人工智能赋能教学设计新范式

This study focuses on the potential of generative artificial intelligence tools in education, particularly through the practical application of the 4PADAFE instructional design matrix. The objective was to evaluate how these tools, in combination with the matrix, can enhance education and improve the teaching–learning process. Through surveys conducted with teachers from the University of ESPE Armed Forces who participated in the MOOC course “Generative Artificial Intelligence Tools for Education: GPT Chat Techniques”, the study explores the impact of these tools on education. The findings reveal that generative artificial intelligence tools are crucial in developing massive MOOC virtual classrooms when integrated with an instructional design matrix. The results demonstrate the potential of generative artificial intelligence tools in university education. By utilizing these tools in conjunction with an instructional design matrix, educators can design and deliver personalized and enriching educational experiences. The devices offer opportunities to enhance the teaching–learning process and tailor educational materials to individual needs, ultimately preparing students for the demands of the 21st century. The study concludes that generative artificial intelligence tools have significant potential in education. They provide innovative ways to engage students, adapt content, and promote personalized learning. Implementing the 4PADAFE instructional design matrix further enhances the effectiveness and coherence of educational activities. By embracing these technological advancements, education can stay relevant and effectively meet the digital world’s challenges.

… an instructional design problem related to GenAI. Much so-called research being produced in the fields of instructional design and … So, as instructional designers, we must determine the …

Recently, generative AI has been at the center of disruptive innovation in various settings, including educational sectors. This article investigates ChatGPT, which is one of the most prominent generative AI in the market, to explore its usefulness and potential for instructional design. Four researchers used a set of prompts to generate a course map for an online course that is aimed to teach the topic of makerspace and conducted SWOT analysis to identify strengths, weaknesses, opportunities, and threats of using generative AI for instructional design. The findings suggest that there is promise in using ChatGPT as an efficient and effective tool for creating course maps, yet it still requires the domain knowledge and instructional design expertise to warrant quality and reliability of the tool.

This study investigates how instructional designers incorporate generative AI into their workflows using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Using a descriptive research design approach with 144 survey participants and six focus group members, the research reveals three key findings: (1) emerging human-AI partnerships in instructional design workflows, (2) domain-specific challenges unique to instructional design, and (3) significant organizational influences on AI adoption patterns. The study found that while UTAUT’s construct of Performance Expectancy strongly drives adoption, instructional designers maintain a cautious outlook toward AI reliability. They also face barriers related to UTAUT’s constructs of Effort Expectancy (prompt engineering) and Facilitating Conditions (organizational support). These findings advance existing research by providing a theoretical understanding of how instructional designers develop complex integration patterns and navigate domain-specific issues when implementing generative AI tools.

The emergence of generative artificial intelligence (GenAI) has caused significant disruptions on a global scale in various workplace settings, including the field of instructional design (ID). Given the paucity of research investigating the impact of GenAI on ID work, we conducted a mixed methods study to understand instructional designers (IDs)’ perceptions and experiences of utilizing GenAI across a spectrum of ID tasks. A total of 70 IDs completed an online survey, and 13 of them participated in the semi-structured interviews. The survey results indicated IDs’ familiarity with and perceived usability of GenAI tools in performing various ID responsibilities in their specific contexts. Qualitative findings further explained that IDs often utilized GenAI tools in (1) brainstorming ideas, (2) handling low-stake tasks, (3) streamlining design process, and (4) enhancing collaborations. Participants also expressed their concerns and challenges while using GenAI in ID, including (1) quality concerns, (2) data security and privacy concerns, (3) concerns over authorship, ownership and plagiarism, amongst others. Implications and recommendations are also discussed to inform future ID practices and research.

Advances in generative artificial intelligence (AI) are transforming possibilities across industries, including instructional design. Tools like ChatGPT can draft objectives, assessments, and content rapidly. This mixed-methods study surveyed 144 instructional designers on current adoption, tasks, benefits, and concerns regarding generative AI integration. Analysis revealed widespread mainstream usage with 83% leveraging ChatGPT. Accelerating efficiency ranked as the top benefit, with 67% achieving moderate-to-significant time savings that allow more strategic work. Additional gains centered on accelerated content drafting, feedback, and ideation. However, key challenges included verifying accuracy, addressing ethical risks, formulating effective prompts, and lacking personalization. While meaningful automation freed up instructional designer capacity, truly customized innovation still requires human oversight. Guidelines must shape practical, responsible applications. Though comfort levels remain polarized and generative AI capabilities are immature, participants reported that generative AI brings notable workflow improvements. Though not a solution to all course development challenges, AI may help focus instructional design talent on more creative and complex design opportunities.

Integrating generative artificial intelligence (GenAI) into professional practice has become an important topic for professional instructional design practice and training. Accordingly, the purpose of this multiple-case study was to examine six professional instructional designers’ integration of GenAI into their professional practice and the factors affecting this integration. Research data were collected through semi-structured interviews conducted with professional instructional designers working in corporate or higher education settings. The results were as follows: (a) instructional designers mostly integrate GenAI into instructional design and/or development phases and they think that it also has the largest impact on these two phases; and (b) instructional designers’ integration of GenAI into their professional practice is mainly based on their ambivalent attitudes toward it, which is closely linked to the advantages and disadvantages associated with the technology. Specifically, instructional designers’ basic understanding of GenAI, the efficiency of generating instructional content through GenAI, the inaccuracy of GenAI-created products, instructional designers’ use of GenAI in everyday life, and institutional or company support shape their attitudes towards and integration of GenAI into their professional practice. All these findings suggest that instructional design and development phases are especially vulnerable to and can benefit from instructional designers’ attitudes and use of GenAI. Accordingly, it can be useful to address and enhance attitudes toward GenAI technology in instructional design training, which can promote instructional designers’ acceptance of the technology and effective use of it.

