Search Results (29)

Generative artificial intelligence (GenAI) has transformed design education, yet growing evidence suggests that the fluency of AI-generated outputs may create a “fluency illusion”—a metacognitive bias whereby learners conflate polished AI artifacts with genuine cognitive mastery. A critical unresolved question is how to quantitatively diagnose this AI-induced fluency illusion without disrupting the natural learning process. This study introduces MBS-AIGC, a purpose-built AI-supported design education platform grounded in the Meaning–Behavior–Spirit (MBS) cultural cognition model for Chinese intangible cultural heritage. Drawing on the industrial soft-sensor paradigm, we computationally formalized six behavioral soft-sensor indicators from the digital interaction traces of 71 undergraduate design students over a four-week instructional period and applied K-means clustering to identify latent engagement patterns. Three distinct human–AI collaboration profiles emerged: Deep Explorers (n = 41), Progressive Builders (n = 16), and Surface Operators (n = 14). Crucially, expert-assessed cognitive flexibility significantly differentiated the three groups (F(2, 68) = 5.66, p = 0.005, η² = 0.143), whereas a conventional self-report questionnaire failed to distinguish among them (F(2, 36) = 0.29, p = 0.748), providing preliminary empirical evidence for the fluency illusion in design education. By addressing the lack of objective diagnostic tools for metacognitive miscalibration, this research contributes a scalable, zero-intrusion behavioral soft-sensor framework that enables educators to decode human–AI collaboration patterns and mitigate the fluency illusion in creative learning environments. Full article

The increasing integration of generative artificial intelligence (AI) into teacher education and pedagogical supervision requires explanatory frameworks capable of clarifying how AI-generated feedback may support professional learning processes. Existing research has predominantly focused on technological adoption, implementation challenges, and user perceptions, while comparatively limited attention has been devoted to the cognitive and reflective mechanisms involved in AI-supported pedagogical supervision. In response to this gap, this article proposes a theory-building conceptual framework explaining how AI-supported pedagogical supervision may influence pedagogical decision-making through sequential mechanisms involving feedback quality, cognitive processing, and reflective practice. Drawing on feedback theory, Cognitive Load Theory, reflective practice literature, and distributed cognition perspectives, the proposed framework conceptualises AI not as a direct instructional agent, but as a support system embedded within professional pedagogical reasoning processes. To facilitate future empirical investigation, the article proposes a validation framework based on covariance-based Structural Equation Modelling (CB-SEM). This methodological specification is intended solely as a research agenda for subsequent studies and does not constitute empirical testing of the model. As a conceptual contribution, the article advances a theoretically integrated explanation of how AI-generated feedback may influence professional learning processes. By articulating feedback quality, cognitive processing, reflective practice, and pedagogical decision-making within a coherent framework, it offers a foundation for future empirical research and theory development in AI-supported pedagogical supervision. Full article

The rapid integration of artificial intelligence (AI) technologies into educational contexts has introduced innovative instructional approaches, particularly within Science, Technology, Engineering, and Mathematics (STEM) education. Although an increasing number of empirical studies have examined AI-supported instruction, existing findings remain heterogeneous, making it difficult to draw firm conclusions about its overall effectiveness. This study aims to systematically synthesize experimental and quasi-experimental research on AI-supported instructional interventions in STEM education, quantify their overall effects on student learning outcomes, and examine potential moderating factors, including educational level, STEM discipline, and intervention duration. A comprehensive systematic literature search was conducted across Web of Science, Scopus, ERIC, ScienceDirect, and Google Scholar, covering studies published between 2005 and 2025. A total of 35 studies meeting predefined inclusion criteria were included in the meta-analysis. Effect sizes were calculated using Hedges’ g, and a Random Effects Model (REM) was employed to account for heterogeneity among studies. Moderator analyses were conducted for educational level, STEM discipline, and intervention duration. Publication bias was assessed using multiple diagnostic methods. The meta-analysis revealed a statistically significant overall positive effect of AI-supported instruction on student learning outcomes in STEM education (g = 0.67, 95% CI [0.49, 0.85], p < 0.001). Moderator analyses indicated that AI interventions were most effective at the high school level. Although Science and Mathematics disciplines showed slightly higher effect sizes, the between-group difference was not statistically significant (Q = 4.85, df = 2, p = 0.088). Regarding intervention duration, the highest effect size was observed in interventions lasting more than one month and up to two months, though no consistent pattern of increasing effectiveness with longer durations was found. Publication bias analyses suggested minimal influence on the overall findings. AI-supported instructional interventions demonstrate a moderately to highly positive impact on student learning outcomes in STEM education. The effectiveness of these interventions varies according to educational level, disciplinary context, and intervention duration. These findings provide robust empirical evidence supporting the pedagogical value of AI in STEM education and offer guidance for educators and policymakers regarding effective implementation. Full article

