就业能力忽视课程-训练-AI非线性互动与民办本科三环节整合

经济林与果树栽培学在促进农业结构调整、实现农民增收及推动地方经济增长方面发挥着重要作用。人工智能技术的迅猛发展虽然改变了知识获取的方式，但也带来了新的教育思考。本文以广西大学为例，分析了“经济林与果树栽培学”课程教学存在以下痛点：在知识维度，知识碎片化导致学生对学科体系缺乏整体理解；在能力维度，学生普遍存在实践能力与科研思维匮乏的问题；在发展维度，则表现为团队合作与沟通意识的明显不足。基于此，提出“以讨论促思维、以讨论激创新”的创新教学理念，实施以“讨论互动”为导向的教学方法，引导学生称为思考与创新的主导者。具体改革途径如下。①创新教学理念，强调以讨论引导探究，提出具有挑战性的开放性问题，引导学生在讨论中学习；主张讨论促进创新，鼓励学生在自主探究基础上提出新问题、新假设，借助AI工具进行多样资料分析与验证。②在教学内容方面，构建涵盖基础知识、综合应用和创新探索的“三层级问题库”（多层次讨论主题库），并引入AI工具辅助学生进行信息获取、复杂问题剖析与深度讨论。③在教学环节创新性地采用“四步教学法”，即通过情境引导、分组讨论、案例分析、反思总结4个步骤，逐步引导学生从被动参与转为主动提问，激发其思维潜能。④在评价机制上，建立了关注创新问题生成能力的教学考核体系，引入过程性考核，对学生的讨论深度、团队合作质量及创新实践表现进行全面评估与即时反馈。经过2023—2025年的教学实践，教改成效显著，高频的师生互动与生生研讨显著提升了学生的知识内化率，提高了学生的科学探究能力。最后，对改革中存在的问题进行了反思，虽然AI工具降低了信息获取门槛，但容易导致学生阐述“思维惰性”，在未来的互动设计中，重点不在于教学生如何操作AI工具，而是引导学生如何对AI生成的结果进行批判性研讨，从单向的“人机问答”回归多维的“人人辩论”，实现对知识的深度内化。

Generative AI is reshaping higher education programming through vibe coding, where students collaborate with AI via natural language rather than writing code line-by-line. We conceptualize this practice as help-seeking, analyzing 19,418 interaction turns from 110 undergraduate students. Using inductive coding and Heterogeneous Transition Network Analysis, we examined interaction sequences to compare top- and low-performing students. Results reveal that top performers engaged in instrumental help-seeking -- inquiry and exploration -- eliciting tutor-like AI responses. In contrast, low performers relied on executive help-seeking, frequently delegating tasks and prompting the AI to assume an executor role focused on ready-made solutions. These findings indicate that currently generative AI mirrors student intent (whether productive or passive) rather than optimizing for learning. To evolve from tools to teammates, AI systems must move beyond passive compliance. We argue for pedagogically aligned design that detect unproductive delegation and adaptively steer educational interactions toward inquiry, ensuring student-AI partnerships augment rather than replace cognitive effort.

Classical robot ethics is often framed around obedience, including Asimov's laws. This framing is insufficient for contemporary AI systems, which are increasingly adaptive, generative, embodied, and embedded in physical, psychological, and social environments. This paper proposes conditional mutualism under governance as a framework for human-AI coexistence: a co-evolutionary relationship in which humans and AI systems develop, specialize, and coordinate under institutional conditions that preserve reciprocity, reversibility, psychological safety, and social legitimacy. We synthesize concepts from computability, machine learning, foundation models, embodied AI, alignment, human-robot interaction, ecological mutualism, coevolution, and polycentric governance. We then formalize coexistence as a multiplex dynamical system across physical, psychological, and social layers, with reciprocal supply-demand coupling, conflict penalties, developmental freedom, and governance regularization. The model gives conditions for existence, uniqueness, and global asymptotic stability of equilibria. We complement the analytical results with deterministic ODE simulations, basin sweeps, sensitivity analyses, governance-regime comparisons, shock tests, and local stability checks. The simulations indicate that governed mutualism reaches a high coexistence index with negligible domination, whereas insufficient or excessive governance can produce domination, weak-benefit lock-in, or suppressed developmental freedom. The results suggest that human-AI coexistence should be designed as a co-evolutionary governance problem rather than as a static obedience problem.

In measurement theory, instruments do not simply record reality; they help constitute what is observed. The same holds for generative AI evaluation: benchmarks do not just measure, they shape what models appear to be. Functionalist benchmarks treat models as isolated predictors, while prescriptive approaches assess what systems ought to be. Both obscure the sociotechnical processes through which meaning and values are enacted, risking the reification of narrow cultural perspectives in pluralist contexts. This thesis advances a descriptive alternative. It argues that generative AI must be evaluated as a pluralist sociotechnical system and develops Machine-Society-Human (MaSH) Loops, a framework for tracing how models, users, and institutions recursively co-construct meaning and values. Evaluation shifts from judging outputs to examining how values are enacted in interaction. Three contributions follow. Conceptually, MaSH Loops reframes evaluation as recursive, enactive process. Methodologically, the World Values Benchmark introduces a distributional approach grounded in World Values Survey data, structured prompt sets, and anchor-aware scoring. Empirically, the thesis demonstrates these through two cases: value drift in early GPT-3 and sociotechnical evaluation in real estate. A final chapter draws on participatory realism to argue that prompting and evaluation are constitutive interventions, not neutral observations. The thesis argues that static benchmarks are insufficient for generative AI. Responsible evaluation requires pluralist, process-oriented frameworks that make visible whose values are enacted. Evaluation is therefore a site of governance, shaping how AI systems are understood, deployed, and trusted.

