student-written essays; (2) the development of a stylometry-based BERT model for authorship attribution, focusing on linguistic features to accurately distinguish between student and AI-generated content; and (3) the application of this model to measure AI involvement at the sentence level in collaborative essays. Using a dataset of student and AI-assisted essays, we observed distinct stylistic differences, with AI-generated content exhibiting higher lexical diversity and readability scores. The BERT model demonstrated high accuracy (85%), precision (79%), and F1-scores (74%) in identifying AI contributions, surpassing the adopted baseline. While limitations such as dataset imbalance and variability in AI outputs remain, this study highlights the potential of stylometric analysis in improving authorship attribution and quantifying AI involvement in academic writing. These findings provide educators with tools to monitor student progress, offer personalised feedback, and maintain academic integrity in the face of growing AI usage in education.

This paper proposes a new model for integrating human-AI collaboration in translation teaching. With the rapid advancements in artificial intelligence, particularly Generative Artificial Intelligence (GenAI) and Neural Machine Translation (NMT), this model seeks to combine the strengths of AI in automation and scalability with the unique capabilities of human instructors in critical thinking, creativity, and cultural sensitivity. By structuring a multi-stage pedagogical approach, where AI tools assist in repetitive tasks and human instructors focus on nuanced cultural and ethical considerations, this model aims to enhance both the efficiency and quality of translation education.

With generative artificial intelligence driving the growth of dialogic data in education, automated coding is a promising direction for learning analytics to improve efficiency. This surge highlights the need to understand the nuances of student-AI interactions, especially those rare yet crucial. However, automated coding may struggle to capture these rare codes due to imbalanced data, while human coding remains time-consuming and labour-intensive. The current study examined the potential of large language models (LLMs) to approximate or replace humans in deductive, theory-driven coding, while also exploring how human-AI collaboration might support such coding tasks at scale. We compared the coding performance of small transformer classifiers (e.g., BERT) and LLMs in two datasets, with particular attention to imbalanced head-tail distributions in dialogue codes. Our results showed that LLMs did not outperform BERT-based models and exhibited systematic errors and biases in deductive coding tasks. We designed and evaluated a human-AI collaborative workflow that improved coding efficiency while maintaining coding reliability. Our findings reveal both the limitations of LLMs -- especially their difficulties with semantic similarity and theoretical interpretations and the indispensable role of human judgment -- while demonstrating the practical promise of human-AI collaborative workflows for coding.

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This paper explores ways to use AI for active learning strategies so that students in higher education may perceive generative artificial intelligence (generative AI) as a collaborative partner in their learning experience. This study proposes AI can help advance educational sustainability when students read texts on critical posthumanism, reflect on the philosophical and ontological paradigms through which the human has been understood, and discuss the collaborative relationship between humans and AI using literary texts. By analyzing AI-collaborated writing assignments, student questionnaires, and peer evaluations, this study concludes there are three learning types based on the different levels of students’ perceived difficulties: a cognitive learner, who focuses on AI’s functional aspects such as information retrieval; a metacognitive learner, who engages with generative AI in a two-way communication; and an affective learner, who strictly differentiates the human from the nonhuman and claims reciprocity in human–AI communication to be impossible. This study utilizes a mixed-methods approach by integrating quantitative analysis of the student questionnaires and qualitative analysis of the writing assignments. The findings of the study will serve as a valuable resource for researchers and educators committed to fostering future-oriented citizenship through collaboration between humans and generative AI in higher education.

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The integration of Generative AI (GenAI) into education has raised concerns about over-reliance and superficial learning, particularly in writing tasks in higher education. This study explores whether a theory-driven learning analytics dashboard (LAD) can enhance human-AI collaboration in the academic writing task by improving writing knowledge gains, fostering self-regulated learning (SRL) skills and building different human-AI dialogue characteristics. Grounded in Zimmerman's SRL framework, the LAD provided real-time feedback on learners'goal-setting, writing processes and reflection, while monitoring the quality of learner-AI interactions. A quasi-experiment was conducted involving 52 postgraduate students divided into an experimental group (EG) using the LAD to a control group (CG) without it in a human-AI collaborative writing task. Pre- and post- knowledge tests, questionnaires measuring SRL and cognitive load, and students'dialogue data with GenAI were collected and analyzed. Results showed that the EG achieved significantly higher writing knowledge gains and improved SRL skills, particularly in self-efficacy and cognitive strategies. However, the EG also reported increased test anxiety and cognitive load, possibly due to heightened metacognitive awareness. Epistemic Network Analysis revealed that the EG engaged in more reflective, evaluative interactions with GenAI, while the CG focused on more transactional and information-seeking exchanges. These findings contribute to the growing body of literature on the educational use of GenAI and highlight the importance of designing interventions that complement GenAI tools, ensuring that technology enhances rather than undermines the learning process.

Artificial Intelligence (AI) integration in schooling is absolutely reinventing vintage pedagogical strategies, which in turn, consequences withinside the roles of the trainer and the pupil being redefined, in addition to the remaking of the getting to know experience (Luckin, 2021; Selwyn, 2020). The look at being cited is one which examines human-AI collaboration in schooling, which research how AI-generated equipment upload price to the pupil studying process, in addition to assist withinside the duties administration, and of the aid of statistics-pushed decision-making whilst nevertheless maintaining people as vital to training. For this reason, mixed-strategies layout, which incorporates quantitative surveys applied with college students at universities and instructors in mixture with qualitative interviews carried out with EdTech specialists to acquire records approximately attitudes, benefits, and demanding situations of AI creation in classrooms, turned into applied. The studies targeted most effective on better training establishments making use of AI-pushed studying equipment. The stratified random sampling method helped with the illustration of the establishments from diverse regions. The verified questionnaire aided in probing into the respondents' reviews on AI-assisted schooling, on the alternative hand, thematic evaluation for revealing the modern day instructional technological tendencies associated with trainer-AI collaboration. The implications are empirical insights into the evolving trainer-learner dynamic in AI-assisted schooling and thereby offer pointers for higher curricula. This observe is a part of the problems and answers associated with the harmonization of the technological upswing with human-targeted studying as it makes positive that AI may be simply an assistant and now no longer the stop of the academic process.

This study explores the impact of human-AI collaborative teaching strategies on English teachers in secondary schools. Based on semi-structured interviews with five English teachers in Jiangxi Province, thematic analysis was conducted using the SAMR, UTAUT, and GHEX-IPACK theoretical frameworks. The findings indicate that AI technology is primarily applied in scenarios such as resource generation, assignment distribution, and learning analytics. By substituting traditional tools, enhancing teaching interactions, and reconstructing instructional processes, AI facilitates a shift in teaching strategies from “teacher-led” to “human-AI collaboration”. Teachers generally recognized the potential of this model for improving efficiency and supporting personalized learning, but also pointed out challenges, including data bias, hardware limitations, and a lack of emotional interaction. The study suggests that achieving deep human-AI collaboration requires balancing technological efficacy with humanistic care relying on blended instructional design and teacher training to optimize teachers’ knowledge structures. This research preliminary constructs a practical model of human-AI collaboration in secondary school English education, providing insights for teacher professional development. 

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AI technologies are reshaping our world and prompting education scholars to rethink both the aims and methods of schooling to prepare learners for the future (Holmes et al., 2019). Meanwhile, interest in integrating AI into science education has grown, with much discussion focusing on the impact of AI on student engagement and learning performance. Among those interests and debates, questions arise about AI’s ability to provide instructional, learning, and evaluative tools, as well as the practices and challenges of teacher-AI collaboration in education. To conclude, human–AI collaboration in science education offers substantial potential to enrich teaching and learning, on the condition that AI functions as a collaborative partner guided by teacher expertise, ethical principles, and a commitment to equity. Realizing this potential requires deliberate, evidence-based design decisions, professional development that centers on teacher agency, and governance frameworks that foster trust and transparency in AI-assisted learning. By sustaining an ongoing partnership among teachers, researchers, and AI developers, we can foster collective intelligence in human-AI collaboration that illuminates scientific reasoning, personalizes instruction, and supports students in developing robust scientific understandings for the twenty-first century.

The continuously improving capabilities of Artificial Intelligence (AI) systems are rapidly establishing them as the de facto choice for complex tasks and sophisticated reasoning. Evaluation tasks, for example, are particularly challenging, since they demand expert judgment, contextual analysis, and nuanced decision-making. Educational assessment represents a critical instance of such a complex cognitive task, where scalability challenges force institutions to choose between efficiency and assessment quality, as enrollment outpaces faculty capacity. While large language models seem promising for assisting in this context, they often lack domain-specific knowledge and pedagogical context, which are essential for effective assessment. This paper presents a systematic methodology for human-AI collaboration that addresses these limitations and achieves scalable efficiency, while preserving instructor autonomy. We validate our approach in the Software Engineering education domain, an especially demanding testbed, which requires assessment across multiple technical artifacts that combine objective correctness with subjective design quality. The system is tested against 30 software engineering projects, across different software engineering artifact types. Our evaluation demonstrates significant efficiency improvement against the current (manual) assessment approach, indicating that the systematic provision of domain knowledge can enable AI assistance in complex educational evaluation tasks.

While research on human-AI collaboration exists, it mainly examined language learning and used traditional counting methods with little attention to evolution and dynamics of collaboration on cognitively demanding tasks. This study examines human-AI interactions while solving a complex problem. Student-AI interactions were qualitatively coded and analyzed with transition network analysis, sequence analysis and partial correlation networks as well as comparison of frequencies using chi-square and Person-residual shaded Mosaic plots to map interaction patterns, their evolution, and their relationship to problem complexity and student performance. Findings reveal a dominant Instructive pattern with interactions characterized by iterative ordering rather than collaborative negotiation. Oftentimes, students engaged in long threads that showed misalignment between their prompts and AI output that exemplified a lack of synergy that challenges the prevailing assumptions about LLMs as collaborative partners. We also found no significant correlations between assignment complexity, prompt length, and student grades suggesting a lack of cognitive depth, or effect of problem difficulty. Our study indicates that the current LLMs, optimized for instruction-following rather than cognitive partnership, compound their capability to act as cognitively stimulating or aligned collaborators. Implications for designing AI systems that prioritize cognitive alignment and collaboration are discussed.

: This study investigates the application of Generative AI (GenAI) in human-AI collaborative reading promotion within Chinese university libraries, addressing the paradigm shift from 'resource pushing' to 'demand response.' Utilizing a case study methodology, it analyzes practices from libraries including Harbin Institute of Physical Education, Shijiazhuang University, and Shenyang University of Technology to examine AI's role across the entire activity chain: planning, resource provision, service implementation, and effect evaluation. The research finds that mature collaboration models exhibit a three-dimensional structure of 'technology empowerment, librarian leadership, and reader co-creation,' where the organic integration of intelligent tools and professional expertise enhances service coverage and engagement. However, the study also identifies persistent challenges, including technical immaturity and data security risks. It concludes by proposing strategic countermeasures—such as optimized technology selection, specialized talent cultivation, and robust system construction—to provide a practical reference for advancing intelligent reading promotion in the academic library sector.