… Overall, this research concludes that generative AI presents novel approaches and opportunities within ID, offering significant benefits. It underscores the transformative impact of AI …

The purpose of this exploratory research study was to examine the roles instructional designers (IDs) in higher education play in the integration of generative Artificial Intelligence (GenAI) into their organizations, and how they use GenAI technologies in their own professional practices. Data were collected from 15 participants in the southeastern United States (U.S.) in an ID role or with similar job titles (e.g., educational technologist). Using a general qualitative approach, semi-structured interviews were conducted in Zoom about IDs’ use and integration of GenAI. Our findings resulted in three primary themes related to IDs’ integration of GenAI in online and blended education: (a) use of GenAI for instructional design; (b) collaborative guidance for faculty integration of GenAI; (c) training, resources, and guidelines on the integration of GenAI. A common thread through all the themes and interviews was IDs’ conscientious and cautious approach and ethical concerns about GenAI integration. We unpack these themes while discussing the implications of IDs in higher education integrating GenAI to meet organizational, faculty, and student needs. We provide limitations and a rich discussion about the advancement of GenAI by IDs working in institutions of higher education.

The autoethnographic study investigates the transformative impact of generative AI on educational research, instructional design, and teaching practices over a 5-month period (May–October 2024). By integrating AI tools into every phase of the research process, the study examines AI’s role as both a research partner and a subject of inquiry. Field notes, queries, and AI-generated outputs were systematically collected, creating a corpus for analysis. Grounded in activity theory, this research offers a reflective narrative on the evolving work routines of instructional designers and educators, emphasizing the orchestration of technology rather than prescriptive best practices. The study contributes to educational technology research by documenting the use of AI at a specific point in time, providing a foundation for future inquiry into the practical implications of AI in education.

This study investigates how integrating the 4PADAFE instructional design methodology with generative artificial intelligence (GAI) tools helps develop innovative, pedagogically sound digital learning environments in higher education. To meet the demand for scalable and flexible instructional models, 4PADAFE offers a seven-phase, iterative framework that connects pedagogical goals with the creative use of AI-powered tools. Using a qualitative exploratory approach, 20 Systems Engineering students applied the methodology to collaboratively create a four-week Massive Open Online Course (MOOC) titled “Generative Artificial Intelligence Tools for University Teaching.” They utilized ChatGPT, DALL·E, and Gamma to produce educational materials without direct input from subject-matter experts. Data collection included semi-structured interviews, non-participant observation, and analysis of student-created artifacts. The findings revealed increased learner autonomy, creativity, and digital skills, along with more efficient instructional design processes supported by prompt engineering and real-time feedback. The structured 4PADAFE framework helped participants align AI-generated content with specific learning outcomes while maintaining ethical safeguards. This study concludes that, with proper guidance and a systematic framework, students with technical backgrounds can serve as effective instructional designers, demonstrating the potential of combining structured methodologies and GAI to democratize high-quality course development in digital higher education.

… Using a counterbalanced A/B design embedded in … instructional design activities, this study demonstrates that thoughtfully integrated AI tools can effectively support novice designers, …

The current study aims to present and synthesize extant studies across various fields that have discussed the use of generative artificial intelligence (GAI) in instructional procedures or processes. This study adopted the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model since it is the most widely used instructional and training design model. An integrative literature review of 71 articles was conducted as the primary methodology. We analyzed and summarized information about (a) the integration and implementation of GAI in each stage of the ADDIE model and (b) suggested uses of GAI based on the model to further leverage what it can offer. Potential ethical concerns and recommendations are also examined for future research and practice using GAI in ADDIE. The study concludes with a discussion and implications for future research and practice.

… healthcare education by generating draft simulation scenarios for nursing and medical training [45, 46] illustrating its expanding role in content development and instructional design. …

… educational framework designed to guide … Design Thinking (DT) as an instructional design method, merged with constructive alignment principles, and generative artificial intelligence. …

… A key question is whether GenAI can support teachers in one of their major roles: designing … use impact instructional design outcomes. To achieve this, the study design includes three …

… Integrating AI-generated elements seamlessly with learner-developed … novice instructional designers. Conducted within a graduate-level course (n = 27) focusing on instructional design …

: increasing use of this technology with its tools and applications in the field of educational design, especially its employment in technology-enhanced education, especially with the spread of the Covid pandemic and the increase in reliance on e-learning, there was a need to identify the effectiveness of employing one of the most important and famous image-generating AI techniques through Prompt orders for educational design based on the Addie education design model. The research aims to identify the methods, tools and effectiveness of employing generative AI in educational design. The importance of the research is due to highlighting the importance of employing generative AI with its techniques in educational design, analyzing its effectiveness and contributing to the development of designers' experiences. The research problem is to determine the possibility of using generative AI for images in educational design and how to employ it. The researchers followed the descriptive analytical approach to identify the techniques of generative AI as well as the practical experimental approach in designing models using generative AI to support the scientific and visual content of primary-level educational courses. The research concluded that generative AI has tremendous potential in educational design. Education designers can create more attractive and relevant educational experiences. However, it is necessary to measure the outputs according to the target educational category to ensure that the use of generative AI is compatible with educational goals, which differ for literary-style courses from scientific-style courses.

The rapid development of Generative Artificial Intelligence (GAI) technologies is driving profound changes in education. This study investigates the construction of human–Artificial Intelligence (AI) collaborative teaching models supported by GAI, with the goals of improving teaching efficiency, enabling personalized learning, and optimizing the allocation of educational resources. The proposed framework integrated intelligent content generation, real-time tutoring, adaptive learning pathways, and a teacher–student–AI collaboration mechanism. The generative model employed was LLaMA-2-13B, which was domain-adapted through supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). The experiment was conducted in a university course titled Data Structures and Algorithms, involving 60 students in the experimental group and 60 students in the control group. Multi-dimensional data were collected and analyzed, including academic performance, student engagement, interaction depth, technology acceptance, and long-term retention. The quality of AI-generated content was evaluated using Bilingual Evaluation Understudy (BLEU, 0.74) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE, 0.79), with a Cohen’s κ value of 0.86 indicating high inter-rater consistency. The results showed that the GAI-driven human–AI collaborative model significantly improved final exam scores (85.6 vs. 78.3, p < 0.001), average assignment grades (91.2 vs. 84.7, p < 0.001), and learning satisfaction (p < 0.05), while reducing cognitive load and enhancing personalized and interactive learning. This study provides both a theoretical framework and practical guidance for innovating educational models in the era of intelligent technology, offering valuable insights for advancing the digital transformation of education.