Objective. To explore how novice programmers'trust in Artificial Intelligence-driven Development Environments (AIDEs) relates to their coding performance and AI compliance while programming under time pressure. Background. Computer programming has undergone rapid upheaval due to state-of-the-art AIDEs, which provide clever automation for many aspects of software development. A longstanding interest of researchers of automation more generally has been the attitude of trust. Decades of research seek to explain how influencing trust can help to achieve desirable outcomes in different domains, but very limited work has provided similar focus on trust in AIDEs. Method. We collected subjective measures of trust along with objective measures of performance and AIDE compliance from a diverse group of 27 novice programmers between two study locations. Results. Our results corroborated traditional understandings of how trust changes through experiences. However, we did not find a relationship between trust and subsequent compliance during programming tasks. Greater compliance was associated with strong performance, and strong performance led to greater subsequent trust. Conclusion. Our findings raise new questions about the utility of trust in the context of interacting with AIDEs and generative AI. We call for further research into the effect of trust on compliance to recommendations from imperfect AI. Application. This work can inform the design of training and educational content for generative AI use within and beyond software development. Instructional designers should consider risks of AI misuse and disuse and focus on promoting desirable interaction outcomes, regardless of trust's connection to them.

Education is not merely the transmission of information or the optimisation of individual performance; it is a fundamentally social, constructive, and relational practice. However, recent advances in generative artificial intelligence (GenAI) increasingly emphasise efficiency, automation, and individualised assistance, risking the weakening of relational learning processes. Despite growing adoption, AI in education (AIED) research has yet to fully articulate how AI can be designed in ways that sustain the social and ecological relationships through which learning occurs. In this paper, we re-centre education as relational and frame learner-AI interactions as context-specific relationships with clearly defined purposes and boundaries, rather than positioning them as substitutes for, or replacements of, human interaction. Grounded in participatory design practices and inspired by Indigenous worldviews (including Aboriginal Australian, Native American, and Mesoamerican traditions) that foreground reciprocity and relational accountability, we argue that meaningful educational AI should support learning with others rather than replace them. We advance this perspective by: i) conceptualising AIED as a relational design problem grounded in reciprocity; ii) articulating key tensions introduced by GenAI in education; and iii) outlining design directions that expand the AIED design space toward reciprocity, including when not to use AI, how to define pedagogical boundaries, and how to support responsible uses of AIED innovations that sustain communities and natural environments.

Generative AI systems have entered everyday academic, professional, and personal life with remarkable speed, yet most users encounter them as mysterious artifacts rather than intelligible systems. This chapter discusses large language models within a broader historical shift in computing paradigms and argues that many of the confusions surrounding their use arise from a mismatch between how these systems are built, how they behave, and how people expect computers to behave writ large. Rather than treating generative AI as a monolithic technology, the chapter decomposes it into interacting components, spanning data, models, product features, and user inputs, each introducing distinct affordances and tensions. Particular attention is given to the statistical and data-based foundations of these systems and to the fact that their surface behavior is explicitly human-like, a combination that places them squarely within the intellectual traditions of educational and behavioral research. From this perspective, educational researchers are unusually well positioned to study, evaluate, and productively use generative AI systems, drawing on established methods for modeling latent processes, managing uncertainty, and interpreting complex human-system interactions. The goal is to equip readers with a conceptual map that supports more informed experimentation, critical interpretation, and responsible use as these systems continue to evolve.

Generative AI is changing how research software is developed, but rapid AI-assisted development can weaken continuity, traceability, and methodological clarity. SHAPR (Solo, Human-centred, AI-assisted PRactice) was proposed as a framework for structuring AI-assisted research software development. This paper presents a documented case of applying SHAPR to the development of a modular share trading system. From the outset, the project adopted a SHAPR-informed working configuration that shaped how interaction, implementation, and documentation were organised. Across iterative development cycles, the project generated a structured evidence base including reflection notes, development cycle review notes, source-of-truth documents, contracts, quick captures, workflow notes, and evolving code artefacts. The case showed that continuous documentation updates, supported by quick capture and AI-assisted refinement, helped maintain organised and usable project knowledge throughout development. Five recurring lessons were identified: contracts stabilised AI-assisted coding, a maintained source-of-truth layer improved coherence, cycle-boundary snapshots strengthened continuity, code and documentation co-evolved through quick capture and iterative refinement, and environment setup itself contributed to knowledge generation. The case also illustrates a practical SHAPR operating configuration in which a ChatGPT Project and cycle-specific chats supported interaction, reasoning, summarisation, and coding collaboration, PyCharm supported artefact implementation, and Obsidian supported external working memory, structured documentation, reflection, continuity, and repository-oriented note organisation, while remaining consistent with SHAPR's tool-agnostic principle. The paper contributes practical guidance and good practices for researchers conducting AI-assisted research software development.

The rapid integration of large language models (LLMs) into everyday workflows has transformed how individuals perform cognitive tasks such as writing, programming, analysis, and multilingual communication. While prior research has focused on model reliability, hallucination, and user trust calibration, less attention has been given to how LLM usage reshapes users'perceptions of their own capabilities. This paper introduces the LLM fallacy, a cognitive attribution error in which individuals misinterpret LLM-assisted outputs as evidence of their own independent competence, producing a systematic divergence between perceived and actual capability. We argue that the opacity, fluency, and low-friction interaction patterns of LLMs obscure the boundary between human and machine contribution, leading users to infer competence from outputs rather than from the processes that generate them. We situate the LLM fallacy within existing literature on automation bias, cognitive offloading, and human-AI collaboration, while distinguishing it as a form of attributional distortion specific to AI-mediated workflows. We propose a conceptual framework of its underlying mechanisms and a typology of manifestations across computational, linguistic, analytical, and creative domains. Finally, we examine implications for education, hiring, and AI literacy, and outline directions for empirical validation. We also provide a transparent account of human-AI collaborative methodology. This work establishes a foundation for understanding how generative AI systems not only augment cognitive performance but also reshape self-perception and perceived expertise.