In early childhood initial teacher education, a growing number of mature-aged students with diploma qualifications and years of professional experience are undertaking their early childhood teacher degrees through online modes. Given the national staff shortage of early childhood teachers and the important role of higher education in professional development, it is crucial to support these students’ success in their online learning. Drawing on the critical reflection theory and the notions of “reflection-in-action” and “reflection-on-action”, this autoethnographic study examines a university lecturer’s perspective on the challenges of teaching mature-aged students in online Bachelor of Early Childhood Education programs. Four themes have been identified from the current study: the need for step-by-step technical support for the online learning system; acknowledgment of students’ practical experience contributes to online tutorial classrooms; the need for guidance for ethical and responsible use of Generative Artificial Intelligence (GenAI) in class discussions; and interactive dialogic guidance to support their assessment preparation. This study also included specific pedagogical adaptations to support these students, including offering technical support to assist mature-aged students in transitioning to university study, drawing on students’ professional knowledge to promote active engagement, providing interactive guidance to support understanding of assignment instructions, integrating open discussions about the use of GenAI in online class activities, and asking follow-up questions to encourage critical thinking. This study deepens our understanding of how university educators support mature-aged ECE students in their online learning through tailored pedagogical adaptations that align with their unique needs. Full article

Generative AI tools are increasingly being adopted in education, where they function as collaborators that can provide feedback, suggest alternatives, and scaffold learning. In this paper, I conducted an autoethnographic study by examining my experience as a teacher-researcher integrating generative AI tools as a More Knowledgeable Other (MKO) within the context of game design education. Drawing on Vygotsky’s sociocultural theory, this study documents how generative AI can facilitate creative learning by extending learners’ capacity to ideate, iterate, and reflect on their design processes. This study further reflects on instructional practices and observations of learners engaging with AI-supported creative activities across workshops and training programs. My reflections reveal that generative AI tools enhance feedback loops, accelerate prototyping, and democratize access to mentorship by providing context-aware guidance. However, they also introduce challenges related to illusions of competence, a lack of internalization, and reduced iteration design depth. Future work will explore structured pedagogical models that balance human mentorship with AI-assisted guidance, aiming to establish ethical, adaptive, and creativity-centered frameworks for using generative AI in game design education. Through this lens, this study contributes to an emerging understanding of AI-enabled learning partnerships and their implications for cultivating innovation and talent in the creative industries. Full article

This systematic review examines research published between 2021 and 2025 on generative AI and chatbot use in secondary education across nine countries in Southern Europe and the Balkans: Greece, Italy, Spain, Portugal, Malta, Serbia, Croatia, Bulgaria, and Romania. Drawing on studies from IEEE Xplore, the ACM Digital Library, Google Scholar, and arXiv, this review synthesizes evidence on instructional uses, reported learning outcomes, teacher readiness, governance, and language-localization constraints. Across the region, the literature shows rapid experimentation in writing, language learning, programming, and project-based learning but limited long-term evaluation and weak cross-country comparability. Teacher interest is high, yet institutional guidance, assessment frameworks, and local-language resources remain uneven. This review argues that the next phase of adoption should move from isolated classroom experimentation to system-level implementation built around teacher AI literacy, transparent assessment, and context-sensitive design for smaller linguistic ecosystems. Full article