This article theorizes Human-AI Collaborative Teaching as a co-teacher paradigm grounded in joint cognitive systems and reliability-first sociotechnical design, where instructional quality emerges from coupling, constraint and accountable orchestration rather than model fluency. The synthesis reframes teaching as real-time regulation under bounded rationality, linking distributed cognition and situated cognition to role-bearing AI participation across planning, enactment, assessment and reflection. It specifies a governance-ready architecture in which teacher authority is preserved through decision-rights partitioning, mixed-initiative interaction protocols, calibrated uncertainty signalling, and an abstain-escalate safety regime. Epistemic robustness is operationalized through provenance discipline, evidence-anchored feedback and contestability pathways that protect epistemic dignity, participation equity and multilingual-accessibility rights under high-stakes accountability. The article integrates constructs from learning sciences, human-computer interaction, resilience engineering, implementation science and public governance to produce five compact design instruments, a theoretical lens map, role-ecology contracts, interaction protocol patterns, a governance risk register, and an institutional maturity model for scalable adoption. The resulting framework offers concrete, globally portable implementation logic for policy makers, workforce development leaders, and educational technologists seeking audit-ready co-teaching infrastructures that enhance teacher noticing, strengthen formative inference and sustain assessment validity without privacy erosion, surveillance creep or de-skilling.

Artificial intelligence integration, specifically ChatGPT, is becoming increasingly popular in educational contexts. This research paper provides a systematic literature review that examines the effects of incorporating ChatGPT into education. The study examines four primary research questions: the benefits and challenges of ChatGPT, its impact on student engagement and learning outcomes, ethical considerations and safeguards, and the effects on educators and teachers, based on an analysis of numerous scientific research articles published between 2022 and 2023. The results emphasize the numerous benefits of ChatGPT, such as the opportunity for students to investigate AI technology, personalized assistance, and improved learning experiences. Furthermore, advantages such as enhanced learning and enhanced information accessibility are identified. Nevertheless, ethical considerations and biases in AI models are also highlighted. ChatGPT enhances student engagement by offering personalized responses, prompt feedback, and rapid access to information, resulting in enhanced learning outcomes and the growth of critical thinking abilities. Ethical considerations and safeguards, including user education, privacy protection, human supervision, and stated guidelines, are essential for responsible use. The integration of ChatGPT transforms the role of educators from content delivery to assistance and guidance, thereby fostering personalized and differentiated learning. Educators have to consider ethical considerations while monitoring student usage in order to facilitate this transformation. Educational institutions can increase student engagement, learning outcomes, and the responsible use of AI in education by addressing challenges, establishing ethical guidelines, and leveraging the strengths of ChatGPT. This will prepare students for future challenges.

In terms of language models, generative artificial intelligence (GenAI), and more specifically ChatGPT, offer a significant technological achievement as a revolutionary tool for natural language processing (NLP) and a transformative educational business tool. ChatGPT users' suggestions have the ability to optimize teaching and learning, thereby having a substantial impact on the educational environment of the twenty-first century. Educational robots are getting easier to access for a number of reasons. The human-robot cooperation that has advanced scientifically in industry 5.0 extreme digital automation, will also probably become a regular aspect of life in the days to come. This study examines the prospective uses of GenAI for NLP synthesis as well as its potential role as a conversational agent in the classroom business. GenAI's capacity to understand and produce language that is human-like by employing NLP to generate semantics was essential to its ability to replicate the most advanced human technology through comprehensive assumptions of patterns and structures it learns from its training data. With the rise of artificial intelligence (AI) driven conversational agents, prompt engineering has become an important aspect of digital learning. It is essential to get ready for an AI-dominated future when general and educational technologies combine. The study demonstrated how society may impact and contribute to the development of AI pedagogic learning using an instructional robotics application driven by AI, emphasizing the responsibility of humans as producers to reduce any potential misfortunes. The study highlights that since generative AI technologies have the potential to drastically change teaching and learning approaches and necessitate new ways of thinking, more research on organizational robotics, with a focus on human collaboration and education, will emerge from the technological concerns raised in this study.

Introduction Immunology education faces persistent challenges, including abstract concepts, theory-practice disconnect, and delayed feedback. Generative AI offers opportunities, yet its integration into pedagogical frameworks remains underexplored. Methods We developed an Iterative AI-Augmented Immunology Education (IAIE) model integrating generative AI with the 4C/ID model and cognitive load theory. A 12-week quasi-experimental study enrolled 177 medical undergraduates (experimental: n = 88; control: n = 89), with outcomes assessed using the Host-Pathogen Interactions Concept Inventory (HPI-CI) and a modified Clinical Decision-Making in Nursing Scale (CDMNS). Results The experimental group showed significantly greater knowledge mastery (82.7 vs. 70.1, Cohen’s d =1.12) and clinical decision-making accuracy (+31.7%). Over 84% of students recognized AI’s value in understanding complex mechanisms. Reflective practice scores indicated high levels of reflective ability, and DREEM assessments confirmed strong student acceptance of the learning environment. Discussion The IAIE model effectively bridges theory and practice through AI-driven scaffolding that modulates cognitive load, fostering higher-order thinking and critical engagement with AI-generated content. This theory-informed framework offers a transferable approach for competency-based medical education.

The emergence of generative AI (GenAI) models, including large language models and text-to-image models, has significantly advanced the synergy between humans and AI with not only their outstanding capability but more importantly, the intuitive communication method with text prompts. Though intuitive, text-based instructions suffer from natural languages’ ambiguous and redundant nature. To address the issue, researchers have explored augmenting text-based instructions with interactions that facilitate precise and effective human intent expression, such as direct manipulation. However, the design strategy of interaction-augmented instructions lacks systematic investigation, hindering our understanding and application. To provide a panorama of interaction-augmented instructions, we propose a framework to analyze related tools from why, when, who, what, and how interactions are applied to augment text-based instructions. Notably, we identify four purposes for applying interactions, including restricting, expanding, organizing, and refining text instructions. The design paradigms for each purpose are also summarized to benefit future researchers and practitioners.

… It is necessary to acknowledged that further iterations and refinement was required post-… For the learning developers, implementing an AI-augmented process resulted in potentially …

The rapid evolution of cloud-native technologies demands agile pedagogical approaches to cultivate engineering competencies. However, current education struggles with cognitive overload caused by tool abstractions, outdated curricula, and ineffective skill transfer. To bridge these gaps, this paper introduces AI-Augmented Pedagogy (AIAP), an innovative teacher-AI collaborative approach integrating outcome-based education (OBE) with AI engines. Its core contribution is a teacher-driven optimization loop which empowers teachers to leverage AI engines to: (1) align curriculum with industry demands in real time; (2) generate authentic scenario-based experiments contextualizing theoretical knowledge; (3) structure adaptive visual knowledge graphs that decompose technical complexity; and (4) identify multidimensional skill gaps using AI-powered assessments. Validation in cloud-native courses demonstrated AIAP's tangible efficacy. Teachers dynamically aligned curricula with innovations in the cloud-native ecosystem and cultivated students’ essential abilities to design, deploy, diagnose, and optimize, resulting in a significant improvement in high-score rates. This establishes a reusable paradigm where teachers, guided by AI, convert technical abstractions into diagnosable scenarios, enabling effective skill transfer in evolving technical domains.