This paper presents a conceptual framework for AI literacy, a hypothesized learning progression, and assessment design principles for advancing AI literacy among K–12 learners. Recognizing the importance of technical competencies alongside ethical awareness, the framework integrates foundational knowledge, societal implications, and practical applications of AI. Key competencies include ethical decision-making, AI-powered collaboration, and critical evaluation of AI outputs. Developed through an evidence-centered design (ECD) process involving a review of existing literature and frameworks, the proposed AI literacy framework and progression maps a hypothesized trajectory of students’ skill development, providing a structured pathway for improvement with behavior indicators connected to core AI literacy subskills. In this way, the framework and progression may offer educators a roadmap to apply scaffolded and differentiated teaching strategies that actively foster learners’ skill acquisition. To further support connections between assessment and instruction, we introduce three design principles for task design: ensuring relevance to learners, minimizing barriers to resource access, and providing opportunities for skill advancement. These design principles may guide the creation of activities that evaluate and enhance students’ AI literacy. By aligning scaffolded assessments and learning activities with the progression, this framework bridges instruction, assessment, and students’ skill development. It ultimately may be used to support students in developing skills to critically and ethically engage with AI technologies, preparing them to navigate the digital landscape by fostering inclusive instruction that deepens students’ understanding of AI concepts. Chakraburty, S., Ober, T. M., & Liu, L. (2025). Preparing K–12 students with AI literacy: Proposed framework, progression, and task design principles (Research Report No. RR-25-14). ETS. https://doi.org/10.64634/46jn1p41  

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Abstract: This study aims to examine the relationship between artificial intelligence (AI) literacy levels and 21st-century skill self-efficacy perceptions among university students enrolled in faculties of sports sciences. Designed within the framework of a correlational survey model, the sample consists of 410 students from various departments in Faculties of Sports Sciences across Turkey. The data were collected using the "Artificial Intelligence Literacy Scale" and the "21st Century Skills Self-Efficacy Perception Scale." Data were analyzed using SPSS 26.0, employing independent samples t-test, one-way ANOVA, Tukey HSD, and Spearman correlation tests. The findings revealed a positive and statistically significant relationship between AI literacy and 21st-century skills. Additionally, significant differences were identified based on demographic variables such as age, class level, and department. These results underscore the necessity of integrating digital literacy and 21st-century competencies holistically in contemporary teacher education. Systematically developing these competencies within teacher training and sports sciences programs should be regarded as a strategic imperative to adequately prepare future educators for the digital era.

This paper presents a justification for the implementation of AI Literacy courses in higher education. Ethical concerns and biases surrounding AI technologies are explored, highlighting the importance of critical analysis and responsible use of AI. A conceptual framework is then proposed, focusing on awareness, skill development, and practical application of AI. This framework aims to foster comprehensive understanding and empower students to leverage AI's potential while mitigating risks. The paper provides sample course titles and learning objectives. The suggested course format covers AI concepts, ethical considerations, bias awareness, and practical prompt engineering skills. There is a need for integrating AI literacy courses in higher education curriculum and this paper offers a roadmap for implementation. By equipping students with AI literacy, colleges can prepare students to responsibly navigate an AI-driven world while nurturing innovation and critical thinking skills that will be needed for future success.

Artificial Intelligence (AI) presents transformative potential for enhancing efficiency and effectiveness in educational administration. However, successful integration necessitates a strategic approach to managing the human element, including evolving roles, requisite skills, and organizational change. This study addressed the gap in targeted frameworks for optimizing human-AI collaboration within the specific context of educational administration in Muara Bungo, Jambi, Indonesia. A mixed-methods, quasi-experimental pre-post design was employed. Data were collected from 50 educational administrative staff in Muara Bungo using surveys assessing AI perceptions, skill readiness, and job satisfaction, alongside semi-structured interviews exploring experiences with role changes and change management. A bespoke Human Resource Development (HRD) framework, encompassing structured role redefinition workshops, targeted AI literacy and collaboration skill training modules, and a multi-faceted change management communication plan, was developed and implemented over six months. Quantitative data were analyzed using descriptive statistics and paired t-tests, while qualitative data underwent thematic analysis. Post-intervention, quantitative analysis revealed statistically significant improvements (p < 0.05) in participants' perceived usefulness of AI (t(49)=5.82), confidence in collaborating with AI tools (t(49)=6.15), and reported efficiency in administrative tasks (t(49)=4.98). Qualitative findings indicated that the HRD framework facilitated a clearer understanding of new roles, reduced initial anxiety towards AI, and highlighted the importance of ongoing support and transparent communication during the transition. In conclusion, the study demonstrated that a context-specific HRD framework integrating role redefinition, skill development, and change management can significantly enhance human-AI collaboration in educational administration. The findings underscore the necessity of proactive HRD interventions to equip staff, manage transitions effectively, and harness the synergistic potential of humans and AI in improving administrative functions within educational institutions in regions like Muara Bungo.

As artificial intelligence (AI) continues to influence all facets of society, fostering AI literacy has become essential—not only for students but also for educators across disciplines, including music education. In an exploratory review of thirty peer-reviewed articles, Davy Tsz Kit Ng and colleagues describe AI literacy as comprising four essential dimensions: understanding AI, using/applying AI, evaluating AI, and considering AI ethics. These core aspects closely correspond to Aristotle’s classical division of knowledge: episteme (theoretical understanding), techne (practical skill), and phronesis (ethical reasoning). Acknowledging the growing importance of AI literacy, I suggest that music teachers adopt Aristotle’s framework as a guide to develop AI literacy through self-directed learning. This framework can support music teachers in building a strong theoretical foundation, acquiring practical skills, and thoughtfully addressing the ethical considerations involved in responsibly integrating AI into their teaching practice.

This article explores the integration of a generative AI chatbot into undergraduate education to enhance information literacy. By leveraging the ACRL Framework for Information Literacy, specifically the 'Scholarship as Conversation', 'Research as Inquiry', and 'Searching as Strategic Exploration' frames, the authors argue that AI tools can improve students' research skills while emphasizing the importance of critical reading and resource verification. Using scaffolding with AI supports students transitioning from their current skill levels to achieving specific learning objectives. The paper reviews the impact of generative AI on higher education and proposes two adaptable assignments for classroom use. The article concludes that effective use of AI in educational settings can enhance learning outcomes and prepare students for the complexities of modern research practices while promoting ethical information usage.

Against the backdrop of the profound transformations in disciplinary development logic and talent cultivation paradigms driven by the construction of New Liberal Arts, university liberal arts teachers have been reshaped into compound talent cultivators, learning process enablers, and social value constructors. Their AI literacy directly determines the depth and effectiveness of New Liberal Arts construction. To address this, based on a systematic review of existing research, this paper clearly defines the core connotation of AI literacy for university liberal arts teachers, systematically analyzes its constituent elements, and constructs a two-level indicator system framework. Meanwhile, considering the practical challenges faced by these teachers in improving AI literacy—such as insufficient knowledge and skill reserves, difficulties in integrating teaching content with AI technologies, and inadequate support from practical guarantee conditions—this paper further proposes a trinity improvement pathway of "philosophy guidance- competence cultivation - ecological support." The research conclusions provide theoretical guidance and practical references for the systematic improvement of teachers' AI literacy and the realization of the New Liberal Arts construction goal of "technology empowering liberal arts innovation and liberal arts guiding technology for good."

The rapid proliferation of generative Artificial Intelligence (AI) necessitates a fundamental shift in educational paradigms, positioning prompt engineering (PE) as a crucial 21st-century literacy for K-12 students. This paper argues that PE transcends mere technical skill, embodying a complex interplay of critical thinking, iterative refinement, creative problem-solving, and ethical awareness. It proposes a comprehensive K-12 curriculum framework for PE, grounded in Backward Design principles and leveraging Project-Based Learning (PBL) and Computational Thinking (CT) for authentic skill development across elementary, middle, and high school levels. Furthermore, a multi-dimensional assessment framework is presented, incorporating formative and summative strategies, along with detailed rubrics, to evaluate the multifaceted nature of PE literacy. The paper also addresses the significant ethical considerations inherent in PE education, including algorithmic bias, data privacy, and student agency, and outlines essential competencies and professional development models for empowering educators. By fostering PE literacy, K-12 education can equip students to critically and creatively engage with AI, preparing them for a future where human-AI collaboration is ubiquitous.

With the rapid advancement of AI technology in the field of basic education, the concept, form, method, mode, and environment of education and teaching are undergoing significant changes. As a result, information technology teachers are confronted with the challenge of restructuring their subject knowledge and developing intelligent literacy. In the context of applying AI to basic education, intelligent literacy has become an essential professional skill for IT teachers. This paper proposes a framework for cultivating intelligent literacy among information technology teachers based on Artificial Intelligence Technological Pedagogical Content Knowledge (AI-TPACK) theory. The study aims to elucidate the meaning of AI-TPACK for information technology teachers, establish AI-TPACK models and practice paths for them, propose strategies for integrating subject teaching knowledge with artificial intelligence technology, and enhance cultivation and development plans for their intelligent literacy. The goal is to enhance the teaching abilities of information technology teachers so that they can acquire comprehensive artificial intelligence literacy in their work and better adapt to the new requirements and challenges presented by the era of AI.

Artificial intelligence (AI) is reshaping health care, making AI literacy vital for nursing professionals. The Navigate AI basics, Utilize AI strategically, Recognize AI pitfalls, Skills support, Ethics in action, and Shape the future framework provides a structured approach to AI integration into nursing. Nurses need to understand AI's fundamentals and its impact on clinical practice and patient care, both in the classroom and at the bedside. Nurses can use AI effectively and responsibly by recognizing benefits, such as enhanced decision-making, and challenges, like biased data. Ethical considerations should guide AI usage in health care, with a commitment to frequent skill development. Nurses play a pivotal role in shaping the future by ensuring AI is applied to benefit their organizations and, more importantly, healthcare workers and patients. This AI literacy guide is designed to empower nurses to navigate and help build the future of health care and AI with confidence and competence.

Prompting is becoming a foundational skill in the age of generative AI, yet little is known about what makes someone a good prompter. This study introduces a multi‐dimensional framework of prompt literacy and investigates it through an eye‐tracking experiment. Sixty participants were invited to create short stories using the Doubao platform, while their gaze behavior was recorded. By integrating cognitive data, experiential reports, and sociocultural backgrounds, we analyze how prompt construction patterns relate to satisfaction, perceived difficulty, and creative output. Our findings provide insight into the competencies that define good prompting and offer practical implications for prompt training and interface design.