The rapid integration of artificial intelligence (AI) into higher education is reshaping teaching, learning, and assessment, particularly in programming education. While AI coding assistants can enhance feedback, scaffolding, and student engagement, their educational value depends on pedagogical alignment, institutional readiness, and faculty practice, not merely technical capability. Existing adoption frameworks, however, inadequately address these pedagogical and institutional dimensions in domain-specific contexts. This study proposes the AI Coding Assistant Adoption Framework (AICAAF), a theoretically grounded model integrating the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and Self-Determination Theory (SDT). The framework was developed iteratively from prior literature and refined through faculty perspectives. It conceptualises adoption across four interrelated dimensions: usability, pedagogical adequacy, institutional readiness, and faculty engagement. Using PyChatAI as an instrumental case study, this qualitative research draws on semi-structured interviews with 15 faculty members teaching programming courses at Jouf University, a public institution in Saudi Arabia operating in a low- to mid-resource context. Data were analysed using reflexive thematic analysis. Findings indicate that PyChatAI is intuitive and beneficial for novice learners, particularly through instant feedback and automated error correction. However, its pedagogical value is limited in advanced and industry-aligned contexts. Institutional barriers, such as inadequate infrastructure, limited technical support, and the absence of policy frameworks, significantly constrain effective integration. Despite this, faculty expressed strong commitment to adopting AI tools, proposing strategies including curriculum redesign, professional development, and gamified instruction. The study reconceptualises AI adoption as a pedagogical and institutional transformation rather than a purely technological shift. The AICAAF provides a robust foundation to guide curriculum design, teaching practice, and policy development for responsible AI integration in programming education. Full article

Despite widespread claims that Generative Artificial Intelligence (GenAI) will transform education, longitudinal empirical evidence on its pedagogical integration remains limited. This study examines how GenAI use shapes teaching and learning practices over time. Using a mixed methods longitudinal design, the study draws on 34 semi structured interviews conducted at two time points, six to eight months apart, with 17 secondary school teachers who independently adopted GenAI tools. The analysis was triangulated with 212 GenAI-supported teaching and learning activities. A theory-driven classification based on the SAMR framework was combined with inductive thematic analysis and quantitative pre-post comparisons. The findings, based on a thematic analysis of teacher discourse, reveal differentiated trends in opportunities and challenges. Opportunities related to fostering creativity increased over time, whereas efficiency, workload reduction, and teacher empowerment remained stable. Concerns regarding content quality and inherent biases showed a marginal increase, while references to prohibited or improper use declined. Regarding teaching and learning activities, a significant increase was observed in teaching-related uses of GenAI over time. In addition, a significant increase was identified at the Modification level, indicating a shift toward more advanced forms of pedagogical redesign, particularly through the development of personalized materials, AI-supported instructional planning, and adaptive feedback practices, while learning activities at higher levels remained comparatively stable. Taken together, these findings position the SAMR as a dynamic framework for examining longitudinal patterns of GenAI integration and suggest that GenAI currently accelerates instructional innovation more than it fundamentally restructures student learning paradigms. Full article

Background/Context: Creating instructional materials that authentically meet the needs of marginalized learner groups such as student veterans, multilingual adult learners, and first-generation doctoral students demands consistent application of Universal Design for Learning (UDL) principles coupled with meaningful content expertise about those learners’ traits, access needs, and lived experiences. Faculty at teaching-intensive institutions face persistent constraints of time, knowledge, and course load that make systematic UDL implementation difficult. Objective: This practitioner-scholar case study examines whether HAIST-structured agentic LLM-assisted instructional design can produce UDL-aligned materials for student veterans and multilingual learners at a quality level and time frame realistic for under-resourced faculty. Methodology: Drawing from the Human-AI Symbiotic Theory (HAIST) and UDL guidelines, we document four AI-assisted cycles of instructional design at a Hispanic-Serving Institution. Outcomes related to UDL alignment were measured using a rubric adapted from CAST Guidelines 2.2. Results: Across four materials, initial AI generation averaged 61.4% UDL alignment (SD = 8.7%); following iterative calibration, this rose to 84.2% (SD = 5.3%). The largest gains occurred in the Engagement category. Conclusions: These descriptive findings, interpreted as exploratory rather than inferential given the single-site case study design and n = 4 materials, suggest that HAIST-structured AI-assisted design has the potential to produce accessible materials for underserved learner populations in time frames feasible for working faculty. Learner outcome data were not collected in this study; future quasi-experimental work is needed to assess the effectiveness of these materials with target learner populations. Full article