… the continuity and feedback structure of individualized instruction. Grounded in Bloom’s Two … to remember as learners do — imperfectly, iteratively, and with reflection. This vision now …

… -tutor) that provides students with adaptive scaffolding while … about a kind of scaffolding the meta-tutor should provide for … cognitive scaffolding as a new functionality of the meta-tutor. …

… in particular, AI tools such as intelligent tutoring systems, generative models (eg, ChatGPT), and adaptive learning systems are employed to provide personalized instruction (Amiri et al., …

ABSTRACT To address the limitations of general-purpose artificial intelligence (AI) tools, we developed a task-oriented AI chatbot based on the 5E (i.e. “engage”, “explore”, “explain”, “elaborate” and “evaluate”) model to scaffold students’ instructional design process. We examined the impact of integrating the 5E instructional model-informed AI chatbot on students’ learning performance and perceptions. The results indicated that the AI chatbot, when combined with human teacher scaffolding, significantly improved the students’ instructional design performance relative to receiving human teacher scaffolding only. The chatbot provided valuable suggestions on instructional design frameworks, class activities and teaching topics during the “explore” phase. In the “evaluate” phase, the chatbot offered immediate feedback on the students’ design plans and proposed alternative instructional frameworks regarding areas for improvement. However, the students expressed concerns about the chatbot’s evaluation quality, noting that it needed to be better aligned with the course assessment rubric. We recommend using AI chatbots for instructional design conceptualisation, although we emphasise the critical role of human teachers in evaluating final design work and providing timely support.

Undergraduate civil engineering students frequently struggle to transition from deterministic to probabilistic reasoning, a conceptual shift essential for modern structural design practice governed by reliability-based codes. This paper presents a design-based research (DBR) contribution and a theoretically grounded pedagogical framework that integrates AI-powered conversational tutoring with interactive simulations to scaffold this transition. The framework synthesizes cognitive load theory, scaffolding principles, self-regulated learning research, and threshold concepts theory. The design incorporates three novel elements: (1) a structured misconception inventory specific to structural reliability, derived from literature and expert elicitation, with each misconception linked to targeted intervention strategies; (2) an integration architecture connecting large language model tutoring with domain-specific simulations, where simulation states inform tutoring and misconception detection triggers targeted activities; and (3) a scaffolded module sequence building systematically from deterministic foundations through probability concepts to reliability analysis methods. Sequential modules progress from uncertainty recognition through Monte Carlo simulation and design applications. We provide technical specifications for the implementation of AI tutoring, including prompt engineering strategies, accuracy safeguards that address known limitations of large language models (LLMs), and protocols for escalation to human instructors. An assessment framework specifies concept inventory items, process measures, and practical competence tasks. Ultimately, this paper provides testable conjectures and identifies conditions under which the framework might fail, structuring subsequent empirical validation with student participants following institutional ethics approval.

This paper conducts an integrative review to synthesize current research on the intersection of Artificial Intelligence (AI), adaptive learning systems, and Vygotsky's Zone of Proximal Development (ZPD). It aims to clarify how AI functions as a "digital scaffold" and evaluate the extent to which current technologies align with the core principles of ZPD. A systematic search of academic databases was conducted for peer-reviewed literature published between 2020 and 2025. The review analyzes and synthesizes findings from these sources to identify key themes, practical applications, and research gaps. The analysis reveals that AI-powered systems operationalize ZPD primarily through three mechanisms: (1) personalized learning paths that adapt content difficulty in real-time; (2) immediate, targeted feedback that corrects misconceptions; and (3) the facilitation of self-regulated learning. Recent literature indicates a shift towards using generative AI to also support collaborative and social learning, moving beyond purely individualized instruction. The paper highlights a gap between the theoretical potential of AI in education and its practical implementation, particularly concerning equity, teacher preparedness, and ethical data use. It concludes by proposing a framework for designing and evaluating AI tools based on Vygotskian principles and calls for more empirical research on the effectiveness of AI as a digital scaffold in real-world classroom settings.

This article explores how nursing professional development (NPD) practitioners can use artificial intelligence (AI) and generative artificial intelligence (Gen AI) across each phase of the ADDIE (Analyze, Design, Develop, Implement, Evaluate) instructional design model to drive timely, personalized, data-driven education that aligns with health care system priorities, boosts learner engagement, and improves outcomes. The authors describe ways to use AI according to the ADDIE framework for NPD education, including the benefts and risks of this technology. Integrating Gen AI within the ADDIE framework positions NPD practitioners as strategic enablers of agile, responsive education. It allows for faster, tailored content development, real-time learner adaptation and evaluation, and alignment of education with organizational performance. Applying Gen AI within the ADDIE framework provides a structured, yet flexible strategy to operationalize innovation. When intentionally integrated within the ADDIE framework, these technologies allow NPD practitioners to assess, design, develop, implement, and evaluate education programs that are agile, scalable, and directly aligned with organizational goals.

The growing integration of artificial intelligence (AI) in education presents new opportunities and challenges for instructional design (ID). This study aims to develop and validate an AI-Incorporated Analyze, Design, Develop, Implement, and Evaluate (ADDIE) Instructional Model that embeds AI functionalities into the five phases—Analyze, Design, Develop, Implement, and Evaluate. The model is designed to enhance instructional planning through learner diagnostics, content generation, adaptive assessments, and automated feedback. To examine the perceived necessity and applicability of AI integration in each ADDIE phase, a survey-based research design was employed. A total of 167 mathematics educators, including 143 pre-service and 24 in-service teachers, participated by responding to Likert-scale items and open-ended questions. Results indicate strong support for AI integration, especially in the Design, Develop, and Evaluate phases. Pre-service teachers prioritized support in the Implement phase, while in-service teachers emphasized the value of AI in the Evaluate phase. Despite this broad enthusiasm, participants raised concerns about data privacy, ethical implications, and the potential loss of human creativity. These findings underscore the need for balanced, ethical, and pedagogically grounded AI integration in education. The proposed model offers a structured, adaptable framework for instructional designers and educators aiming to leverage AI for learner-centered and technology-enhanced teaching.