This paper presents and discusses the results of a national survey on adults’ media and robot literacy in Finland with a focus on robot literacy. The study addresses three pressing global challenges: population aging, increasing robotization, and a research gap concerning the skills and competencies needed for interacting with robots. The survey operationalized the authors’ robot literacy framework, which focuses on physical robots and defines robot literacy across seven skill dimensions: 1. Awareness of robots; 2. Interaction with robots; 3. Understanding and evaluating the information robots provide; 4. Understanding the data security and privacy of robots; 5. Programming of robots; 6. Ethical reflection; and 7. Providing and receiving social support on robotics-related questions. The survey addressed the following research questions: What is the scope of adults’ robot literacy? How are gender, age, education, household composition, and use of the internet connected to adults’ robot literacy? The study marks the first attempt to map robot literacy across a national population. It shows that despite recent progress in AI and future forecasts of advances in the development of social and humanoid robots, awareness of robots is still limited among Finland’s adult population and does not originate mainly in firsthand experiences with robots. Furthermore, the respondents exhibited uncertainty in their ethical reflection, in knowledge about interaction with robots, and in their understanding and evaluation of the information provided by robots. They also reported being entirely unprepared—or possessing low or very low skills—in providing social support related to robotics. For the field of computing education, the study offers new insights into the relatively limited robot literacy of adults, particularly older adults. A key practical implication is that adult educators—computing educators included—as well as researchers, instructional designers, the media, robotic service providers, robot developers, and other stakeholders must actively promote robot literacy among the adult population.

India’s vision for Viksit Bharat@2047 outlines an ambitious goal of becoming a fully developed nation by its centenary of independence. Central to achieving this vision is robust education and skill development sectors, which play pivotal roles in fostering economic growth, social equity, and technological advancement. This research delves into the sub-theme of Strengthening Education and Skill Development within the broader Viksit Bharat@2047 framework, highlighting the challenges in India’s education system, including outdated curricula, disparities in access, and a disconnect between industry needs and academic training.The article examines government initiatives such as the Skill India Mission and programs while comparing India’s progress with global standards in education and skill development. It identifies key areas for reform, such as enhancing digital literacy, improving teacher quality, and fostering industry-academia linkages. Technological innovations, including artificial intelligence (AI), virtual reality (VR), and blockchain, are explored as transformative tools for modernizing education and skill development.The paper concludes by proposing strategic policy interventions and public-private partnerships to bridge existing gaps and align India’s education system with global demands. Implementing these changes is vital for building an education system that provides importance and opportunity for all, ultimately driving greater social mobility and reducing inequalities, ensuring that India’s workforce is equipped to meet future challenges and contribute to the nation’s growth by 2047.

Artificial Intelligence (AI) is revolutionizing education and career development, influencing students' learning experiences and professional trajectories. This study examines the impact of AI on students’ sustainable education and career growth using the Extended Technology-Organization-Environment (TOE) framework. The framework integrates technological, organizational, and environmental factors with additional dimensions such as individual and societal influences to provide a comprehensive analysis. The research highlights AI-driven tools, including intelligent tutoring systems, personalized learning platforms, and career guidance applications, which enhance student engagement, learning efficiency, and decision-making. However, challenges such as ethical concerns, digital divide, and the need for AI literacy pose barriers to sustainable implementation. Findings suggest that integrating AI in education fosters adaptive learning, skill development, and career readiness, promoting long-term sustainability. The study underscores the importance of strategic policies, institutional support, and AI governance to maximize benefits while mitigating risks. The Extended TOE framework serves as a valuable lens to understand AI adoption in educational ecosystems and its broader implications for students' future careers.

In an era defined by information overload, Generative AI-driven content, and sophisticated misinformation, the ability to think critically has shifted from a desirable professional attribute to an essential one. Conventional educational frameworks often reduce information literacy to a procedural skill set for finding and citing sources, inadequately preparing students for the intricate nature of the modern digital landscape. In an attempt to bridge the educational gap, this paper designs and offers support for a new conceptual framework, which we term as Synergistic Framework for Critical Information Engagement (SFCIE). The specific research objective of this study is to develop an integrated and comprehensive framework that synergizes information literacy (IL) capabilities at an advanced level with modern learning technologies in higher education. This study targets higher education curricula and programs which prepare future professionals in a spectrum of fields, including but not limited to management, law, and health science, among many others. This paper follows the research design of a review of the literature and a conceptual development. This research thus reviews the development of IL and its pedagogical tools, the underlying cognitive abilities for critical thinking (CT), and the functional affordances of learning technologies, such as Artificial Intelligence tutors and collaborative digital workspaces to support these dimensions, especially in the case of the recent rise of Generative AI and its paradoxical effect for both supporting and undermining CT. The main finding of this article is an integrative framework of building and supporting CT at the intersection of core literacies, higher-order cognitive skills such as algorithmic thinking, source analysis, and ethical integration of information, and pedagogical methods enabled by learning technologies. SFCIE thus provides teachers and instructors with a principled approach for designing learning experiences that enable and support the development of robust CT. We concluded that higher education programs must progress beyond lower-order learning skills and take an integrative approach, putting pedagogy at the center, to better prepare graduates as professionals who are insightful, analytical, and ethically aware. The article also provides a blueprint for educators and higher education leaders to support CT in a world of increasing integration of AI and AI-powered systems.

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With the rise of social media and the sharing of information, as well as the use of AI tools like ChatGPT in education, the ability to evaluate information credibility has become a crucial skill. The CREDIBLE framework, standing for Credibility, Reliability, Evidence, Date, Intent, Bias, Logic, and Expertise, offers a practical, student-friendly approach to source evaluation, especially suited for secondary and postsecondary learners. Unlike models and frameworks designed for higher education, CREDIBLE helps learners critically assess both online and AI-generated content. This paper introduces the framework and explores how educators can embed it into instruction to foster critical thinking, academic integrity, and responsible digital literacy.

Data Literacy is the essential competency in this digital era. However, there is a lot of college students have difficulty in comprehending and delivering the information data-driven information effectively. This study aims to explore the potential of Artificial Intelligence (AI) in enhancing data literacy through implementation of International Toastmaster project which has been proven effective in developing speaking and leadership skill. Mix method is conducted in this study which involve 30 english literature students of State University of Medan who have follow International Toastamaster program. This study evaluates the influence of Artificial Intelligence (AI) which is intergrated in 10 International Toastmaster project for developing data literacy within the framework of a structured Toastmasters program. The data was collected through questionaires, interviews and survey for evaluating data literacy level of the students. The finding of this study shows that the integration of Artificial Intelligence (AI) in 10 International Toastmaster project can significantly improve students’ ability in analyzing, interpreting and presenting data findings. The finding indicates that the integration can increase essential speaking skills to face challenges in an increasingly data-dependent era.

The rapid evolution of digital technologies has reshaped the educational landscape across the globe, necessitating a paradigm shift from traditional teaching methods toward future-ready technology-enhanced learning environments. This paper explores how emerging technologies like AI, ML, VR/AR, data analytics, cloud computing, and blockchain are transforming pedagogies, curriculum design, and institutional frameworks in modern education. These integrations have fostered more personalized, adaptive, and inclusive learning experiences, thereby aligning education with Industry 5.0 requirements and lifelong skill development. The synthesis of recent research will analyze the pedagogical impact of technology-driven education, focusing on how innovation fosters active student engagement, critical thinking, and collaborative problem-solving. The paper also looks at infrastructural limitations, ethical issues, and gaps in digital literacy associated with digital transformation and puts forward a strategic framework for sustainable adoption. This work emphasizes the fact that future-ready education is not about deploying technologies but reimagining pedagogy, policy, and practice to nurture human potential in the digital era.

This study addresses critical limitations in traditional vocational education assessment systems by integrating value-added assessment theory with artificial intelligence (AI) to develop a Two-Orientation Four-Dimensional (TOFD) evaluation model. Targeting environmental monitoring courses in higher vocational education, the proposed system overcomes fragmented evaluation dimensions, static monitoring, and delayed feedback inherent in conventional methods. The TOFD framework employs AI-driven analytics to track longitudinal student growth across four dimensions: knowledge acquisition, technical skills, professional literacy, and career development. Leveraging multi-source data from academic platforms, simulations, and industry partnerships, the model enables real-time competency profiling and dynamic feedback. A study with 97 students showed the value-added group outperformed the traditional-evaluation group, with 12.59% rise in vocational skill certification rates; 11.14% higher competition awards; and 10.92% improved employer satisfaction. The process-oriented metrics demonstrated a 29.18% relative value-added rate in final project scores compared to initial benchmarks for individual cases, while the class-wide average reached 26.27%.Results validate the system's efficacy in bridging skill gaps, enhancing self-efficacy, and aligning vocational training with industry needs. The study establishes a replicable AI-powered assessment paradigm that shifts vocational education evaluation from terminal certification to competency development, offering actionable insights for curriculum reform and digital transformation in technical education.

This study argues that in the era of artificial intelligence (AI), liberal arts education must shift from the transmission of knowledge to wisdom-centered learning. As AI automates cognitive and analytical tasks, human capacities for interpretation, ethical discernment, and reflective reasoning become increasingly vital. Yet the liberal arts remain structurally marginalized within universities. Grounded in the humanistic tradition, this study reconceptualizes the liberal arts as essential knowledge directed toward self-understanding, ethical responsibility, and meaning-making rather than instrumental skill acquisition. An analysis of Korean university curricula and student surveys reveals a discrepancy between strong student interest in identity- and ethics-oriented courses and the continued decline of general education requirements and humanities autonomy. To respond, this study introduces the Humanitas-Techne-Data (HTD) model, a convergent framework integrating humanistic inquiry, technological literacy, and data-driven reasoning. The HTD model repositions the liberal arts as a vital intellectual ecosystem for cultivating wisdom and fostering ethically grounded engagement with human-technology relations in an AI-mediated world.

This study examines the perceptions of student teachers regarding artificial intelligence technologies particularly their knowledge, their willingness to use them, any concerns and perceived benefits and challenges in relation to the Digital Competence Framework for Citizens (DigComp) 2.2 digital competence framework. Our mixed-methods research, involving 372 undergraduate student teachers, revealed correlations among these aspects, indicating that frequent AI users have a stronger intention to use AI, while infrequent users express greater concerns about it. Student teachers acknowledge AI’s time-saving benefits as well as the convenience and academic enhancement it provides, but also voice concerns about its misuse and reliability and the potential impact on skill development and learning. These concerns are in agreement with digital competence areas of information literacy and safe technology use. Reflecting on these perceptions, it is essential to maximise the educational benefits of effective and responsible AI integration into higher education and foster the digital competencies of future teachers.

Summary The integration of generative AI (GenAI) into academic workflows represents a fundamental shift in scientific practice. While these tools can amplify productivity, they risk eroding the cognitive foundations of expertise by simulating the very tasks through which scientific competence is developed, from synthesis to experimental design to writing. Uncritical reliance can lead to skill atrophy and AI complacency. We propose a framework of essential AI meta-skills: strategic direction, critical discernment, and systematic calibration. These constitute a new form of scientific literacy that builds on traditional critical thinking. Through domain-specific examples and a pedagogical model based on situated learning, we show how these meta-skills can be cultivated to ensure that researchers, particularly trainees, maintain intellectual autonomy. Without deliberate cultivation of these meta-skills, we risk creating the first generation of researchers who serve their tools rather than direct them.

This study investigates integrating data-driven learning and Generative AI within the Meta-Cognitive Resource Utilization Framework (MCRUF) and its potential to enhance educational outcomes. It highlights how AI-driven tools can personalize learning experiences, foster meta-cognitive skill development, and offer real-time feedback to improve learner autonomy and engagement. However, the study identifies key challenges, such as over-reliance on technology, digital literacy gaps, data privacy concerns, and unequal access to AI resources. The findings suggest important implications for educators and policymakers, emphasizing the need for ethical guidelines, equitable access, and a balanced approach combining AI assistance with active learner participation. Future research should focus on long-term impacts and strategies to ensure responsible implementation of these technologies.