This paper explores how Interactive Digital Narratives (IDNs) can support learners in developing the critical literacies needed to address complex societal challenges, so-called wicked problems, such as climate change, pandemics, and social inequality. While digital technologies offer broad access to narratives and data, they also contribute to misinformation and the oversimplification of interconnected issues. IDNs enable learners to navigate nonlinear, interactive stories, fostering deeper understanding and engagement. We introduce Systemic Learning IDNs: interactive narrative experiences explicitly designed to help learners explore and reflect on complex systems and interdependencies. To guide their creation and use, we propose the CLASS framework, a structured model that integrates systems thinking, design thinking, and storytelling. This transdisciplinary approach supports learners in developing curiosity, critical thinking, and collaborative problem-solving. Focusing on the classroom context, we apply CLASS to two cases, one commercial narrative simulation and one educational prototype, offering a comparative analysis and practical recommendations for future design and implementation. By combining narrative, systems mapping, and participatory design, this paper highlights how IDNs can become powerful tools for transformative, systems-oriented learning in an increasingly complex world.

Current generative AI models like ChatGPT, Claude, and Gemini are widely used for knowledge dissemination, task decomposition, and creative thinking. However, their linear interaction methods often force users to repeatedly compare and copy contextual information when handling complex tasks, increasing cognitive load and operational costs. Moreover, the ambiguity in model responses requires users to refine and simplify the information further. To address these issues, we developed"Mindalogue", a system using a non-linear interaction model based on"nodes + canvas"to enhance user efficiency and freedom while generating structured responses. A formative study with 11 users informed the design of Mindalogue, which was then evaluated through a study with 16 participants. The results showed that Mindalogue significantly reduced task steps and improved users' comprehension of complex information. This study highlights the potential of non-linear interaction in improving AI tool efficiency and user experience in the HCI field.

The rapid integration of generative AI into academic workflows demands curricula that equip students not only with tool proficiency but with the critical judgment to use those tools responsibly in scholarly work. Existing offerings cluster around two inadequate poles: technical AI development courses serving narrow specialist audiences, and brief general-literacy interventions that cannot develop the sustained, practice-based competencies rigorous research requires. This paper reports the design, theoretical rationale, and implementation of BSTA 495/395: Getting Started with AI-Assisted Research, developed and delivered at Lehigh University (Spring 2026). The course addresses an underserved gap: the competencies required for rigorous AI-assisted literature review. Its architecture organizes instruction into four sequential modules aligned with the cognitive demands of that task: comprehension of individual papers, construction and validation of knowledge taxonomies, identification of research gaps, and synthesis and production of complete literature reviews. Each module embeds an explicit verification discipline and standardized AI attribution practice. Prerequisite-free and discipline-agnostic, the course enrolls upper-level undergraduates and graduate students across all fields with differentiated assessment expectations. Pre- and post-course survey data from the inaugural offering indicate substantial self-reported confidence gains, with the largest in hallucination detection (d = +1.45), responsible AI use (d = +1.33), and AI attribution practice (d = +2.40), consistent with the course's design emphasis. The course constitutes a replicable model for the emerging genre of AI research literacy curricula.

Real-world Capstone Projects (RWCPs) are a key component of software engineering education, enabling students to develop software for external clients under authentic conditions. Their high ecological validity, combined with substantial variation in domains, technologies, and stakeholders, typically requires flexible and minimally prescriptive teaching approaches. The rapid integration of generative AI (GenAI) into professional software development adds new challenges: students are expected to use AI tools that are common in practice, yet unguided use may affect learning, collaboration, and consistency in ways that are not yet well understood. To establish an empirical baseline for responsible GenAI integration, we conducted a large-scale study of self-determined GenAI use in an undergraduate RWCP course. The module involved 178 students working in 18 teams across 15 client projects over four months, with GenAI use explicitly permitted. We collected mixed-method survey data from 150 students on attitudes, usage prevalence, workflows, use cases, and perceived benefits and risks, and surveyed client stakeholders regarding expectations and concerns. Our findings provide (1) a characterization of GenAI practices across the software engineering lifecycle, including a distinction between emerging workflows; (2) student-recommended use cases and responsible-use directives emphasizing verification and maintaining independent understanding; (3) client perspectives highlighting strong support for GenAI use but clear expectations regarding understanding, quality, and data protection; and (4) implications for future course iterations, including the need for explicit responsible-use guidelines, targeted AI literacy resources, and team-level governance roles. This study offers a status quo baseline for evidence-based pedagogical interventions in the era of GenAI.

Student engagement with large language models (LLMs) in academic writing is not a stable trait, an adoption decision, or a competency level; it is a continuously negotiated process that existing frameworks cannot adequately theorize. Typological models provide categories without mechanisms; technology acceptance models explain adoption but not post-adoption quality; AI literacy frameworks treat competency as a static predictor rather than a live input. None accounts for within-student variability across tasks, the developmental paradox whereby experience produces habituation rather than sophistication, or principled non-use as a form of ethical reasoning. This article introduces the Reliance Negotiation Framework (RNF), developed from a sequential explanatory mixed-methods study of 382 undergraduates at a public minority-serving institution in the United States (survey, N = 382; 14 semi-structured interviews; three qualitative survey strands; 1,435 coded instances). The RNF reconceptualizes LLM reliance as an ongoing negotiation among four concurrent inputs (perceived benefits, perceived risks, ethical commitments, and situational demands) with outputs that recursively modify subsequent decisions. A Two-Model Architecture accommodates the 13.0% of participants whose categorical ethical commitments foreclose negotiation entirely. The framework generates four falsifiable predictions with implications for AI literacy pedagogy, academic integrity policy, and equity-centered practice at minority-serving institutions.

Despite AI tools becoming more prevalent and applicable to a variety of workplaces, workers consistently report uncertainty about where AI applies, what problems it can help solve, and how it fits into real workflows. In other words, there is a gap between `knowing'and `doing'when it comes to AI literacy. We propose an experiential form of AI literacy which integrates participant's daily experiences into the learning experience by brainstorming grounded AI use cases through storytelling. We introduce a novel pedagogical approach that helps individuals move away from abstract notions of AI towards practical knowledge of how AI would (or would not) work in different workflows, contexts, and situations. Through this approach, we anticipate two major outcomes: (1) enhanced AI literacy for stakeholders within a variety of work sectors and (2) concrete AI use cases developed through participatory design that are grounded in AI literacy and participant's expertise.