This study examines the application of the ADDIE (Analysis, Design, Development, Implementation, Evaluation) framework in developing an Artificial Intelligence (AI) training program through an open-source Learning Management System (LMS) for vocational school teachers. It addresses the lack of structured AI training and low teacher preparedness in vocational education. The objective of this study is to evaluate how each ADDIE phase contributes to improving teachers’ competencies in teaching Artificial Intelligence (AI) using an open-source LMS. A mixed-methods approach was used with 150 teachers from 15 vocational schools in West Java, Indonesia. Training was delivered through a public LMS using blended learning. Data were collected via surveys, classroom observations, and LMS analytics. Path analysis was conducted using SmartPLS to assess the relationships between ADDIE phases and training outcomes. Results revealed a notable improvement in AI literacy and an increase in instructional capabilities and teachers’ technology proficiency post-training. The Design and Implementation stages had the most substantial impact on learning outcomes. The open-source LMS proved effective and scalable for AI training. This study provides a structured approach to professional development and highlights the potential for integrating AI into vocational education, particularly in low-resource settings. AbstrakPenelitian ini mengkaji penerapan kerangka kerja ADDIE (Analysis, Design, Development, Implementation, Evaluation) dalam mengembangkan pelatihan Kecerdasan Artifisial (AI) berbasis platform Learning Management System (LMS) sumber terbuka bagi guru Sekolah Menengah Kejuruan (SMK). Penelitian ini merespons minimnya pelatihan AI yang terstruktur serta rendahnya kesiapan guru vokasi. Tujuan penelitian ini adalah mengevaluasi kontribusi setiap tahapan ADDIE terhadap peningkatan kompetensi guru vokasi dalam mengajar AI dengan memanfaatkan LMS sumber terbuka. Metode campuran digunakan, melibatkan 150 guru dari 15 SMK di Jawa Barat, Indonesia. Pelatihan diselenggarakan melalui LMS publik dengan pendekatan blended learning. Data dikumpulkan melalui survei, observasi kelas, dan analisis penggunaan LMS. Analisis jalur dilakukan menggunakan SmartPLS untuk menilai hubungan antara tahapan ADDIE dan hasil pelatihan. Hasil menunjukkan peningkatan signifikan dalam literasi AI dan kemampuan instruksional guru dalam penguasaan teknologi pasca pelatihan. Tahapan Design dan Implementation berpengaruh paling besar terhadap hasil pembelajaran. LMS sumber terbuka terbukti efektif dan dapat diskalakan untuk pelatihan AI. Penelitian ini menawarkan model pengembangan profesional yang terstruktur dan menunjukkan integrasi AI dalam pendidikan vokasi, terutama di lingkungan dengan keterbatasan sumber daya.Kata Kunci: guru sekolah menengah kejuruan; kecerdasan artifisial; LMS sumber terbuka; model ADDIE; pelatihan guru

… AI tools can support the creation of an online course using Bloom’s taxonomy and the ADDIE … was participating in the selection of the AI tools. According to the preliminary results, for the …

The ADDIE model is traditionally used to systematically guide the development of effective educational experiences. This chapter explores how the model is applied to shape the design and implementation of a no-code AI chatbot system that enables personalized online learning. Insights and strategic approaches from studies conducted on AI chatbots built using this system are discussed, with clarity provided through illustrative examples. The chapter also delves into other forms of chatbots, such as those incorporating virtual humans or generative AI, comparing different chatbot types used both locally and internationally. Their optimal use cases are identified, offering insights into the literacy required for effective utilization. This helps readers navigate and maximize the potential of various chatbot types in different contexts. Additionally, the chapter outlines considerations common to AI platforms and tools—such as data collection and analysis, language capabilities, learning behaviors, and preferences—at each stage of the ADDIE model.

… This research presents the ADGIE model, which integrates generative AI with the ADDIE model to improve the efficiency, personalization and adaptability of instructional design. Our …

The dynamic cybersecurity threats in specialized domains like space systems create significant challenges for educational content development. Traditional curriculum development struggles to keep pace with dynamic industry requirements, taking months to develop and requiring large expert teams. This paper introduces a systematic framework that integrates Generative AI (GenAI) with established instructional design principles to rapidly develop domain-specific cybersecurity training. Our framework combines Retrieval Augmented Generation (RAG) with the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model, enabling requirements-driven curriculum development that translates industry stakeholder interviews and job descriptions directly into comprehensive educational materials. We demonstrate framework feasibility through Space Information Systems Security Officer (ISSO) curriculum development, generating 500+ domainspecific Knowledge, Skills, and Tasks (KSTs), modular lectures, 886 assessment questions, and gamified exercises within hours rather than months. Market research with 33 industry professionals revealed critical gaps in existing training frameworks, with $82 \%$ emphasizing soft skills and $67 \%$ requiring holistic system understanding beyond traditional cybersecurity domains. Our proof-of-concept implementation with 5 completing participants showed promising results with test scores improving from $73.9 \%$ to $92.1 \%$, though larger validation studies are needed. The systematic framework addresses identified gaps in current cybersecurity training approaches while providing a replicable methodology for other rapidly evolving technical domains requiring specialized workforce development.

Purpose: Extensive use of Large Language Models (LLMs) threatens students' higher-order cognitive skills. This paper presents a novel framework redefining Prompt Engineering as a Technological Pedagogical Design Skill. It provides the syntax to operationalize pedagogical intent, specifically to sustain Productive Struggle and foster Evaluative Judgement. Methods: Following a Design Science Research (DSR) methodology, our framework artifact adapts foundational prompt pattern catalogs and maps them to pedagogical goals. We demonstrate its application via a secure coding course design using the DeLTA model and report on its preliminary validation in a master's course. Results: Our course design shows how patterns like the Cognitive Verifier can structure inquiry to sustain Productive Struggle. Preliminary validation from the master's course indicates the framework effectively scaffolds complex tasks, such as research proposal development, thereby fostering students' Evaluative Judgement. Conclusion: This DSR artifact offers a systematic method to transform the LLM from an unmanaged risk into a controlled pedagogical agent. Mastering prompt patterns allows instructors to design AI-augmented experiences that support, rather than supplant, student cognition, shifting the educator's role towards that of a 'learning architect'.