The rapid advancement of Artificial Intelligence (AI) is repositioning the skill demands of the global workforce in Nigeria. This shift necessitates a re-evaluation and restructuring of higher education curriculum. This paper examines the integration of AI competencies into Electrical/Electronic Technology Education programmes in universities in southwest Nigeria, with a view to enhancing global competitiveness. The paper identifies existing gaps in technology education curricula, limited infrastructure, inadequate training and many other factors that constitute the consequence of the lack of integration of AI into the curriculum of Technology Education in Nigerian universities, particularly, universities in southwest Nigeria. The paper also proposes strategic framework for curriculum reform, incorporating AI literacy, practical applications of AI in Electrical/Electronic context and industry collaboration. The paper affirms, that infusing AI competencies into Electrical/Electronic Technology Education curriculum is crucial for producing graduates, that will be capable of meeting evolving global technological standards, with a view to contributing to national development.

As AI becomes an integral part of education and the future job market, understanding how students perceive and use it is more important than ever. This study explores Russian university students' attitudes toward AI and examines the relationship between their attitudes and their actual skill to use AI effectively. To assess students' AI perceptions, researchers used a questionnaire covering four key factors: interest in AI, perceived AI experience, AI's value for the future, and AI-related risks. AI proficiency was measured through a practical test in which students had to create an effective prompt for a large language model. The results revealed that many students actually struggled with prompting. Those who had more perceived experience with AI performed slightly better (r = 0.20), as did students who showed a stronger interest in AI (r = 0.12). However, the overall connection between AI attitudes and actual proficiency was weak. Notably, students who viewed AI as a risk were less likely to see it as valuable for the future (r = −0.09), but this did not significantly impact their interest in AI or their perceived experience with it. Ultimately, while students express a high level of interest in AI, their ability to use it effectively remains limited.

Students at higher vocational colleges frequently struggle with their English language skills, making it difficult for them to interact with people from different cultures and meet the communication requirements of the job. The shortcomings found in the 2021 English Curriculum Standard for Higher Vocational Education in China are addressed by this systematic research, which investigates how AI-powered task design might improve language acquisition and interactional competency. This study investigated the AI technologies utilized, their advantages and disadvantages, and the research gaps by analyzing pertinent literature. By providing individualized, scalable, and interactive learning experiences that equip students for globalized job situations, the findings demonstrate AI’s potential to close the proficiency gap.

The transition from university to workplace demands not only technical knowledge but also advanced communication skills, particularly in job-seeking contexts. In Malaysia’s public and private sectors, proficiency in English, especially in resume writing, cover letter development, and job interviews, is an important determinant of graduate employability. However, existing English for Specific Purposes (ESP) courses such as LCC502 often lack the personalized and formative feedback necessary to prepare learners for these high-stakes real-world tasks. This paper presents JobLinguaAI, an AI-supported educational platform developed to address this gap by integrating AI-powered writing assistance, speech coaching, and interview simulations within a CEFR B2 High aligned framework. Grounded in a design-based research approach and informed by the course outcomes of LCC502, JobLinguaAI supports students in producing professional documents, improving spoken fluency, and acquiring domain specific vocabulary through iterative feedback loops. Preliminary feedback from educators affirms its relevance, clarity, and alignment with course learning outcomes while also suggesting enhancements such as localized vocabulary and lecturer dashboards. The platform’s modular design allows for scalability across ESP domains and integration with institutional LMS platforms. With its potential to transform job readiness pedagogy in Malaysian higher education, JobLinguaAI offers a novel model for AI enhanced, task-based language learning that promotes learner autonomy, performance accuracy, and employability.

Submission of well-structured job application letters is an essential aspect of business and government recruitment processes. However, due to difficulties with grammar and mechanics, many students, fresh graduates included, fail to produce high-quality job applications. While human reviewers typically give feedback to enhance these skills, Artificial Intelligence (AI) has brought about automated feedback systems. The research compared the performance of two artificial intelligence models, Gemini and ChatGPT, and human experts in assessing job application letters. The study compared feedback in six writing features—subject-verb agreement, parallelism, spelling, capitalization, punctuation, and sentence variety—of 10 job application letters composed by final-year high school students. Results indicated that ChatGPT and Gemini performed better than human experts in most metrics. These findings add to the discussion of using AI to improve application letter quality and offer implications regarding the potential of AI-assisted writing feedback. Integrating such tools into writing instruction can offer consistent, real-time feedback, thereby supporting skill development and improving students' employability prospects. The study contributes to the growing field of AI in education by highlighting the practical benefits and potential limitations of AI-generated feedback in high-stakes writing tasks such as job applicationsHow to cite this paper: Noerjaman, L. A., Dallyono, R., Hidayati, F. (2025). Enhancing job application writing: A comparative case study of AI and human feedback on grammar and mechanics. Journal of English Language Teaching Innovations and Materials (Jeltim), 7(1), 54-72. http://dx.doi.org/10.26418/jeltim.v7i1.89240

With the rapid advancement of artificial intelligence, the demand for AI talent is constantly evolving. Traditional expert-driven competency modeling approaches suffer from slow update cycles, hindering their ability to provide timely educational guidance. This study proposes an AI competency knowledge graph constructed using large language models (LLMs), enabling the transformation of unstructured recruitment texts into structured educational knowledge through an end-to-end automated framework. A total of 1,142 industry job postings were collected, and competency entities were extracted using few-shot prompt engineering. A two-stage strategy combining semantic embedding and LLM-assisted validation was employed for entity alignment and standardization. The method achieved a micro F1 score of 72.5% on a validation set of 120 samples, resulting in a knowledge graph containing 5,793 standardized competency entities. Application cases such as core skill identification and personalized career planning demonstrate the graph’s applicability in curriculum design, career guidance, and learning support. This research establishes a data-driven approach for translating dynamic labor market demands into structured educational knowledge, providing a digital foundation for AI education.

This study explores the perceptions of Universitas Pendidikan Indonesia (UPI) students regarding the role of Artificial Intelligence (AI) in the recruitment process. As AI technologies increasingly influence hiring decisions through tools such as resume screening algorithms, chatbots, and video assessments, understanding how students perceive and interact with these systems is vital. Using a qualitative approach, semi-structured interviews were conducted with ten final-year and postgraduate students from various faculties. Thematic analysis revealed five major themes: limited awareness of AI tools, perceived efficiency and objectivity, concerns about bias and data privacy, a preference for human judgment, and a strong call for institutional support. While students recognized AI's potential to improve hiring outcomes, many raised concerns about bias, accountability, and lack of knowledge. The findings underscore the importance of integrating AI literacy into higher education career services to equip students with a critical understanding of AI’s role in modern recruitment. This study contributes to the discourse on digital transformation in HR by amplifying the perspectives of future job seekers in an emerging market context.

This article examines graduate employability challenges in the tourism and hospitality sector of Marrakech, a major tourism destination and strategic regional labour market in Morocco, characterised by strong seasonality, high labour turnover, and persistent education–employment mismatches. Rather than focusing exclusively on technology, the study analyses employability as a multidimensional and context-dependent process, in which digitalisation and artificial intelligence (AI) constitute one influencing factor among others. The research adopts a qualitative, purposive design based on semi-structured interviews conducted between August and October 2025 with 20 stakeholders directly involved in recruitment, training, or early career integration. These include five-star hotel general managers and HR officers, riad managers, travel agencies, recruitment intermediaries, representatives of Morocco’s public employment service (ANAPEC—National Agency for the Promotion of Employment and Skills) and private, regional tourism authorities, academics and young tourism graduates. Interview transcripts were thematically analysed using NVivo to identify recurrent patterns in recruitment practices, skill expectations, and the impact of AI in employability. The results, reflecting stakeholders’ perceptions within this local labour market, show that employability is shaped by six interrelated dimensions: (1) the structure and functioning of the tourism labour market (segmentation, turnover, mobility); (2) partial misalignment between training provision and operational service realities; (3) recruitment standards that prioritise behavioural and relational competences alongside formal qualifications, particularly for frontline positions; (4) language proficiency, especially English and French, as a baseline employability condition; (5) growing expectations regarding digital literacy linked to tourism operations (property management systems, reservation platforms, online reputation management); and (6) the perceived impact of AI-enabled tools (automation of routine tasks, decision-support systems, chatbots), which is seen less as a source of job destruction than as a driver of task reconfiguration and skill upgrading. By situating employer and graduate perceptions within the broader Moroccan employment and training context, the study contributes a place-based understanding of employability in tourism. It highlights the shared responsibility of individuals, employers, and education and training institutions in supporting skill development. The article concludes by discussing policy and practice-oriented levers to strengthen graduate employability, including co-designed curricula, structured internships and mentoring schemes, employer-supported upskilling in tourism-specific digital and AI-related competences, and reinforced labour-market intermediation through ANAPEC and regional governance actors.

The rapid growth of Artificial Intelligence (AI) is fundamentally changing the demand for foreign language skills, displacing traditional professions while also creating new opportunities for individuals with diverse skill sets. Based on human capital theory and a constructivist learning approach, this empirical study analyzes 2,824 job advertisements (2022–2023) from a large recruitment platform in Guangdong, China, to investigate these changing demands and offer an innovative training program. Quantitative content analysis reveals a significant market movement away from pure linguistic competency and toward a model of compound knowledge. According to key findings, high-frequency demands now combine advanced language skills (English first and Japanese second) with technical competencies in programming (Python, Java), AI applications (machine learning, NLP), and industry-specific knowledge (e.g., mechanical engineering, international trade), as well as essential soft skills such as intercultural communication. Explicit mentions of AI-related talents confirm the technology’s penetration into the industry. A large gap exists between these diverse market needs and the output of traditional language-centric systems. In response, the study proposes a unique training structure based on the “Foreign Language + Professions + AI” curriculum. This strategy combines multidisciplinary knowledge, embedded AI literacy, and hands-on project-based learning to develop future-ready professionals capable of prospering in a technologically driven global economy.

More organizations are using automated job interview platforms to screen candidates at the early stages of the recruitment process. These platforms enable job candidates to take automated video interviews remotely using their personal devices. The interviews are analysed by artificial intelligence (AI) powered algorithms to produce analytics which inform hiring decisions. In this article, we explore the use of automated job interviewing within professional English subjects delivered to undergraduate students in Hong Kong. The innovation was introduced to help students to keep up with recruitment practices, provide speaking practice and explore AI-powered evaluation as a form of feedback. As well as outlining how automated interviewing was integrated into our teaching practice, we discuss student feedback on the experience and offer suggestions for future teaching practice with this technology. We also highlight considerations for practitioners such as the acceptance and adoption of AI technologies into language learning provision and the development of AI literacy skills. AI technologies have much potential for language teaching and this article offers a practical exploration into one such technology: automated job interviewing.