The rapid proliferation of Generative Artificial Intelligence (GenAI) is reshaping pedagogical practices and assessment models in higher education. While institutional and educator perspectives on GenAI integration are increasingly documented, the student perspective remains comparatively underexplored. This study examines how students perceive, use, and evaluate GenAI within their academic practices, focusing on usage patterns, perceived benefits, and expectations for institutional support. Data were collected through a questionnaire administered to 436 postgraduate Computer Science students at the University of Hertfordshire and analysed using descriptive methods. The findings reveal a Confidence-Competence Paradox: although more than 60% of students report high familiarity with tools such as ChatGPT, daily academic use remains limited and confidence in effective application is only moderate. Students primarily employ GenAI for cognitive scaffolding tasks, including concept clarification and brainstorming, rather than fully automated content generation. At the same time, respondents express concerns regarding data privacy, reliability of AI-generated information, and the potential erosion of critical thinking skills. The results also indicate strong student support for integrating AI literacy into curricula and programme Knowledge, Skills, and Behaviours (KSBs). Overall, the study suggests that universities should move beyond a policing approach to GenAI and adopt a pedagogical framework that emphasises AI literacy, ethical guidance, and equitable access to AI tools.

Most AI literacy courses for non-technical undergraduates emphasize conceptual breadth over technical depth. This paper describes UNIV 182, a prerequisite-free course at George Mason University that teaches undergraduates across majors to understand, use, evaluate, and build AI systems. The course is organized around five mechanisms: (1) a unifying conceptual pipeline (problem definition, data, model selection, evaluation, reflection) traversed repeatedly at increasing sophistication; (2) concurrent integration of ethical reasoning with the technical progression; (3) AI Studios, structured in-class work sessions with documentation protocols and real-time critique; (4) a cumulative assessment portfolio in which each assignment builds competencies required by the next, culminating in a co-authored field experiment on chatbot reasoning and a final project in which teams build AI-enabled artifacts and defend them before external evaluators; and (5) a custom AI agent providing structured reinforcement outside class. The paper situates this design within a comparative taxonomy of cross-major AI literacy courses and pedagogical traditions. Instructor-coded analysis of student artifacts at four assessment stages documents a progression from descriptive, intuition-based reasoning to technically grounded design with integrated safeguards, reaching the Create level of Bloom's revised taxonomy. To support adoption, the paper identifies which mechanisms are separable, which require institutional infrastructure, and how the design adapts to settings ranging from general AI literacy to discipline-embedded offerings. The course is offered as a documented resource, demonstrating that technical depth and broad accessibility can coexist when scaffolding supports both.

This paper introduces `Math Battles with AI', an innovative competitive format designed at ITMO University to redefine the role of generative AI in mathematics education. Moving away from a purely defensive stance, the authors propose an AI agent with intentionally increased hallucination likelihood in specific modes to train verification skills. We describe the three-stage tournament structure and a specialized assessment system that rewards critical verification over blind reliance. Initial results indicate a significant shift in student mindsets, fostering essential skills in digital hygiene and prompt engineering. This work serves as a practical guide for academic institutions aiming to leverage AI for enhancing, rather than undermining, intellectual development.

Recent empirical research by Sharma et al. (2026) demonstrated that AI assistant interactions carry meaningful potential for situational human disempowerment, including reality distortion, value judgment distortion, and action distortion. While this work provides a critical diagnosis of the problem, concrete pedagogical interventions remain underexplored. I present an AI literacy framework built around eight cross-cutting Learning Outcomes (LOs), developed independently through teaching practice and subsequently found to align with Sharma et al.'s disempowerment taxonomy. I report a case study from a publicly available online course, where a co-teaching methodology--with AI serving as an active voice co-instructor--was used to deliver this framework. Drawing on inoculation theory (McGuire, 1961)--a well-established persuasion research framework recently applied to misinformation prebunking by the Cambridge school (van der Linden, 2022; Roozenbeek&van der Linden, 2019)--I argue that AI literacy cannot be acquired through declarative knowledge alone, but requires guided exposure to AI failure modes, including the sycophantic validation and authority projection patterns identified by Sharma et al. This application of inoculation theory to AI-specific distortion is, to my knowledge, novel. I discuss the convergence between the pedagogically-derived framework and Sharma et al.'s empirically-derived taxonomy, and argue that this convergence--two independent approaches arriving at similar problem descriptions--strengthens the case for both the diagnosis and the proposed educational response.

How do students develop AI literacy through everyday practice rather than formal instruction? While normative AI literacy frameworks proliferate, empirical understanding of how students actually learn to work with generative AI remains limited. This study analyzes 10,536 ChatGPT messages from 36 undergraduates over one academic year, revealing five use genres -- academic workhorse, emotional companion, metacognitive partner, repair and negotiation, and trust calibration -- that constitute distinct configurations of student-AI learning. Drawing on domestication theory and emerging frameworks for AI literacy, we demonstrate that functional AI competence emerges through ongoing relational negotiation rather than one-time adoption. Students develop sophisticated genre portfolios, strategically matching interaction patterns to learning needs while exercising critical judgment about AI limitations. Notably, repair work during AI breakdowns produces substantial learning about AI capabilities, developing what we term"repair literacy"-- a crucial but underexplored dimension of AI competence. Our findings offer educators empirically grounded insights into how students actually learn to work with generative AI, with implications for AI literacy pedagogy, responsible AI integration, and the design of AI-enabled learning environments that support student agency.