Educational leadership increasingly operates under conditions of uncertainty, ambiguity, and competing demands. The rapid emergence of artificial intelligence (AI) in education intensifies these challenges, requiring school leaders to navigate tensions between innovation and ethics, autonomy and regulation, and professional judgment and accountability. This study examines AI integration primarily through the lens of educational leadership, proposing that leadership not only shapes teachers’ perceptions of AI but also strengthens the translation of those perceptions into practice. Drawing on transformational leadership theory and technology acceptance models (TAM; UTAUT2), the study tests an integrative model in which teachers’ perceptions of AI function as proximal predictors of use, while transformational leadership serves as a contextual moderator. Data were collected from 141 teachers and analyzed using correlational and regression-based moderation analyses. Findings indicate that transformational leadership significantly predicts teachers’ perceptions of AI and strengthens the relationship between perceptions and AI use. While leadership does not directly predict AI use once perceptions are accounted for, it plays a critical role in enabling the enactment of professional beliefs in instructional practice. These findings position school leadership as a central factor in understanding AI integration, highlighting leadership’s role as a contextual enabler of educational innovation. Full article

This study stems from the need to investigate how GenAI tools, particularly ChatGPT-4o, can support the professional development of mathematics teachers. It explores how Blum’s modeling cycle can serve as a conceptual and operational framework for mathematics teachers’ instructional design when supported by ChatGPT-4o. Drawing on a qualitative case study within a teacher professional development program, the research analyzes how two upper secondary school teachers engaged with ChatGPT-4o to redesign a mathematical task involving probability and real-world contexts. Data include responses to three modeling-related tasks, teachers’ prompts and interactions with ChatGPT-4o, and the final mathematical activity they designed. These materials were analyzed qualitatively according to the modeling cycle and its sub-competencies. The results indicate that the modeling cycle provided teachers with a cognitive and methodological scaffold to guide their interaction with ChatGPT-4o, allowing them to structure, validate, and refine AI-generated ideas through all stages of modeling—from understanding and mathematizing to interpreting and validating. These findings suggest that the modeling cycle can be reinterpreted as a design-oriented framework for integrating ChatGPT-4o in mathematics teacher education. Implications for teacher professional development and future research directions are discussed. Full article

Generative artificial intelligence and foundation models have changed machine learning by allowing systems to produce readable text, realistic images, and other multimodal content with little direct input from a user. Foundation models are large neural networks trained on very large and varied datasets, and they form the core of many current generative AI (GenAI) systems. Their rapid development has led to major advances in areas like natural language processing, computer vision, multimodal learning, and robotics. Examples include GPT, LLaMA, and diffusion-based architectures, such as models often used for image generation. Systems such as Stable Diffusion show this shift by illustrating how AI can interpret information, draw basic inferences, and produce new outputs using more than one type of data. This review surveys common foundation model architectures and examines what they can do in generative tasks. It reviews Transformer, diffusion, and multimodal architectures, focusing on methods that support scaling and transfer across domains. The paper also reviews key approaches to pretraining and fine-tuning, including self-supervised learning, instruction tuning, and parameter-efficient adaptation, which support these systems’ ability to generalize across tasks. In addition to the technical details, this review discusses how GenAI is being used for text generation, image synthesis, robotics, and biomedical research. The study also notes continuing challenges, such as the high computing and energy demands of large models, ethical concerns about data bias and misinformation, and worries about privacy, reliability, and responsible use of AI in real settings. This review brings together ideas about model design, training methods, and social implications to point future research toward GenAI systems that are efficient, easy to interpret, and reliable, while supporting scientific progress and ethical responsibility. Full article