The growing demand for professionals in artificial intelligence (AI) and data analytics has highlighted the persistent challenge of education-to-employment mismatches, particularly in the Philippines, where many graduates lack structured guidance to align their competencies with industry needs. To address this, the Philippine Skills Framework for Analytics and Artificial Intelligence (PSF-AAI) was introduced, though its integration into higher education career services remains limited. This study presents a data-driven career guidance platform that combines a PSF-AAI–aligned skills competency assessment with academic performance to generate personalized career recommendations. The system employs three main algorithms: a skills competency assessment that evaluates student proficiency across 33 items mapped to PSF-AAI domains, a career pathway mapping algorithm that computes readiness against role-specific requirements, and a job-matching algorithm that integrates Grade Point Average (GPA) with competency scores using a weighted scoring model. Results from 165 graduating students revealed that most participants demonstrated intermediate proficiency (46.06%), while fewer achieved advanced (29.70%) and expert (4.85%) levels. The integration of GPA ensured a holistic evaluation, producing realistic and actionable career recommendations. The system frequently recommended entry-level roles, such as Associate Data Analyst, while advanced roles like Machine Learning Engineer and Data Engineer were matched to a smaller pool of qualified students. The findings demonstrate that the platform effectively provides evidence-based career guidance, reduces job-skills mismatch, and supports targeted upskilling. Although currently limited to multiple-choice assessments and AI-related career pathways, the system establishes a scalable foundation for competency-based guidance in higher education, with potential for expansion to broader domains and assessment formats.

In a dynamically evolving labor market, career guidance has become a key factor for the successful professional integration of young people. In Bulgaria, the high proportion of youth not in education, employment, or training (NEET), along with the mismatch between acquired competencies and labor market demands, highlights the need for innovative solutions. This article presents a concept and development stages for constructing an AI-powered advisory system that integrates algorithms such as Random Forest and neural networks to analyze skills, interests, and employer requirements. The system offers automated profile generation, personalized recommendations, and match evaluation between candidates and job positions, thereby enhancing the transparency and efficiency of the guidance and recruitment process. The results demonstrate the potential of artificial intelligence to reduce subjectivity, improve alignment between education and labor market needs, and promote sustainable development through more informed career decisions.

This study developed and evaluated a "Hiring Management System with AI Integration" for the Human Resource Office of State Universities and Colleges (SUCs). The system aims to streamline recruitment by using artificial intelligence to assess applicants based on their education, skills, and work experience, matching them to job requirements. Built using the Agile Software Development Model, the system was refined through iterative feedback from users, ensuring it met the needs of HR staff and applicants. Core features include AI-driven applicant ranking on a 1-100 scale based on uploaded qualifications, AI job matching self-assessment, AI chatbot for frequently asked questions (FAQs) and decision support to enhance hiring accuracy and efficiency. The system was assessed through surveys involving IT experts, HR office employees, and other Pampanga State Agricultural University (PSAU) staff. IT experts evaluated technical aspects such as functionality, reliability, and security, resulting in a strong mean score of 4.00. HR staff and PSAU employees rated the system highly in terms of usability, giving perfect scores of 4.00 for interface design and navigation, indicating ease of use and minimal training requirements. With an overall acceptability score of 4.00, the system proved effective and user-friendly. Results show it significantly improves hiring efficiency, reduces administrative workload, and supports better decision-making. The AI-integrated Hiring Management System, developed through Agile methodology, is ready for deployment and offers a modern solution to enhance recruitment processes in SUC HR offices.

As digital entrepreneurship accelerates, aspiring founders increasingly rely on artificial intelligence (AI) to drive innovation. This study examines how AI literacy—defined as the ability to identify, use, and evaluate AI tools—influences digital entrepreneurial intention. Based on Social Cognitive Career Theory (SCCT), we propose a moderated mediation model, where digital entrepreneurial self-efficacy mediates the relationship between AI literacy and entrepreneurial intention, and AI self-efficacy moderates this pathway. Survey data from 1,061 Vietnamese university students were analyzed using the PROCESS macro. Results reveal that AI literacy positively predicts entrepreneurial intention both directly and indirectly via increased self-efficacy. Furthermore, AI self-efficacy strengthens the link between AI literacy and entrepreneurial self-efficacy, amplifying its mediated impact on intention. These findings extend SCCT to the AI-enabled entrepreneurship domain, emphasizing the joint role of competence and confidence. Practical implications highlight the need for entrepreneurship education to integrate AI skill development and foster technological self-belief among students.

Youth preparing for self-reliance face a critical transitional period, as they must assume economic and social independence immediately upon care exit. Moreover, amid structural constraints stemming from a lack of social capital, rapid digital transformation and the diffusion of AI technologies pose both risks and opportunities for youth with weak social capital. Against this backdrop, this study examined the structural pathway through which cognitive and psychological variables—educational satisfaction, AI literacy, and career decision-making self-efficacy—lead to career preparation behavior among youth preparing for self-reliance who participated in the digital career education program D'velop. The findings indicate that greater improvements in AI literacy and career decision-making self-efficacy through program participation were associated with significantly higher levels of career preparation behavior. Notably, educational satisfaction did not have a direct effect on career preparation behavior. Instead, a fully mediated pathway was identified: career preparation behavior emerged only when competencies were strengthened through improved AI literacy and subsequently translated into enhanced career decision-making self-efficacy. These results suggest that emotional satisfaction or fragmented skill acquisition alone is insufficient; meaningful behavioral change occurs only when competency development is sequentially linked to self-efficacy beliefs. This study offers the following theoretical and policy implications. First, AI literacy education should be promoted as an alternative form of capital that can compensate for insufficient human networks among youth preparing for self-reliance with weak social capital. Second, self-reliance support policies should move beyond simple skill transfer by strengthening curricula and revising performance indicators so that mastery experiences can effectively enhance career decision-making self-efficacy. Third, integrated curricula should be designed to reflect a hierarchical pathway that begins with emotional satisfaction and proceeds through practical competencies and psychological confidence to behavior, thereby generating tangible career actions. Finally, a fundamental shift is needed from policies centered on economic and institutional support toward proactive interventions that foster internal growth and self-directed career design grounded in strengthened digital competencies.

Artificial intelligence is reshaping higher education, yet little is known about how students' AI related beliefs connect to their career expectations in emerging economy systems. This study examines links between AI literacy, perceived usefulness of AI, attitudes toward AI, AI self-efficacy, and career expectations in AI-driven industries among Vietnamese undergraduates. A cross-sectional survey of 850 students across five campuses was analysed using Partial Least Squares Structural Equation Modelling within an integrated Technology Acceptance Model and Social Cognitive Career Theory framework. AI literacy significantly influenced perceived usefulness, attitudes, and AI self-efficacy, while perceived usefulness was the strongest impact on both attitudes and self-efficacy. Together, literacy, usefulness, and attitudes explained 47.1% of the variance in AI self-efficacy, which in turn was the strongest effect on career expectations in AI-driven industries, accounting for 37.4% of their variance. These findings show that students' confidence in using AI is a key psychological mechanism linking AI literacy and perceived usefulness to career expectations for working in AI-intensive environments. The study provides large-scale evidence from a non-Western context and suggests that higher education institutions should embed structured AI literacy pathways, authentic AI-supported learning tasks, and explicit attention to responsible AI practices in order to strengthen both academic confidence and students' career expectations in AI-driven industries.

Artificial Intelligence (AI) is rapidly transforming the global world of work, raising important questions about how younger generations perceive its impact on their future careers. This study investigates the perceptions of Generation Y (Gen Y) and Generation Z (Gen Z) university students toward AI and its influence on employment opportunities. A quantitative survey was conducted with 153 respondents from universities in China and Malaysia, examining three key variables: AI awareness, attitudes toward AI, and perceptions of employment prospects. The regression model explains 59.9% of the variation in students’ perceptions (R² = 0.599). Both awareness (β = 0.400, p < 0.05) and attitude (β = 0.521, p < 0.01) significantly shape students’ views, with attitude exerting the stronger influence and mediating the effect of awareness on perception. These findings underscore the importance of higher education institutions in enhancing AI literacy, cultivating adaptive mindsets, and integrating technology-oriented curricula to prepare students for the demands of an AI-driven labor market. By fostering proactive and confident engagement with emerging technologies, universities can empower future professionals to navigate challenges and leverage opportunities in the evolving digital economy

This study evaluates an AI-supported, LinkedIn-based networking pedagogy in undergraduate business courses at a private university. Using a convergent mixed-methods design (N = 222), we analyzed platform analytics, branding artifacts, and reflections. Post-intervention, the mean number of impressions per post increased from under 200 to 4,925; the audience composition shifted toward industry professionals; and themes of vocational clarity and agency emerged. Findings indicate that AI prompting strengthens message quality, amplifies visibility, and broadens students’ professional networks. The intervention offers a scalable, values-aware model for integrating digital identity, AI literacy, and career readiness within business curricula.

The rapid integration of artificial intelligence (AI) into higher education has introduced transformative opportunities—such as personalized learning, democratized access to resources, and career readiness—while simultaneously creating critical dilemmas. College students now grapple with over-reliance on AI tools, information overload, skills gaps, ethical ambiguities, and psychological anxiety about competing with AI-driven automation. These challenges stem from institutional inertia, lagging curricula, and a lack of AI literacy among educators and learners. To address these issues, this essay proposes multifaceted solutions: pedagogical reforms emphasizing active learning and AI literacy, curriculum modernization to blend technical proficiency with uniquely human skills, institutional policies for ethical AI governance, and mental health support to mitigate AI-induced anxiety. By advocating for strategic collaboration between humans and AI, the essay underscores the urgency of reimagining education to balance technological efficiency with intellectual rigor, creativity, and ethical integrity. The path forward lies in fostering adaptability, equitable access, and human-AI synergy to empower students as resilient, ethically grounded leaders in an AI-augmented future.

This study aims to explore the current state of generative artificial intelligence (genAI) in the workplace and discuss a potential digital divide in relation to genAI. Using a quantitative approach, we study career-relevant predictors – family socio-economic status, education and work characteristics – and their relationship with different indicators of digital divide – access, genAI use, attitude toward AI and perceived AI literacy. To test our hypothesis, we used logistic and linear regression analyses. Additionally, latent profile analysis was conducted to identify patterns regarding work characteristics within the sample. Among the 1,341 participants, 326 individuals were genAI users. Our results show that higher family socio-economic status, education and enriched and demanding work can be linked to a more positive attitude toward AI and higher perceived AI literacy. In the case of access and frequency of use, the results were mixed. Our findings offer a novel contribution by examining a potentially upcoming digital divide in the case of genAI. We focus on how the career adaptation of the workforce might develop in the age of genAI. Importantly, we highlight that not all individuals may have an equal opportunity to adapt to genAI, which could hinder their future career development and reinforce patterns of inequality. Future research should address how to promote inclusivity and consider individual differences in adapting to genAI.