Generative artificial intelligence (GenAI) tools are increasingly adopted in education, yet many educators lack structured guidance on responsible and context sensitive prompt engineering, particularly in African and other resource constrained settings. This case report documents a three day online professional development programme organised by Generative AI for Education and Research in Africa (GenAI-ERA), designed to strengthen educators and researchers capacity to apply prompt engineering ethically for academic writing, teaching, and research. The programme engaged 468 participants across multiple African countries, including university educators, postgraduate students, and researchers. The training followed a scaffolded progression from foundational prompt design to applied and ethical strategies, including persona guided interactions. Data sources comprised registration surveys, webinar interaction records, facilitator observations, and session transcripts, analysed using descriptive statistics and computationally supported qualitative techniques. Findings indicate that participants increasingly conceptualised prompt engineering as a form of AI literacy requiring ethical awareness, contextual sensitivity, and pedagogical judgement rather than technical skill alone. The case highlights persistent challenges related to access, locally relevant training materials, and institutional support. The report recommends sustained professional development and the integration of prompt literacy into curricula to support responsible GenAI use in African education systems.

Global frameworks increasingly advocate for Responsible Artificial Intelligence (AI) in education, yet they provide limited guidance on how ethical, culturally responsive, and curriculum-aligned AI can be operationalized within functioning teacher education systems, particularly in the Global South. This study addresses this gap through the design and evaluation of GenAITEd Ghana, a context-aware, region-specific conversational AI prototype developed to support teacher education in Ghana. Guided by a Design Science Research approach, the system was developed as a school-mimetic digital infrastructure aligned with the organizational logic of Ghanaian Colleges of Education and the National Council for Curriculum and Assessment (NaCCA) framework. GenAITEd Ghana operates as a multi-agent, retrieval-augmented conversational AI that coordinates multiple models for curriculum-grounded dialogue, automatic speech recognition, voice synthesis, and multimedia interaction. Two complementary prompt pathways were embedded: system-level prompts that enforce curriculum boundaries, ethical constraints, and teacher-in-the-loop oversight, and interaction-level semi-automated prompts that structure live pedagogical dialogue through clarification, confirmation, and guided response generation. Evaluation findings show that the system effectively enacted key Responsible AI principles, including transparency, accountability, cultural responsiveness, privacy, and human oversight. Human expert evaluations further indicated that GenAITEd Ghana is pedagogically appropriate for Ghanaian teacher education, promoting student engagement while preserving educators'professional authority. Identified challenges highlight the need for continued model integration, professional development, and critical AI literacy to mitigate risks of over-reliance.

Claims about whether large language model (LLM) chatbots"reason"are typically debated using curated benchmarks and laboratory-style evaluation protocols. This paper offers a complementary perspective: a student-led field experiment embedded as a midterm project in UNIV 182 (AI4All) at George Mason University, a Mason Core course designed for undergraduates across disciplines with no expected prior STEM exposure. Student teams designed their own reasoning tasks, ran them on widely used consumer chatbots representative of current capabilities, and evaluated both (i) answer correctness and (ii) the validity of the chatbot's stated reasoning (for example, cases where an answer is correct but the explanation is not, or vice versa). Across eight teams that reported standardized scores, students contributed 80 original reasoning prompts spanning six categories: pattern completion, transformation rules, spatial/visual reasoning, quantitative reasoning, relational/logic reasoning, and analogical reasoning. These prompts yielded 320 model responses plus follow-up explanations. Aggregating team-level results, OpenAI GPT-5 and Claude 4.5 achieved the highest mean answer accuracy (86.2% and 83.8%), followed by Grok 4 (82.5%) and Perplexity (73.1%); explanation validity showed a similar ordering (81.2%, 80.0%, 77.5%, 66.2%). Qualitatively, teams converged on a consistent error signature: strong performance on short, structured math and pattern items but reduced reliability on spatial/visual reasoning and multi-step transformations, with frequent"sound right but reason wrong"explanations. The assignment's primary contribution is pedagogical: it operationalizes AI literacy as experimental practice (prompt design, measurement, rater disagreement, and interpretability/grounding) while producing a reusable, student-generated corpus of reasoning probes grounded in authentic end-user interaction.

The rapid assimilation of Artificial Intelligence technologies into various facets of society has created a significant educational imperative that current frameworks are failing to effectively address. We are witnessing the rise of a dangerous literacy gap, where a focus on the functional, operational skills of using AI tools is eclipsing the development of critical and ethical reasoning about them. This position paper argues for a systemic shift toward comprehensive AI literacy that centers human agency - the empowered capacity for intentional, critical, and responsible choice. This principle applies to all stakeholders in the educational ecosystem: it is the student's agency to question, create with, or consciously decide not to use AI based on the task; it is the teacher's agency to design learning experiences that align with instructional values, rather than ceding pedagogical control to a tool. True literacy involves teaching about agency itself, framing technology not as an inevitability to be adopted, but as a choice to be made. This requires a deep commitment to critical thinking and a robust understanding of epistemology. Through the AI Literacy, Fluency, and Competency frameworks described in this paper, educators and students will become agents in their own human-centric approaches to AI, providing necessary pathways to clearly articulate the intentions informing decisions and attitudes toward AI and the impact of these decisions on academic work, career, and society.

Major AI ethics guidelines and laws, including the EU AI Act, call for effective human oversight, but do not define it as a distinct and developable capacity. This paper introduces human oversight as a well-being capacity, situated within the emerging Well-being Efficacy framework. The concept integrates AI literacy, ethical discernment, and awareness of human needs, acknowledging that some needs may be conflicting or harmful. Because people inevitably project desires, fears, and interests into AI systems, oversight requires the competence to examine and, when necessary, restrain problematic demands. The authors argue that the sustainable and cost-effective development of this capacity depends on its integration into education at every level, from professional training to lifelong learning. The frame of human oversight as a well-being capacity provides a practical path from high-level regulatory goals to the continuous cultivation of human agency and responsibility essential for safe and ethical AI. The paper establishes a theoretical foundation for future research on the pedagogical implementation and empirical validation of well-being effectiveness in multiple contexts.