The rapid adoption of generative artificial intelligence tools such as ChatGPT in educational settings has generated both enthusiasm and concern regarding their influence on student learning. While several studies report improvements in efficiency, confidence, and perceived understanding, evidence for durable conceptual learning and knowledge transfer remains mixed. This article examines these tensions through the concept of fluency illusion, a cognitive phenomenon in which information that is easy to process is mistakenly judged as being well understood. Using a narrative conceptual review approach, this study synthesizes findings from 41 publications identified through searches of Google Scholar, Scopus, Web of Science, and ERIC covering the period from 2022 to early 2026. The reviewed literature includes 28 empirical studies, nine conceptual or theoretical analyses, and four review articles addressing the use of ChatGPT in educational contexts. Across domains such as writing and language learning, STEM problem solving, feedback and tutoring, and assessment, the literature shows a recurring pattern in which fluent AI-generated responses increase learners’ confidence without consistently improving deeper conceptual understanding. Drawing on research from cognitive psychology and metacognition, this review proposes an integrative conceptual account of how fluent AI output may shape learners’ judgments of understanding and influence their engagement with learning tasks. The paper concludes by discussing implications for instructional design, assessment practices, and metacognitive scaffolding, and outlines directions for future research aimed at empirically examining the proposed framework and identifying strategies to reduce fluency-driven misjudgments while preserving the potential benefits of generative AI in education. Full article

Recent shifts in teacher perspectives indicate that generative artificial intelligence (GenAI) has begun to transform long-standing patterns of routine and repetition in educational practice. This study investigates how educators across different educational levels within an Arab educational context perceive the role of GenAI in fostering sustainable teaching and learning. It examines its influence on learning processes, instructional practices, and educational continuity. Adopting a qualitative research design, the study draws on focus group discussions to capture teachers’ perspectives and applies thematic analysis to explore shared experiences of AI integration in classroom settings. The analysis identified six interconnected themes reflecting a move toward more open and generative learning, the sustainability of learning activities through diversity and personalization, support for teachers in planning, implementation, and assessment, the empowerment of students’ understanding, thinking, achievement, and learning continuity, the central role of ethical considerations, and the challenges and requirements associated with sustainable implementation. The findings demonstrate that the educational value of GenAI is shaped by how it is meaningfully integrated to sustain teaching and learning practices over time. GenAI can contribute to quality and inclusive education in ways that support the long-term aims of Sustainable Development Goal 4. Full article

The accelerated integration of Generative Artificial Intelligence (GenAI) tools such as ChatGPT is transforming learner engagement, instructional design, and institutional governance in education. This systematic literature review synthesizes theory-driven scholarship on GenAI adoption and pedagogical use through the Antecedents–Decisions–Outcomes (ADO) framework, examining how cognitive, motivational, technological, and institutional factors collectively shape implementation and learning outcomes. Drawing primarily on the Technology Acceptance Model (TAM), Self-Determination Theory (SDT), and Institutional Theory, the review integrates complementary insights from Constructivist Learning and Diffusion of Innovations perspectives to conceptualize how antecedents influence decision-making and outcomes across educational settings. The findings indicate that learner motivation, perceived usefulness, digital literacy, and institutional readiness constitute key antecedents affecting GenAI adoption. Decision processes—spanning instructional design, ethical regulation, and pedagogical adaptation—mediate how these antecedents translate into practice. Outcomes reveal a dual trajectory: GenAI enhances personalization, feedback, and self-regulated learning, yet introduces challenges related to ethical ambiguity and overreliance. The review offers a conceptually integrated synthesis that bridges motivational, technological, and organizational perspectives, advancing a theoretical roadmap for ethical and sustainable GenAI adoption. For educators and policymakers, the findings emphasize transparent governance, faculty capacity-building, and equitable access to ensure that innovation remains aligned with pedagogical integrity and human-centered values. Full article