The integration of generative artificial intelligence (GAI) into higher education is transforming students’ learning processes, academic performance, and psychological well-being. Despite the increasing adoption of GAI tools, the mechanisms through which students’ AI literacy and self-regulated learning (SRL) relate to their academic and emotional experiences remain underexplored. This study investigates how AI literacy and SRL are associated with writing performance and digital well-being among university students in GAI-supported higher learning contexts. A survey was administered to 257 students from universities in China, and structural equation modeling was used to examine the hypothesized relationships. Results show that both AI literacy and SRL significantly and positively predict students’ writing performance, with SRL having a stronger effect. Moreover, AI literacy shows a positive association with GAI-driven well-being, with writing performance serving as a partial mediator in this relationship. These findings suggest that fostering both technological competencies and effective learning strategies may support students’ academic outcomes while supporting their psychological well-being in AI-enriched educational environments. By integrating AI literacy and SRL into a unified model, this study contributes to the growing body of research on GAI-driven well-being in higher education and offers practical implications for cultivating balanced and sustainable learning experiences in the age of GAI.

No abstract available

Artificial Intelligence (AI) literacy has emerged as a critical skill across various disciplines and industries, including education. This study aimed to identify the factors that influence educators' AI literacy and to examine the relationships among these factors. A sequential mixed methods approach was used to investigate the factors influencing faculty members' AI literacy, with qualitative data collected from 33 faculty members through focus group discussions and semi-structured interviews. Quantitative data were then gathered using the finalized survey instrument, completed by 538 faculty members from diverse disciplines and higher education institutions in Palestine. Data analysis was conducted using Smart PLS. The findings revealed several key factors that impact educators' AI literacy, including AI competencies, perceived usefulness of AI, ease of use, professional development, and community support. Additionally, prior experience with technology played a significant role in developing AI literacy. While the study's mixed methods design provided depth, one limitation was that the qualitative phase involved a relatively small sample. Future research should further explore the broader implications of AI in education and its integration across various academic fields.

This study delves into the factors that drive teachers’ adoption of generative artificial intelligence (GenAI) technologies in higher education. Anchored by the technology acceptance model (TAM), the research expands its inquiry by integrating the constructs of intelligent technological pedagogical content knowledge (TPACK), AI literacy, and perceived trust. Data were gathered from a sample of 237 university teachers through a structured questionnaire. The study employed structural equation modeling (SEM) to determine the relationships among the constructs. The results revealed that both AI literacy and perceived ease were the most influential factors affecting teachers’ acceptance of GenAI. Notably, intelligent TPACK and perceived trust were found to be pivotal mediators in this relationship. The findings underscore the importance of fostering AI literacy and adapting intelligent TPACK frameworks to better equip educators in the age of AI. Furthermore, there is a clear need for targeted professional development initiatives focusing on practical training that enhances AI literacy. These programs should provide hands-on experience with GenAI tools, boosting educators’ confidence and ability to integrate them into their teaching practices.

This paper seeks to contribute to the emergent literature on Artificial Intelligence (AI) literacy in higher education. Specifically, this convergent, mixed methods case study explores the impact of employing Generative AI (GenAI) tools and cyber-social teaching methods on the development of higher education students ’ AI literacy. Three 8-week courses on advanced digital technologies for education in a graduate program in the College of Education at a mid-western US university served as the study sites. Data were based on 37 participants ’ experiences with two different types of GenAI tools – a GenAI reviewer and GenAI image generator platforms. The application of the GenAI review tool relied on precision fine-tuning and transparency in AI-human interactions, while the AI image generation tools facilitated the participants ’ reflection on their learning experiences and AI ’ s role in education. Students ’ interaction with both tools was designed to foster their learning regarding GenAI ’ s strengths and limitations, and their responsible application in educational contexts. The findings revealed that the participants appeared to feel more comfortable using GenAI tools after their course experiences. The results also point to the students ’ enhanced ability to understand and critically assess the value of AI applications in education. This study contributes to existing work on AI in higher education by introducing a novel pedagogical approach for AI literacy development showcasing the synergy between humans and artificial intelligence.

This study utilizes a dual-perspective framework that integrates stable individual differences (Big Five personality traits) and developable technological competencies (AI literacy). Drawing on the Theory of Planned Behavior (TPB) and Innovation Resistance Theory (IRT), it constructs both a "cognitive empowerment pathway" and a "psychological resistance pathway" to systematically reveal the behavioral intention mechanisms underlying the adoption of AI in teaching by design faculty in Chinese higher education institutions. Based on 513 valid survey responses, the research applies a mixed-methods approach. Structural Equation Modeling (SEM) results demonstrate that: 1) Openness (OPE), Extraversion (EXT), and AI literacy significantly and positively influence perceived behavioral control (PBC), while EXT and Agreeableness (AGR) significantly reduce psychological barriers (PB), collectively enhancing usage intention (UI); 2) Neuroticism (NEU) has a negative impact on PBC and a positive effect on PB, which consequently inhibits UI; 3) OPE, AGR, and AI literacy are significant positive predictors of UI, whereas NEU has a significant negative relationship with UI. The Fuzzy-set Qualitative Comparative Analysis (fsQCA) further identifies complex configurational effects between Big Five personality traits (BFP) and AI literacy, revealing eight discrete causal pathways leading to high AI usage intention among design educators, which stands in contrast to some SEM results. This study provides a systematic examination of how individual differences affect AI adoption behaviors among design faculty in Chinese higher education from an interdisciplinary perspective, thereby expanding the theoretical framework in this domain and offering empirical evidence and practical recommendations for capacity-building in AI-enabled teaching among design educators in China.

This study presents a cross-national quantitative analysis of how university students in the United States and Bangladesh interact with Large Language Models (LLMs). Based on an online survey of 318 students, results show that LLMs enhance access to information, improve writing, and boost academic performance. However, concerns about overreliance, ethical risks, and critical thinking persist. Guided by the AI Literacy Framework, Expectancy-Value Theory, and Biggs'3P Model, the study finds that motivational beliefs and technical competencies shape LLM engagement. Significant correlations were found between LLM use and perceived literacy benefits (r = .59, p<.001) and optimism (r = .41, p<.001). ANOVA results showed more frequent use among U.S. students (F = 7.92, p = .005) and STEM majors (F = 18.11, p<.001). Findings support the development of ethical, inclusive, and pedagogically sound frameworks for integrating LLMs in higher education.

No abstract available

As artificial intelligence increasingly permeates higher education systems worldwide, developing students' ethical awareness has become essential for responsible AI implementation. This study seeks to examine the connections between technical understanding, applied knowledge, and critical appraisal in shaping ethical awareness within the context of AI literacy. The study utilizes a quantitative method, applying Partial Least Squares Structural Equation Modeling (PLS-SEM) to data gathered from 322 university students. The findings indicate that technical understanding has a direct favorable influence of 0.180 on ethical awareness, while applied knowledge demonstrates a stronger impact of 0.467. Critical appraisal serves as a significant complementary partial mediator, with indirect path coefficients of 0.083 for technical understanding and 0.155 for applied knowledge, strengthening their relationships with ethical awareness. This study concludes that AI literacy educational programs should not only emphasize technical and applied knowledge but also foster critical appraisal skills to promote ethical AI usage.

This study explores the role of artificial intelligence (AI) in teacher education, focusing on preservice teachers’ preparedness for AI integration. It examined the levels of AI literacy, readiness-confidence, and acceptance among preservice teachers in Philippine higher education institutions, and investigated differences across gender, academic discipline, and internet connectivity. Using a cross-sectional survey design, data were collected from 384 preservice teachers through validated instruments that measured AI literacy, readiness-confidence, and acceptance. Analyses included descriptive statistics, independent samples t-tests, and correlation analysis. Findings revealed high readiness-confidence and moderate to high literacy and acceptance levels. Significant differences emerged, with male preservice teachers, STEM students, and those with reliable internet access reporting higher scores, particularly in readiness-confidence. Strong positive correlations among literacy, readiness-confidence, and acceptance underscored their interdependent relationship in shaping preparedness for AI integration. These results emphasize the need for tailored and inclusive AI education and training programs that address demographic and infrastructural disparities. Beyond equipping preservice teachers with skills, preparing them for AI adoption is about shaping the future of education by ensuring that tomorrow’s classrooms are led by educators who are competent, confident, and capable of driving innovation, equity, and progress in a rapidly evolving digital age.  

As AI becomes integral to students’ learning, educators must adapt to this AI-driven landscape. However, there is a notable gap in research focusing on fostering AI literacy among higher education lecturers. This paper presents a design-based research project aimed at developing a professional development curriculum for educators at the tertiary level through iterative cycles. In the first cycle, a voluntary internal professional development course was offered as a blended learning scenario. Evaluation involved a validated AI literacy performance test and AI readiness scale items. The results of the first cycle are going to be presented and discussed. Based on these findings, modifications to the course are outlined.

The integrative literature review addresses the conceptualization and implementation of AI Literacy (AIL) in Higher Education (HE) by examining recent research literature. Through an analysis of publications (2021-2024), we explore (1) how AIL is defined and conceptualized in current research, particularly in HE, and how it can be delineated from related concepts such as Data Literacy, Media Literacy, and Computational Literacy; (2) how various definitions can be synthesized into a comprehensive working definition, and (3) how scientific insights can be effectively translated into educational practice. Our analysis identifies seven central dimensions of AIL: technical, applicational, critical thinking, ethical, social, integrational, and legal. These are synthesized in the AI Literacy Heptagon, deepening conceptual understanding and supporting the structured development of AIL in HE. The study aims to bridge the gap between theoretical AIL conceptualizations and the practical implementation in academic curricula.

This chapter introduces the AI&Data Acumen Learning Outcomes Framework, a comprehensive tool designed to guide the integration of AI literacy across higher education. Developed through a collaborative process, the framework defines key AI and data-related competencies across four proficiency levels and seven knowledge dimensions. It provides a structured approach for educators to scaffold student learning in AI, balancing technical skills with ethical considerations and sociocultural awareness. The chapter outlines the framework's development process, its structure, and practical strategies for implementation in curriculum design, learning activities, and assessment. We address challenges in implementation and future directions for AI education. By offering a roadmap for developing students'holistic AI literacy, this framework prepares learners to leverage generative AI capabilities in both academic and professional contexts.

This research aims to explore the level of artificial intelligence (AI) literacy and ethical awareness among university students in Indonesia, focusing on their understanding of AI as well as the ethical challenges associated with its use in education and daily life. Using a mixed-methods approach, this study collected quantitative data through a survey involving 300 students from various universities in Indonesia and qualitative data through in-depth interviews with 30 selected respondents. The results show that although AI literacy among students from engineering majors is quite high, students from non-engineering majors show lower understanding. In addition, there are significant concerns regarding ethical issues such as data privacy, algorithmic bias, and the social impact of AI. The study also identified several key challenges, including the lack of integration of AI literacy in higher education curricula and limited training related to AI ethics. Based on these findings, this study recommends the need to strengthen a curriculum that includes AI literacy and comprehensive ethics education in all departments. This research contributes to the development of a more inclusive and responsible education policy in facing the digital era in Indonesia.