This paper offers a phenomenological reading of contemporary machine learning through Heideggerian concepts, aimed at enriching practitioners'reflexive understanding of their own practice. We argue that this philosophical lens reveals three insights invisible to purely technical analysis. First, the algorithmic Entwurf (projection) is distinctive in being automated, opaque, and emergent--a metaphysics that operates without explicit articulation or debate, crystallizing implicitly through gradient descent rather than theoretical argument. Second, even sophisticated technical advances remain within the regime of Gestell (Enframing), improving calculation without questioning the primacy of calculation itself. Third, AI's lack of existential structure, specifically the absence of Care (Sorge), is genuinely explanatory: it illuminates why AI systems have no internal resources for questioning their own optimization imperatives, and why they optimize without the anxiety (Angst) that signals, in human agents, the friction between calculative absorption and authentic existence. We conclude by exploring the pedagogical value of this perspective, arguing that data science education should cultivate not only technical competence but ontological literacy--the capacity to recognize what worldviews our tools enact and when calculation itself may be the wrong mode of engagement.

In recent years, there has been a change in the objectives of Higher Education (HE): the inclusion of employability. The successful inclusion of employability as a goal of HE requires a change to the sector’s teaching, learning and assessment (TLA) methods, which ought to be part of an HEI’s strategy. In particular, there needs to be an emphasis on adjustments to both curriculum design and modes of assessment, both for the inclusion of employability and also in preparation for digital transformation (Education 4.0) as well as internationalisation. Any HEI development plan should acknowledge the importance of employability. To meet national standards and keep up with evolving trends in the job market, every programme needs to equip students with the necessary skills, knowledge, and experience to succeed in their future careers. Every institution is obligated to provide opportunities for students from all backgrounds and capabilities to learn about work.

This paper presents a systematic literature review of the impact of emerging technologies such as Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and gamification on student engagement, learning outcomes, and employability in Built Environment (BE) education. This review covers studies conducted between 2013 and 2023, utilizing the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) framework. From an initial pool of 626 studies, 61 were identified and rigorously analyzed. The findings reveal that these technologies significantly enhance student engagement by providing immersive and interactive learning experiences that bridge the gap between theoretical knowledge and practical application. Furthermore, their use is shown to improve learning outcomes by facilitating a deeper understanding of complex concepts and increasing student motivation. In terms of employability, the integration of digital tools into BE education equips students with the requisite skills that are increasingly demanded in the modern workplace. However, the study also identifies several challenges, including high costs, limited resources, and the need for extensive faculty training, which act as barriers to the effective implementation of these technologies. Despite these challenges, this review underscores the transformative potential of digital technologies in BE education. This study is significant as it synthesizes recent evidence to highlight the critical role of digital technologies in reshaping BE education. It offers practical recommendations for educators and policymakers to enhance teaching and learning practices. Providing pathways for integrating these technologies into BE curricula, this study aims to inform future research and pedagogical strategies, ultimately contributing to the development of a highly skilled and adaptable workforce.

The emergence of Artificial Intelligence (AI) in recent years has brought about substantial changes in several industries, including the End-of-Life Vehicle (ELV) sector by optimizing processes such as vehicle dismantling, material classification, and recycling. As artificial intelligence (AI) progresses, it is crucial for Technical and Vocational Education and Training (TVET) institutions to ensure that its graduates possess the essential AI skills in order to improve their chances of finding employment. However, the integration of AI into TVET programs and its impact on graduate employability remains underexplored, especially within the ELV sector. The purpose of this paper is to examine and evaluate the crucial factors that enhance the job prospects of TVET graduates in the ELV industry by incorporating AI technology. Through the application of the Analytical Hierarchy Process (AHP), a thorough examination was carried out on 27 factors that were classified into different categories. These categories include Curriculum Integration, Industry-driven, AI Applications, Ethics & Sustainability and AI Management. The study prioritizes the top factors including AI integration into curricula, hands-on training with AI tools, industry collaboration, AI-driven predictive maintenance, and process optimization. The findings offer practical insights for TVET institutions and industry stakeholders to align educational strategies related to AI with industry demands. This will ultimately improve the job prospects of graduates in the ever-changing ELV sector.

… With the wide application of generative AI, the ability to interact with AI efficiently and effectively has become one of the most important media literacies. Hence, it is imperative for …

Propelled by their remarkable capabilities to generate novel and engaging content, Generative Artificial Intelligence (GenAI) technologies are disrupting traditional workflows in many industries. While prior research has examined GenAI from a techno-centric perspective, there is still a lack of understanding about how users perceive and utilize GenAI in real-world scenarios. To bridge this gap, we conducted semi-structured interviews with (N = 18) GenAI users in creative industries, investigating the human-GenAI co-creation process within a holistic LUA (Learning, Using and Assessing) framework. Our study uncovered an intriguingly complex landscape: Prospects – GenAI greatly fosters the co-creation between human expertise and GenAI capabilities, profoundly transforming creative workflows; Challenges – Meanwhile, users face substantial uncertainties and complexities arising from resource availability, tool usability, and regulatory compliance; Strategies – In response, users actively devise various strategies to overcome many of such challenges. Our study reveals key implications for the design of future GenAI tools.

Generative AI technologies are reshaping multiple sectors by enhancing how tasks are executed, and information is processed. With advancements from companies like OpenAI, Microsoft, and Google, these AI agents have become pivotal in automating workflows, generating text that resembles human writing, and offering actionable insights. Central to harnessing their potential is mastering prompt engineering-the art of designing effective queries to optimize AI responses. This article delves into the burgeoning field of generative AI, emphasizing the significance of prompt engineering in enhancing the interaction with these sophisticated tools. It provides an overview of leading AI tools like ChatGPT, Microsoft Copilot, and Google Bard, explores the technical underpinnings of these models, and addresses key aspects of prompt engineering including linguistic nuances, iterative refinement, and bias mitigation. The article also examines practical strategies for mastering prompt engineering, discusses security concerns such as prompt injection attacks and data privacy, and highlights future trends in AI technology. Through case studies and expert insights, the article underscores the critical role of prompt engineering in maximizing the effectiveness and ethical use of generative AI systems.