… an innovative framework that integrates ChatGPT into L2 writing through a synthesis of active, inquiry-based, and adaptive learning principles. Within the framework, learners occupy the …

… framework exists to guide their optimal use. This research aims to address this gap by examining current LLM … impacts on learning outcomes, and proposing a framework to enhance …

… We propose an LLM-augmented framework that could serve as a reference model for incorporating LLMs into the curriculum design process. Our work aims to support educators and …

Large Language Models (LLMs) are increasingly prevalent in software engineering (SE) practice, yet their integration into education remains improvised and lacks theoretical grounding. This paper presents a quality-oriented framework for LLM integration in programming curricula, drawing on Cognitive Load Theory and Constructivism. We demonstrate its utility by using an LLM (Claude Sonnet 3.7) to redesign an Operating Systems assignment, followed by expert evaluation. Results show that LLMs can serve as tools for curriculum design and aids for student learning. For instructors, LLMs streamline the redesign of assignments while maintaining quality. For learners, they provide structured guidance and promote metacognitive development. We position LLMs as reusable educational components that can improve learning outcomes for students and streamline material design for instructors, while preparing both for professional environments where Artificial Intelligence (AI) collaboration is increasingly expected.

The IEEE P2807.6 Education Knowledge Graph (EduKG) standard defines a semantic infrastructure to represent educational knowledge, resources, and pedagogy in a unified graph format. This paper expands on the core EduKG architecture, detailing its ontology design and key entities-Learning Points, Resource Items, and Pedagogical Rules-that collectively model the domain, content, and instructional strategies of learning systems. We further explore how EduKG can be integrated with advanced AI technologies, including large language models (LLMs) and retrieval-augmented generation (Graph-RAG) via embedding databases, to enable intelligent behavior such as semantic search, question answering, and dynamic content generation. These integrations position EduKG as a central component in next-generation smart education systems, wherein knowledge graphs work in concert with intelligent agents and adaptive instructional systems to deliver fully automated, personalized, and interactive learning experiences. By leveraging the standardized graph-structured representation and semantic reasoning capabilities of EduKG, such systems can achieve interoperability across platforms and support complex AI-driven tutoring and training scenarios. This work provides a comprehensive overview of the EduKG framework and highlights its role in empowering adaptive, cognitive, and collaborative learning solutions for the future of digital education.

Dialogue systems have long supported learner reflections, with theoretically grounded, rule-based designs offering structured scaffolding but often struggling to respond to shifts in engagement. Large Language Models (LLMs), in contrast, can generate context-sensitive responses but are not informed by decades of research on how learning interactions should be structured, raising questions about their alignment with pedagogical theories. This paper presents a hybrid dialogue system that embeds LLM responsiveness within a theory-aligned, rule-based framework to support learner reflections in a culturally responsive robotics summer camp. The rule-based structure grounds dialogue in self-regulated learning theory, while the LLM decides when and how to prompt deeper reflections, responding to evolving conversation context. We analyze themes across dialogues to explore how our hybrid system shaped learner reflections. Our findings indicate that LLM-embedded dialogues supported richer learner reflections on goals and activities, but also introduced challenges due to repetitiveness and misalignment in prompts, reducing engagement.

… LLM support, for introducing elementary NLP concepts. This begins with identifying the modules where LLM integration can significantly enhance learning. … interact with LLM-generated …

This project constructs a subject knowledge map for instructional design based on natural language processing technology. This provides a new way of thinking and method for the teaching practice of this subject. Firstly, the ontology-oriented dictionary learning method is studied. By using the fusion ontology relation extraction method, the subject-action-semantic association is extracted to enrich the category of association. Ontology alignment technique is used to disambiguate the extracted knowledge base. Using “Neo4j” as the development tool, the knowledge map of course design theme is displayed visually. It is found that presenting the image of information and knowledge in the subject teaching is an operable technological support. The system further expands the applied research field. Knowledge graph and other visual tools will be used in more education fields in the future to achieve specific visual education based on the corresponding experience and lessons.

… , course plans, student learning objectives, and … Knowledge Graph Reciprocal Instructional Design (KGRID), which employs KGs as an instructional design tool to organize learning …

Digital transformation is reshaping the education sector, fostering an AI-enabled, learner-centered ecosystem. This shift is characterized by the adoption of large language models (LLMs) in education, which is forging a new paradigm for intelligent teaching. However, the integration of LLMs into K–12 AI education is often hindered by their tendency to generate factually inaccurate and pedagogically misaligned content. To address this, we constructed a knowledge graph (KG) of the K–12 AI curriculum and developed a question-answering system based on KG-augmented LLMs. The system was evaluated on a dedicated AI curriculum dataset comprising 1098 questions categorized into three difficulty levels. The evaluation employed the G-Eval with no-reference metrics. Using DeepSeek-V3 as the scoring model, the system performance was assessed across three mainstream LLMs and measured along five distinct dimensions. Results indicated that the integration of curriculum KG significantly enhanced the factual accuracy and relevance of LLM-generated answers in K–12 AI education. However, this enhancement involves a trade-off, as the incorporation of non-declarative knowledge can negatively affect linguistic fluency and coherence. Performance gains varied across LLMs: Qwen and Baichuan demonstrated the strongest improvements, particularly in complex tasks. This study provides a scalable, knowledge-anchored framework for developing reliable AI teaching assistants, demonstrating a practical pathway to mitigate domain-specific hallucinations in educational applications.

Artificial Intelligence (AI) is altering the ways of digital pedagogy through the use of adaptive learning systems that are more accurate, scalable, and context-aware. This paper put forward a hybrid instructional framework that merges Large Language Models with Knowledge Graph technologies to create a technically robust and personalized learning environment. By harness the generative and conversational abilities of LLMs and the structured semantic reasoning by knowledge graphs, the system is set to surpass the constraints of the usual AI-based education tools. The method here is the design of the architecture, the integration at the module-level, and the controlled experimental evaluation across several learning conditions. The observed results reveal an increase in educational performance goals namely learner engagement goes up by 78 percent, comprehension metrics improve by 65 percent, and long-term retention gets better. The architectural design is laden with sophisticated natural language processing pipelines, graph-driven semantic retrieval mechanisms, and adaptive learning algorithms that can change content difficulty, sequencing, and feedback if real-time learner analytics are different.This amalgamation of two models lessens the rates of hallucination, enhances the factuality of the content, and helps in the uniformity of the domain-specific instruction. The findings position the system as a scalable solution ready for a massive number deployment in academic settings. The study, in general, is a great idea towards future intelligent tutoring systems technical basis and is a workable framework for the institutions that plan to implement AI-driven personalized learning technologies.