This study moves beyond theoretical frameworks to empirically analyse artificial intelligence (AI) literacy among undergraduate students, identifying distinct performance profiles to inform educational interventions. Using a validated, performance-based instrument, we assessed the functional, technical and socio-critical competencies of 353 students at a private university in Mexico. Our analysis revealed three distinct student profiles: lower performance (n = 85), mid-range proficiency (n = 158) and higher competence (n = 107). A critical finding across all profiles was a significant deficit in the socio-critical dimension, with only 2.6% of students demonstrating outstanding ability. Furthermore, the profiles varied significantly by gender and academic stage, challenging traditional assumptions about technology literacy. These findings provide an evidence-based typology for diagnosing student needs and developing targeted, equitable educational strategies to foster comprehensive AI literacy in higher education.   Implications for practice or policy: Curriculum leaders should view AI literacy as a transversal competence, integrating ethical and critical reflection across curricula. Educators and instructional designers must apply differentiated instruction based on learner profiles, recognising that AI literacy development is complex and challenges assumptions about gender and academic progression. Policymakers should promote validated assessment tools to replace anecdotal evidence with empirical data, guiding institutional strategies and resource allocation for AI education.

The rapid integration of AI into higher education has reshaped the intelligent education ecosystem, demanding systemic mechanisms for faculty AI literacy development. This study bridges educational ecology and professional development theories to propose a "environmental input–agent transformation–ecological output" framework, addressing the disconnect between technological evolution and faculty readiness. Key challenges include fragmented institutional integration, ethical risks from algorithmic dominance, and regional disparities. Through institutional analysis and multi-agent simulation, we identify a three-dimensional mechanism: driving forces, coupling pathways and synergistic effects. The study offers a governance toolkit for China’s plan-driven context, balancing algorithmic efficiency with humanistic values while aligning with China’s Education Modernization 2035.

The ever-growing number of new and constantly evolving AI-based (writing) tools poses a major challenge for university teaching, especially in writing classes. While students are increasingly exploring these tools without reflecting their use critically enough, many instructors are unsure how to approach these new tools in their own teaching and whether or how to integrate them into their courses. They are unfamiliar with many of the tools or feel overwhelmed by the abundance of options and the obvious or sometimes perceived expectation to integrate them into their teaching. As part of an award-winning pilot project, a platform was therefore developed to support instructors in teaching basic AI literacy skills, providing curated teaching materials and specially designed workshops. Survey results, ongoing subscriptions to the platform, and its partial integration into a new cross-university project all highlight the strong appreciation and continued relevance of these resources among instructors and higher education experts.

Introduction: The rapid advancement of artificial intelligence and digital technologies has created new requirements for faculty professional development in higher education. This study examines connections between AI literacy, digital competencies, institutional support, technological self-efficacy, teaching effectiveness, and professional development readiness. Objective: To investigate how AI literacy, digital competencies, institutional support, and technological self-efficacy affect professional development readiness among higher education faculty, with teaching effectiveness as a mediating factor. Method: A cross-sectional survey was conducted with 412 faculty members from various higher education institutions between September 2024 and January 2025. Data were analyzed using PLS-SEM to examine direct effects and mediation relationships. Results: All four independent variables significantly influenced teaching effectiveness (digital competencies β=0.324, AI literacy β=0.298, technological self-efficacy β=0.203, institutional support β=0.185) and professional development readiness. Teaching effectiveness strongly predicted professional development readiness (β=0.487) and partially mediated all relationships (VAF: 41.7%-50.5%). Conclusions: Teaching effectiveness serves as a crucial mediating mechanism through which AI literacy, digital competencies, institutional support, and technological self-efficacy enhance faculty professional development readiness. These findings suggest that comprehensive faculty development programs should simultaneously address multiple technological competencies while emphasizing practical teaching applications.

Purpose: The study investigates the effectiveness of Generative AI Literacy, which includes Technical Proficiency, Critical Evaluation, Ethical Awareness, and Creative Application in influencing the academic performance of postgraduate management and education students. It also addresses the mediating role of the student engagement in the learning outcomes in the higher education. Methods: The sample was 197 postgraduate students of different stream in Kapilvastu District. Descriptive and Explanatory research design has been used to conduct the study. The questionnaire was a structured questionnaire with 7 points Likert scale used to collect data. PLS-SEM was used to evaluate the relationship between variables. Results: The findings indicate that Generative AI Literacy is an important indicator of academic performance (0.486). Embracing technical excellence and creative application not to mention other factors have the largest factor loadings but other factors including critical evaluation and ethical awareness have also made a substantial contribution. AI literacy (0.516) had a positive influence on the student engagement (0.266) and, therefore, student academic performance (0.209). The mediation analysis has established that there is indeed such a thing as student engagement being a mediator of relationship between AI literacy and academic success and that student endowed with high AI competencies are most likely to be active hence more successful in their performance.  Implications: Higher education institutions need to implement structured AI literacy programs, ethical/critical use of AI tools and models of teaching that are engagement-based in order to foster academic excellence in an AI-infused learning environment. Originality: The present study is among the pioneer studies involving the application of PLS-SEM to test the multidimensional nature of Generative AI Literacy and its mediated effect on student engagement in academic performance in the environment of higher education within Nepal.

Aim/Purpose: This study empirically analyzes how a tailored AI literacy initiative can empower higher education faculty in creative and cultural-focused disciplines to adopt AI image generators into their courses. It achieves that by first identifying the foundational components required for developing an AI literacy initiative for integrating AI image generators into creative and cultural courses (RQ1) and then assessing how an AI literacy workshop designed around these components influences faculty’s perceived confidence, understanding, and readiness to adopt AI image generators (RQ2). Background: The proliferation of AI image generators has disrupted the Creative and Cultural Industries (CCI), creating both opportunities and challenges for educators. Faculty face barriers such as limited technical skills, ethical concerns, and a lack of pedagogical strategies. Despite growing interest, few AI literacy initiatives are tailored to the unique needs of creative and culturally-focused faculty. Our study is presented against such a backdrop. Methodology: A two-phase mixed-methods approach was conducted. Phase One involved focus group interviews (Activity 1) with faculty in CCI-focused disciplines (e.g., arts, design, architecture, marketing, music, fashion, film, IT, and the like) to identify key components for designing an AI literacy initiative. In Phase Two, the designed workshop (Activity 2) was delivered to 51 CCI faculty from a midwestern university. Data was collected through pre- and post-workshop surveys, as well as live polls to assess changes in participants’ perception around AI image generators’ adoption into their courses. Contribution: This study offers multiple contributions. The systematic data collection and analysis of participants’ data (empirical contribution) through a mixed-methods approach, which combines statistical analysis and human-AI thematic analysis (methodological contribution), informed the design and development of an AI literacy workshop (artifactual contribution) tailored for higher education faculty in CCI disciplines. Additionally, it contributes to the underexplored area of adopting image generators ethically and via pedagogical best practices into creative and cultural education. Findings: In Activity 1, the key components for designing an AI literacy initiative tailored for CCI faculty include technical skills, ethical awareness, and pedagogical strategies combined with practical applications and continuous AI Literacy support/resources. In Activity 2, faculty showed a positive attitudinal shift. They reported increased confidence, pedagogical and ethical awareness, access to institutional resources, and practical applications post-workshop. They expressed appreciation for image generators as complementary tools and readiness to integrate them into their teaching. Recommendations for Practitioners: Institutions should support faculty development through accessible AI tools, discipline-based literacy resources, hands-on training, and ongoing learning opportunities tailored to them. Interdisciplinary collaboration and developing ethical guidelines are essential for responsible GenAI adoption. Recommendation for Researchers: Future research should examine the long-term impacts of AI literacy initiatives on teaching and learning outcomes, exploring other multimodal GenAIs, and include broader stakeholders (e.g., students, administrators, and even technologists). Impact on Society: Equipping higher education faculty with AI literacy around GenAIs fosters responsible technology adoption and use, enhances teaching practices and learning outcomes, and prepares students to thrive in an AI-driven workforce, where humans and AIs can co-exist, collaborate, and co-create. Future Research: Future research should include longitudinal studies to assess faculty engagement and student outcomes. Expanding future research across institutions and academic levels (e.g., K-12) will help develop scalable, discipline-specific AI literacy resources, initiatives, and frameworks.

This study analysis the dimensions of artificial intelligence (AI) literacy in building future leadership for intelligent, ethical and sustainable communities. Using a quantitative survey approach, data were collected from 235 university students, as future leaders, involved in the management of interdisciplinary student organizations. The analysis focused on five dimensions of AI literacy identified by the Digital Education Council (DEC) as being linked to future leadership: understanding AI and data (D1), evaluating AI content (D2), ethical and responsible use of AI (D3), utilizing AI for collaboration, creativity, and adaptation (D4), and understanding AI trends and their impact on careers (D5) in the context of future leaders to build intelligent ethical and sustainable communities. An analysis of the data indicates that D1, D4 and D5 has achieved a high level of success in training for future generation leadership. On the other hand, D2 and D3 exhibit not much progress. Such findings can be of use to higher education institutions in educating leaders for sustainable community development. In addition, this article adds to the worldwide conversations on AI for sustainable education by calling attention to digital literacy as a basis for building adaptive and ethical leadership towards the realization of 11 th SDGs.

The article focuses on the importance of preparing computer science teachers to use artificial intelligence (AI) in their professional practice and to develop their AI competence to equip students with the skills needed to face modern challenges and opportunities through digital technologies. The rapid advancement of AI demands continuous professional development from computer science teachers. Professional training programs should support the formation of competencies related to selecting and analysing AI-based educational resources, creating instructional content using AI, and assisting students in environments where learning is partially or fully AI-driven. An analysis of research allowed the identification of approaches to integrating AI into the educational process and highlighted the potential of the AI competence frameworks for teachers and students as tools to define directions for professional growth. This includes the establishment of AI hubs and the involvement of highly competent AI teachers in delivering professional development courses. A survey of 180 teachers regarding their self-assessed AI competence and needs for professional development programs enabled a comparison between theoretical insights and the actual demands of computer science educators in contrast to teachers of other disciplines. Based on this, strategies and directions for teachers’ professional growth were identified to ensure the effective implementation of innovative AI tools. These include learning analytics, AI ethics, instructional material design, and more. The integration of AI into computer science education shows significant potential to enhance student learning outcomes. Survey results confirm the readiness of computer science teachers to engage in professional development and participate in AI-focused training programs. The professional development of computer science teachers in the context of AI should be based on the integration of technical, pedagogical, ethical, and regulatory aspects. This study explores the significance and key areas of teacher preparation for AI use in postgraduate education and presents ideas and approaches to fostering AI competence among computer science teachers, ultimately supporting educational quality and preparing the younger generation for the challenges of the future.