This paper explores the effects of integrating Generative Artificial Intelligence (GAI) into decision-making processes within organizations, employing a quasi-experimental pretest-…

As artificial intelligence becomes an everyday presence across education, arts and creative technologies, and cultural heritage, the interaction between users and intelligent systems deserves critical examination. This submission presents a systematic review of 95 case studies, 64 in education, 14 in arts, and 17 in heritage — selected via a PRISMA-guided search and expert screening — to map how generative artificial intelligence is embedded at both the interface and interaction levels. We identify nine interface archetypes (e.g., conversational, adaptive dashboards, immersive environments interfaces), eight interaction patterns (e.g., conversing, collaborating, manipulating),and eight main user experience dimensions as observed in case studies. Our analysis further categorizes six modality-usage patterns—from text, image, audio, and video up to fully multi-modal workflows and distillsfour main categories of end-to-end application pipelines. Notably, only two studies were found to articulate design-phase guidelines, and limitations cluster around output quality, ethical risks, and a lack of longitudinal evaluations. We conclude with limitations observed and future research focused on explainability, participatory design, and sustained field deployments. This synthesis provides a foundation for researchers and practitioners seeking to harness generative artificial intelligence as a responsive, human-centered collaborator.

Considering both the transformative opportunities and challenges presented by generative AI (GenAI) in academic writing, effectively integrating GenAI into the academic setting becomes a significant need requiring prioritization. Yet, there is limited understanding regarding the nature of interactions between different types of students, what behavioral patterns students exhibit during a student-GenAI interaction (SAI) on a given task, and how these different SAI patterns relate to the actual writing task performance. This study, therefore, aimed to identify SAI patterns of academic writing tasks depending on students’ level of AI literacy and examine the differences in academic writing performance between the identified SAI patterns. Drawing from the combination of three data sources, including think-aloud protocols, screen-recordings, and chat histories between 36 Chinese graduate students and a GenAI writing system, epistemic network analysis (ENA) was used to reveal the distinctive SAI patterns of students with different levels of AI literacy. The study found that students with a high level of AI literacy exhibited a collaborative approach to SAI, actively accepting GenAI’s suggestions and engaging GenAI in meta-cognitive-related activities such as planning, whereas students with a low level of AI literacy demonstrated much less interaction with GenAI in completing their writing tasks, instead choosing to ideate and evaluate independently. In addition, the Wilcoxon rank-sum (Mann-Whitney U) test was conducted to assess the writing task performance of the two AI literacy groups. Findings revealed statistical differences in all evaluation rubrics (content, structure/organization, expression). This study offers implications for the design and implementation of GenAI agents in writing tasks and the pedagogy of GenAI-assisted instruction.

The democratization of powerful artificial intelligence (AI) tools, including ChatGPT, has sparked the interest of business practitioners given their ability to fundamentally change the way we work. While AI tools are positioned to augment human capabilities, their effective implementation requires the skill to understand where, when and how to best utilize them efficiently. Furthermore, meaningful engagement with the content produced by generative AI (GenAI) necessitates the intricacy of appropriate prompt engineering to optimize the learning process. As the field of GenAI continues to advance, the art of developing impactful prompts has become a necessary skill for harnessing its full potential. This research develops an AI prompting protocol through a constructivist theory lens. Based on the principles of constructivism, where individuals assimilate new knowledge by bridging it with their existing understanding, this research suggests an active engagement process in the human-AI co-construction of knowledge through GenAI. The goal is to empower business managers and their teams to construct effective AI prompts and validate responses, thereby enhancing user interaction, optimizing workflows, and maximizing the potential outcomes of AI chatbots.

Purpose Current knowledge and research on students’ utilization and interaction with generative artificial intelligence (AI) tools in their academic work is limited. This study aims to investigate students’ engagement with these tools. Design/methodology/approach This research used survey-based research to investigate generative AI literacy (utilization, interaction, evaluation of output and ethics) among students enrolled in a four-year public university in the southeastern USA. This article focuses on the respondents who have used generative AI (218; 47.2%). Findings Most respondents used generative AI to generate ideas for papers, projects or assignments, and they also used AI to assist with their original ideas. Despite their use of AI assistance, most students were critical of generative AI output, and this mindset was reflected in their reported interactions with ChatGPT. Respondents expressed a need for explicit guidance from course syllabi and university policies regarding generative AI’s ethical and appropriate use. Originality/value Literature related to generative AI use in higher education specific to ChatGPT is predominantly from educators’ viewpoints. This study provides empirical evidence about how university students report using generative AI in the context of generative AI literacy.

ABSTRACT Second language teaching approaches that were originally proposed for classroom context are used for blended learning context as well as distance learning contexts regardless of their fundamental differences in terms of applicability, accessibility, objectives, designs and theoretical bases. Such theoretical and practical incompatibilities served as the rationale to bridge the gap of a basically devised second language (L2) teaching approach for blended and distance learning contexts. The proposed computer-assisted nonlinear dynamic approach (CANDA) enables L2 learners and teachers to improve psychological factors and communicative skills by suggesting the use of a variety of technology-based apps for a multitude of learning styles. The CANDA, as an L2 teaching approach has significant theoretical and pedagogical implications for educational app designers, computer-assisted teaching programmers, blended and distance learning researchers and officials.

… nonlinear and contextual manifestations of high school science teachersL professional learning from a … Increasingly, science teacher PD involves field based learning experiences and …

… knowledge are capable of employing principles of NP and that this can lead to greater learning outcomes than coaches with similar expertise and experience of teaching an LP method. …

The integration of generative artificial intelligence (AI) tools like ChatGPT in education has raised concerns that students may become dependent on AI-generated solutions, potentially stifling the development of critical thinking skills. Compounding this issue is the fact that Bloom’s Taxonomy—the widely used framework for designing educational goals—fails to address the cognitive demands of AI-assisted learning. This exploratory study presents a revised framework that incorporates AI-specific competencies, offering a more relevant model for nurturing critical thinking in an AI-driven environment. Using a conceptual approach supported by empirical evidence from MSc Marketing students’ interactions with AI tools over 4 weeks, the study found that AI can both enhance and challenge critical thinking across cognitive, affective, and metacognitive domains. Key elements such as melioration, ethical reasoning, collaboration, and reflective thinking were identified as critical for developing deeper engagement with AI-generated content. The framework proposes 12 propositions that inform future research and pedagogical strategies. This study outlines a research agenda for examining AI’s impact on cognitive development, serving as a resource for educators, policymakers, and researchers seeking to adapt teaching methods for AI-assisted education.