Metacognitive reflection is a crucial transversal skill, especially in an era where generative AI transforms how we teach and learn. As well as being a driver of the need to develop metacognitive reflection, generative AI is also a tool that can be used to enhance metacognitive reflection, such as chatbots that act as coaches to guide students in metacognitive reflective practice. In this study, we examined the potential of LLM-powered chatbots to promote metacognitive reflection across three distinct educational contexts. Our results show that the chatbot successfully constructed a metacognitive dialogue and delivered relevant, evidence-based recommendations. However, student engagement levels were generally low, with limited active participation observed across all studies. Notably, metacognitive self-regulation, and other individual differences, did not consistently predict engagement levels, suggesting that learners with higher reported self-regulation were not inherently more likely to use the tool. We also found no evidence that metacognitive engagement levels led to improved learning outcomes. However, these findings must be interpreted with caution, as engagement levels may be a limited metric for capturing how students benefit from chatbot-assisted reflection. We conclude by raising key design questions around how to develop chatbot systems that not only deliver metacognitive content and feedback but also encourage active student participation. While system prompts can help LLMs maintain focus on metacognitive reflection, hybrid designs that add an additional layer of scripting or multi-agent systems may be necessary to support an active learner role and ensure that important metacognitive checkpoints are met by the learner.

Rapid emergence of generative artificial intelligence (AI), is transforming the landscape of writing instruction. While current applications often emphasize surface-level uses like grammar correction or paraphrasing, this study proposes a deeper pedagogical integration: conceptualizing AI as metacognitive and dialogic scaffold capable of supporting higher-order thinking. Drawing on Cognitive Load Theory, Sociocultural Theory, Frameworks of Dialogic Learning, and Critical Pedagogy—the research situates AI as a non-human dialogic agent that can interactively engage students within their Zone of Proximal Development. Using a Design-Based Research methodology, the study implemented an AI-integrated instructional model in the course of English as a Second Language (ESL). The intervention combined Socratic instruction in strategic prompting with guided use of AI across writing stages. Collected data and findings indicate that AI, when framed as Socratic dialogue partner, can reduce extraneous cognitive load and increase engagement with germane cognitive processes, particularly in ideation and revision. Students reported greater clarity in argument structure and enhanced rhetorical awareness. Importantly, personalized AI feedback was perceived as a motivating guide contingent on students’ ability to co-direct the interaction through intentional prompting strategies. The study contributes a theoretical and pedagogical framework for AI-responsive writing instruction and raises critical questions about ethical literacy and the evolving role of educators in AI-augmented learning environments.

This study examines how AI can be used to improve higher education students’ ability to learn effectively. The research focuses on using AI to enhance metacognition, which is the students’ ability to understand and control their learning processes. Specifically, the study explores the potential of AI-powered prompts to encourage individuals to reflect on their learning and explain their understanding of the materials provided by teachers. Additionally, it highlights the benefits of implementing AI-powered learning companions to provide personalized support and guidance throughout the learning process. Recommendations for future research include investigating how AI can further improve students’ self-regulation skills and enhance peer-review processes through AI-generated feedback.

This chapter investigates the profound influence of intelligent virtual assistants (IVAs) on the educational domain, specifically in the realm of individualized learning and the instruction of writing abilities and content creation. IVAs, incorporating generative AI technologies such as ChatGPT and Stable Diffusion, hold the potential to bring about a paradigm shift in educational programs, emphasizing the enhancement of advanced metacognitive capacities rather than the fundamentals of communication. The subsequent recommendations stress the need to cultivate enduring proficiencies and ascertain tailored learning approaches for each learner, which will be indispensable for success in the evolving job market. In this context, prompt engineering is emerging as a vital competency, while continuous reskilling and lifelong learning become professional requisites. The proposed innovative method for teaching writing skills and content generation advocates for a reconfiguration of curricula to concentrate on applied learning techniques that accentuate the value of contextual judgment as a central pedagogical tenet and the mastery of sophisticated metacognitive abilities, which will be pivotal in the future of work.

Reflective writing is known as a useful method in learning sciences to improve the metacognitive skills of students. However, students struggle to structure their reflections properly, limiting the possible learning gains. Previous works in educational technologies literature have explored the paradigms of learning from worked and modelling examples, but (a) their application to the domain of reflective writing is rare, (b) such methods might not scale properly to large‐scale classrooms, and (c) they do not necessarily take the learning needs of each student into account. In this work, we suggest two approaches of integrating AI‐enabled support in digital systems designed around learning from worked and modelling examples paradigms, to provide personalized learning and feedback to students using large language models (LLMs). We evaluate Reflectium, our reflective writing assistant, show benefits of integrating AI support into the learning from examples modalities and compare the perception of the users and their interaction behaviour when using each version of our tool. Our work sheds light on the applicability of generative LLMs to different types of providing support using the learning from examples paradigm, in the domain of reflective writing. What is already known about this topic Reflective writing fosters metacognitive skills and improves learning gains and personal growth. The learning from worked and modelling examples paradigms is effective for skill acquisition and applying the acquired knowledge. Existing reflective writing assistants usually lack dynamic, AI‐driven feedback or interactivity, limiting personalization and adaptability to each user's own needs in the learning process. What this paper adds It introduces Reflectium, an AI‐enabled reflective writing assistant, integrating intelligent and interactive writing support for both the learning from worked and modelling examples paradigms. It demonstrates the use of a fine‐tuned large language model (LLM) for providing feedback in the learning from worked examples version, and an LLM‐powered conversational agent simulating instructor interactions for the learning from modelling examples version. It reports findings from a user study comparing the positive impact of artificial intelligence (AI) support on learners' performance, interaction behaviour and learning experience. Implications for practice and/or policy Digital tutoring systems for teaching reflective writing using the learning from worked examples paradigm should incorporate adaptive AI feedback to enhance learning gains. Conversational agents simulating peers/instructors and powered by LLMs can provide scalable, interactive support for learning from modelling examples, notably in large‐scale educational settings. Reflective writing tools should be evaluated for their impact on different aspects of the learning process, such as task performance, interaction behaviour and user experience, to guide future improvements. Educators and policymakers should consider the integration of AI‐driven reflective writing tools into teaching curricula to enhance reflective practices and metacognitive skill development.