No abstract available

Importance. The active development of artificial intelligence (AI) technologies and their integration into education is creating a gap between the potential of AI and the actual readiness of teachers to apply these tools effectively and critically in their professional practice. The study presents a comprehensive analysis and the stages of forming AI competence in foreign language teachers. Materials and Methods. The research is based on an interdisciplinary approach and an analysis of Russian and international scientific sources. The empirical base consisted of survey (60 participants) and reflexive questionnaire (50 participants) results received from foreign language teachers who completed a professional development course at the Faculty of Foreign Languages and Area Studies of Lomonosov Moscow State University from May to June 2025. The survey was aimed at diagnosing the level of awareness, readiness, and practical use of AI technologies, as well as identifying professional needs. A reflexive questionnaire method is used to collect qualitative data, allowing to obtain teachers’ subjective assessment. Results and Discussion. The study presents and substantiates the structure of AI competence for foreign language teachers, outlines the stages of its formation, and refines the concept and structure of neurolinguodidactic competence. A comparative analysis of international and Russian approaches to forming the structure of AI competence is provided, along with a practice-tested model of professional development for foreign language teachers in the field of artificial intelligence.he obtained data indicate that the course contributed to the development of both theoretical knowledge and practical skills necessary for teaching foreign languages with AI support. Conclusion. The practical significance of the study lies in the development of a ready-toimplement professional development model. However, key aspects requiring further attention and refinement were identified: the volume of information, technical difficulties with the distance learning platform, and the need to expand the language support of AI tools. Research prospects are associated with the adaptation of the proposed model for various pedagogical contexts and its further optimisation considering the identified difficulties.

Teachers are required to develop their professional capabilities. However, in practice, many problems affect teachers’ professional development (TPD), such as overflowing administrative tasks, the lack of competence to integrate technology, and the lack of time allocated for continued professional development. Nowadays, the use of artificial intelligence (AI) is considered a way to solve these problems. This research aimed to investigate how AI is used in TPD. This qualitative case study design involved six in-service teachers who have joined the teachers’ professional education program in the Indonesian context. The findings indicate that AI provides an inclusive basis for transforming TPD for several reasons. For example, AI supports teachers’ personality competence (commonly known as professional conduct) by helping lighten their administrative workload. AI assists teachers’ pedagogical competence by offering easy integration of technology. AI upgrades teachers’ professional competence by providing unlimited resources, especially related to online professional development courses. Lastly, AI intensifies teachers’ social competence by providing opportunities to connect with each other. This study implies that AI is important to use in today’s TPD due to its adaptability, compatibility, and flexibility. While this study references specific AI tools used in TPD practices like ChatGPT, Perplexity, and Google Sites, it did not conduct an in-depth analysis of any single AI system. It is recommended that further studies focus on a particular AI tool to better understand its impact on TPD.

The pervasive integration of Artificial Intelligence (AI) into the educational landscape has precipitated a profound paradigm shift, compelling a systematic re-evaluation of pedagogical methodologies and educator roles across various disciplines. This paper focuses on the field of International Chinese Language Education (ICLE), a domain characterized by its unique intercultural and linguistic complexities. It posits that the advent of AI is not merely an introduction of new tools but a catalyst for a fundamental transformation of the ICLE teacher's identity and professional responsibilities. This conceptual study moves beyond a utilitarian discussion of AI applications to theoretically delineate the multifaceted role transitions required of ICLE educators—from knowledge transmitters to learning architects, from assessors to diagnosticians, from content creators to resource curators, from cultural ambassadors to intercultural competence cultivators, and from classroom managers to digital learning community orchestrators. By analyzing these evolving roles, the paper subsequently proposes a structured framework for professional development, emphasizing the cultivation of AI literacy, advanced pedagogical design principles, data-informed instructional strategies, and enhanced socio-affective competencies. The study argues that embracing a symbiotic human-AI collaborative model is imperative for the sustainable and effective development of ICLE. It concludes that the future-ready ICLE professional is not one who is replaced by AI, but one who is empowered by it, strategically leveraging technology to augment the irreplaceable human dimensions of language teaching and intercultural communication. This paper aims to provide a theoretical foundation for scholars, educators, and institutions to navigate the complexities of the AI era and to proactively shape the future of international Chinese language instruction.

As Generative Artificial Intelligence (GenAI) becomes increasingly embedded in academic and professional settings, there is a growing need for pedagogically grounded approaches to cultivate Artificial Intelligence (AI) competence. This paper introduces a learning activity design model based on Kolb’s Experiential Learning Cycle, aligned with the United Nations Educational, Scientific and Cultural Organisation (UNESCO) framework for AI competence, emphasising ethical, critical, and creative engagement with AI systems. The model operationalises AI competence development through practical learning activities, structured reflection, conceptual exploration, and human-AI co-creation, positioning AI tools as cognitive partners rather than passive utilities. To evaluate the model, we conducted a case study of Partnering with AI, a half-day workshop that scaffolded students through three progressive tiers of AI competence: Understand, Apply, and Create. Evaluation findings reveal significant gains in student confidence, ethical awareness, and practical skill, supporting the effectiveness of this experiential design. The paper concludes with recommendations for embedding this learning activity design model into higher education curricula to support sustainable AI competence-building.

In the modern world, the rapid development of artificial intelligence significantly influences the demands on the cognitive competence of young people. The formation of cognitive competence becomes an essential factor in the successful adaptation of young people to new conditions of work and life. This requires not only critical thinking and the ability to take a creative approach but also the skill to make decisions under conditions of uncertainty. The development of cognitive competence serves as a foundation for cultivating flexible thinking skills, allowing young people to quickly adapt to technological and social changes. Furthermore, cognitive competence improves the competitiveness of young professionals in the labor market, empowering them to address global challenges by integrating innovative approaches and cutting-edge technologies. Young people must acquire skills in working with large datasets, algorithms, and analytical tools, positioning cognitive competence as a cornerstone of their professional growth. This study aims to analyze the impact of participation in the development of artificial intelligence projects on the cognitive skills of gifted students of the “Polit” Science Lyceum. The study is based on the analysis of student projects presented in scientific competitions to evaluate their effect on the development of scientific thinking, critical analysis, and decision making skills in the context of working with AI. The research results confirm the positive impact of project-based work in the field of AI on students’ cognitive competence, particularly in the context of solving complex scientific problems and understanding the nature and manageability of AI technologies. Consequently, participating in AI project development fosters the cognitive competence of gifted youth, contributing not only to their success, but also to advances in science and society.

This study explores the diffusion of disruptive innovation in Islamic primary education through the implementation of Smart Teacher AI, an AI-driven professional learning platform designed to strengthen teachers’ digital competence. The research employs a Research and Development (R&D) approach based on Borg and Gall’s model, focusing on the implementation phase of Smart Teacher AI in SD Islam Al Ittihad, Cibubur. Data were collected from 25 teachers using observation sheets, digital competence rubrics, focus group discussions, and system usage analytics. The findings reveal that the implementation of Smart Teacher AI aligns with Rogers’ diffusion of innovation framework, showing gradual adoption from innovators to early and late adopters. The platform significantly improves teachers’ digital skills in lesson planning, designing interactive learning media, and evaluating digital learning outcomes. The novelty of this study lies in integrating AI-assisted professional learning within the framework of Islamic values, bridging the gap between technological disruption and faith-based education. The results contribute to both theoretical and practical insights on innovation diffusion in education, emphasizing that digital transformation in Islamic schools requires not only technology readiness but also value-driven leadership and sustainable pedagogical adaptation.

Generative artificial intelligence (GenAI) is reshaping professional education, hastening the obsolescence of competences and unsettling traditional, linear approaches to upskilling and reskilling. This article advances a new logic of competence development to characterise this shift. Drawing on institutional reports (DeVry University, 2024; World Economic Forum, 2023, 2025) and established theoretical traditions, it shows that the competence landscape is moving towards hybrid models that combine technical expertise with adaptive capabilities such as resilience, reflective judgement and AI literacy. A comparative analysis of employers’ and employees’ perspectives reveals both opportunities and risks, notably gaps in recognition, digital inequalities and a shifting of responsibility for learning. Using the gAI-PT4I4 prototype as an illustrative case, the article demonstrates how GenAI can serve as a vehicle for adaptive, personalised learning and training, while raising questions about scalability, ethics and deskilling. The conceptual contribution is to define competence development as an iterative, co-created and adaptive process, in contrast to static, competence-based models. At the same time, AI can itself provide a pathway for developing new competences, supporting the complex processes of upskilling and reskilling. The gAI-PT4I4 case connects the conceptual argument to a concrete example of AI-enabled adaptive learning.

In this discussion, we consider how the use of scenario-based assessment (SBA) can provide students with a way of developing the digital communication skills that business communication research has found they will need for the workplace, alongside other aspects of professional competence. This is because SBA can be employed to engage learners in the same types of authentic performance tasks in a situated context that they will likely encounter in their professional lives. In addition, SBA can also be used to maximize the integrity of an assignment by harnessing the positive effects of using generative Artificial Intelligence (AI) tools, while simultaneously mitigating against the misappropriation of AI by students. SBA allows learners to practice both their digital, and other, communication skills as well as contributing to their understanding of professional practice, and it also provides instructors with a powerful form of formative assessment. Our aim is to put forward a motivating and effective way of helping our students to develop the skills that they will need to become successful communicators in a postpandemic professional world.

As Artificial Intelligence (AI) reshapes education, professional development (PD) must go beyond tool training to foster critical, meaningful integration. Initial PD should introduce AI’s uses and challenges, but also address the impact on teaching and learning. This paper explores and reflects upon Phase II of the FAITH project, a transatlantic design-based initiative developing an AI and Education (AI&ED) model for higher education. Effective AI pedagogy is grounded in socially constructed, hands-on experiences where educators design lessons, generate content, and critically assess AI outputs. Such approaches build confidence, competence, and prevent mechanical adoption. Leadership and policy must further support a dual PD strategy: immediate classroom applications alongside preparation for broader societal shifts. Early FAITH findings show introductory courses spark essential dialogue, but PD must remain dynamic, ethical, and intentional. Phase II combines theoretical exploration (e.g., sustainability, ethics) with context-relevant practice. Ultimately, AI&ED should be understood as a lifelong professional learning journey.

This article addresses the use of artificial intelligence (AI) tools in the professional development of teaching staff within the context of the digital transformation of education. The growing role of intelligent technologies demands that educators possess a high level of digital competence and the ability to integrate AI into their professional activities. The study combines qualitative and quantitative research methods. A content analysis of international and national scientific publications, legal regulations, and digital education support programs has been conducted. Based on the synthesis of Ukrainian experience, the article analyses educators’ main directions, barriers, and needs regarding integrating AI into their professional development. An analysis of international experience, particularly within the framework of EU regulatory initiatives (such as the AI Act) and studies by foreign authors, indicates a profound transformation in approaches to teacher professional development. AI is increasingly used to create personalized educational pathways, adaptive content, automated assessment, and learning analytics. Ethical, legal, and infrastructural challenges associated with AI implementation are emphasized. A survey of 487 Ukrainian teaching professionals was conducted to assess their level of awareness, frequency of AI tool usage, purposes of application, and self-assessment of digital competences. It was found that most respondents (65.8 %) occasionally use AI in their work, while 28 % mainly apply AI tools for their professional development. At the same time, 57.1 % of educators assessed their AI proficiency as average, and 26.9 % as low. A strong demand for convenient, free, user-friendly tools and scientific and methodological support in AI integration processes was identified. The article also describes activities conducted by the authors between 2023 and 2025, including international conferences, webinars, workshops, and practical sessions attended by over 750 educators. These events aimed to develop skills in modern AI tools for generating texts, images, presentations, and more. The results demonstrated high participant engagement, a desire for continued learning, and readiness to integrate AI into their pedagogical practices.