Blended learning is widely known for its ability to improve learning, nevertheless little is still known about the best ways of designing effective blended learning environment which can support immersive learning such as greater learning experience and accessibility to education. In this respect, this study investigates the mapping of the principles of three Education 4.0 innovative pedagogies, namely, heutagogy, peeragogy, and cybergogy, with the capabilities of three technological learning tools, that is, Facebook (FB), Learning Management System (LMS), and Blog, via a systematic literature review technique. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used as the methodology, and the literature was further selected using Gough’s Weight of Evidence criteria, resulting in 59 studies. The results show that cognitive factor is the most linked pedagogical principle to the four main capabilities of technological learning tools, that is, time, self-related, learning task, and learning community-related. This mapping is useful for instructors to plan learning and teaching by choosing the technological learning tools that match with appropriate Education 4.0 pedagogies for optimising the immersive blended learning practices.

The latest mutation of Artificial Intelligence, Generative AI, is more than anything a technology of writing. It is a machine that can write. In a world‐historical frame, the significance of this cannot be understated. This is a technology in which the unnatural language of code tangles with the natural language of everyday life. Its form of writing, moreover, is multimodal, able not only to write text as conventionally understood, but also to “read” images by matching textual labels and to “write” images from textual prompts. Within the scope of this peculiarly mechanical manufacturing of writing are mathematics, actionable software procedure, and algorithm. This paper explores the consequences of Generative AI for literacy teaching and learning. In its first part, we speak theoretically and historically, suggesting that this development is perhaps as momentous for society and education as Pi Sheng's invention of moveable type and Gutenberg's printing press—and in its peculiar ways just as problematic. In the paper's second part, we go on to propose that literacy in the time of AI requires a new way to speak about itself, a revised “grammar” of sorts. In a third part, we discuss an experimental application we have developed that puts Generative AI to work in support of literacy and learning. We end with some findings and implications for literacy education and with a proposal for what we will call cyber‐social literacy learning.

… the newly proposed HumanCentric AI Pedagogy (HCAP) … : AI-Technological, AI-Content, AI-Pedagogical, Human-AI … using AI to strategically orchestrating human-AI collaborative …

… AI too much and losing the human element in teaching. The participants also highlighted significant challenges in developing AI literacy, … need for comprehensive AI literacy curricula in …

… problem-solving and AI integration strategies within various pedagogical contexts. Analysis … teachers’ AI literacy, particularly in the domain of knowing and understanding AI, suggesting …

The role of artificial intelligence (AI) in education plays a crucial role in teacher training digitalisation. Although AI has enormous potential, not much is known about how pre‐service teachers perceive and utilise AI tools in professional practice. Hence, this study, guided by the Unified Theory of Acceptance and Use of Technology framework, investigates pre‐service English as a foreign language teachers’ experiences using MagicSchool, an AI‐based educational tool, to design exam questions, aiming to explore how AI tools can enhance assessment practices in teacher education. Participants were 27 fourth‐year pre‐service teachers. Data for this case study were collected through semi‐structured interviews and reflective reports and subsequently subjected to thematic analysis. The findings reveal that MagicSchool improved time efficiency and reinforced the creation of various question types. Participants also mentioned its practicality in generating rubrics and materials for varied proficiency levels. However, challenges such as crafting effective prompts, verifying content and addressing cultural or contextual mismatches were recognised. Moreover, ethical concerns, such as plagiarism and minimised creativity, were highlighted, with participants warning against over‐reliance on AI. The study underscores the potential of AI in exam preparation while emphasising challenges, advocating for a balanced approach that integrates AI responsibly. Implications for teacher education include fostering AI literacy, promoting critical engagement with AI‐generated content and ensuring ethical and pedagogically sound implementation in assessment design.

This article highlights the significance of AI Literacy for promoting sustainable teacher education in an AI-driven world. Given the rapid progress of AI, a crucial aspect of organisational development for teacher education institutions involves fostering AI Literacy among teaching staff, and enabling them to use and teach AI ethically and responsibly. We underscore the necessity for teacher education institutions to create opportunities for developing AI Literacy as a fundamental goal for sustainable development. Further, we explore recommendations for sustainable organisational and professional development as well as future research directions.

… affordances provided by Generative Artificial Intelligence (GenAI) … of “digital plastic”, Critical AI Literacy (CAIL) must be part of a … this paper to foster Critical AI Literacy (CAIL) within the …

This study leverages the Stimulus‐Organism‐Response (S‐O‐R) framework to investigate the effects of teacher and technical support (TCHS) on learners' willingness to communicate (WTC) in artificial intelligence (AI)‐enhanced English as a foreign language (EFL) contexts, considering the mediating effects of learners' artificial intelligence literacy (AIL) and foreign language enjoyment (FLE). A quantitative survey encompassing 637 non‐English major university students across four institutions was conducted. Structural equation modelling (SEM) results demonstrated that teacher support (TEAS) exerts a direct influence on learners' WTC, whereas TCHS does not. The study also revealed that AIL and FLE significantly mediate the relationship between teacher and TCHS and learners’ WTC. The findings underscore the pivotal role of cognitive and affective factors, emphasising the substantial impact of TEAS and the value of nurturing learners’ AIL and enjoyment of foreign languages. This research offers strategic implications for educational practitioners and policymakers, advocating for the integration of innovative educational technologies and fostering sustainable growth in artificial intelligence in education.