… deeper exploration of how AI might serve as a metacognitive … sees academic writing as a way of entering into conversation … to give that help, and reflecting on whether the AI output …

… that transitions to Monitoring and Reflection increased with Clair’s … of AI-based conversational agents to scaffold metacognitive … structured engagement with metacognitive regulation. …

Intelligence augmentation can offer personalized learning resources and pathways tailored to each student’s unique characteristics and needs. Among these advancements, the large language model (LLM) agent has ushered in a new revolution in education. In this study, we constructed a metacognitive reflective learning scaffold (MRLS) grounded in metacognitive theory and reflective learning principles to provide conceptual support for students during their reflective practices. In addition, we developed a metacognitive reflective learning agent (MRLA) on the Coze platform designed to deliver personalized guidance and assistance throughout the reflective learning process. We conducted a 16-week $2 \times 2$ quasi-experiment study at Z University in China, where participants were randomly assigned to four groups. Throughout the research process, we collected dialogue data from students using the Coze platform, as well as reflection reports submitted via the XueXiTong platform for quantitative analysis. Empirical results demonstrated that both the MRLS and MRLA significantly enhanced students’ metacognition, indicated that the MRLS offers precise guidance for students’ reflective learning processes, enabling them to better comprehend and articulate their reflections. The MRLA equips students with more convenient, efficient, and intelligent resources, significantly augmenting the provision of metacognitive training support that would otherwise be provided by teachers. This study emphasizes the validity and necessity of MRLS and MRLA for the cultivation of students’ metacognitive ability and provides insights for the future application of LLM agent and learning scaffolds for optimizing students’ learning process.

Generative Artificial Intelligence (GenAI) is revolutionising education, but in doing so, it is often seen simply as a tool for automation and efficiency. This paper challenges this narrow perspective, arguing that GenAI’s transformative potential lies in fostering Meta-AI skills—specialised metacognitive competencies for engaging AI as a cognitive partner. Our objective is to explore how GenAI reshapes learning environments and necessitates a pedagogical shift, hypothesising that traditional education paradigms are insufficient without Meta-AI skills to navigate GenAI’s dynamic outputs. Drawing on constructionist theory, we conceptually analyse seven phenomena: learners’ perceptions, reasoning transitions, scientific research paradigms, information processing shifts, tacit knowledge articulation, multimodal interactions, and early AI education.. The methodology involves synthesising theoretical and empirical literature to frame Meta-AI skills as essential for modern learning. Key results reveal that GenAI transforms education into interactive, exploratory spaces, shifting from symbolic to indexical processing and generalisation-based to situated reasoning. Multimodal GenAI enhances metacognitive awareness, while tacit knowledge exploration deepens self-reflection—outcomes requiring Meta-AI skills beyond conventional metacognition. Practically, educators can integrate these skills into curricula through scaffolded prompt engineering (e.g., refining AI queries), multimodal projects (e.g., creating cross-media narratives), and critical evaluation of AI outputs, empowering students to leverage GenAI effectively. We conclude that GenAI’s integration demands a reevaluation of pedagogy, with Meta-AI skills bridging theoretical shifts and practical applications.

Metacognitive skills, which enable individuals to manage their own learning, can be integrated into artificial intelligence (AI)-supported educational environments. The complexity and rapid change brought about by the information age necessitate that learners not only acquire knowledge but also understand how they manage their learning. In line with this need, the study explores the multifaceted interaction between metacognitive learning strategies and AI systems, both theoretically and practically. The research was designed with a prospective approach, and current developments in the literature were analyzed in depth. The literature review was conducted using qualified academic sources published between 2015 and 2025, with a focus on concepts such as metacognition, artificial intelligence, self-regulation, and learning analytics. The content obtained from these sources was combined through thematic analysis and conceptual coding, and new metacognitive behaviors emerging in human-AI interactions were structured within a conceptual framework. Direct measurement techniques involving quantitative data were not used as data collection tools; instead, conceptual modeling and theoretical synthesis methods were employed. This enabled us to interpret, based on the literature, how metacognitive knowledge types are activated in AI-supported learning processes and what kinds of feedback trigger self-regulation and reflective thinking.

… learning achievement, reflective performance, perception, and metacognitive awareness of … with a Gamified Adaptive Conversational Agent (GAC). It acts as a scaffold for their problem-…

… , an AI-driven conversational tool designed to scaffold student reflection on project-based … practices can enhance crucial transversal and metacognitive skills, including critical thinking…

The dominant paradigm of generative artificial intelligence (AI) in education positions it as an omniscient oracle, a model that risks hindering genuine learning by fostering cognitive offloading.This study proposes a fundamental shift from “AI as Oracle” model to a “Cognitive Mirror” paradigm, which reconceptualizes AI as a teachable novice engineered to reflect the quality of a learner’s explanation. The core innovation is the repurposing of AI safety guardrails as didactic mechanisms to deliberately sculpt AI’s ignorance, creating a “pedagogically useful deficit.” This conceptual shift enables a detailed implementation of the “learning by teaching” principle.Within this paradigm, a framework driven by a Teaching Quality Index is introduced. This metric assesses the learner’s explanation and activates an instructional guidance level to modulate the AI’s responses, from feigning confusion to asking clarifying questions.Grounded in learning science principles, such as the Protégé Effect and Reflective Practice, this approach positions the AI as a metacognitive partner. It may support a shift from knowledge transfer to knowledge construction, and a re-orientation from answer correctness to explanation quality in the contexts we describe.By re-centering human agency, the “Cognitive Mirror” externalizes the learner’s thought processes, making their misconceptions objects of repair. This study discusses the implications on assessment, addresses critical risks, including algorithmic bias, and outlines a research agenda for a symbiotic human-AI coexistence that promotes effortful work at the heart of deep learning.

… the reflective process by increasing learners’ metacognitive … In summary, generative AI can function as a digital scaffold … cognitive regulation throughout the reflective process. It supports …