AI in Education doi: 10.3390/aieduc2040034

Authors: Jing Tian

Background: Generative AI coding tools are increasingly used in university computing education, including emerging practices commonly described as vibe coding. However, evidence on their educational effects is scattered across studies that differ in interaction patterns, instructional designs, assessment practices, and learning contexts. Methods: We conducted a scoping review using a PRISMA-guided process and synthesized 37 studies published between 2022 and July 2026. Five research questions examined student–AI interaction patterns, learning outcomes, task-level instructional strategies, assessment and academic integrity, and curriculum alignment with emerging professional practice. Results: The reviewed articles reported that students commonly relied on debugging-oriented prompts, common-case testing, and short edit-run cycles, while stronger performance was associated with more exploratory and evaluative interactions. Reported outcomes included conceptual learning, programming productivity, and affective benefits. Emerging instructional strategies included planning with hints, adaptive scaffolding, dual-mode assignments, and process visibility. Conclusions: The educational value of AI-assisted coding appears to depend less on tool access than on how student interaction is scaffolded, assessed, and connected to emerging competencies in specification, verification, testing, and critical evaluation. Therefore, computing education should treat AI-assisted programming not simply as accelerated code generation, but as a practice requiring deliberate reasoning and responsible oversight.

AI in Education doi: 10.3390/aieduc2040033

Authors: Alwyn Vwen Yen Lee Timothy Cheng Joo Seng Melvin Chan

This study investigates how generative Artificial Intelligence (AI) can scaffold self-directed learning (SDL) in secondary classrooms. Focusing on the interaction between teacher facilitation and AI affordances, it examines and refines the Content–Response–AI&peers–Fusion–reflecT (CRAFT) framework as a practitioner-informed pedagogical scaffold for operationalizing aspects of the Process dimension of the Person–Process–Context (PPC) model. The study addresses a persistent gap between SDL theory and classroom practice by examining how learner agency can be cultivated within AI-supported learning environments. Adopting a qualitative multi-case study design, the research analyzed instructional practices, lesson artifacts, and reflective accounts from six experienced educators from a secondary school in Singapore across one academic year. Data were analyzed using a theoretically informed, hybrid deductive–inductive thematic analysis, enabling cross-case examination and iterative refinement of CRAFT in relation to the PPC model. Findings indicate that while generative AI supports goal setting, idea generation, and iterative feedback, the metacognitive and reflective dimensions of SDL remain underdeveloped without deliberate teacher mediation. Teachers remain critical orchestrators of AI and peer interactions, supporting disciplinary sense-making and learner autonomy rather than positioning AI as a standalone instructional aid. Overall, this study integrates a practitioner-informed and empirically refined pedagogical scaffold with an established theoretical model, offering a theoretically grounded, context-sensitive pedagogical guide for designing learning experiences that support opportunities for student agency.

AI in Education doi: 10.3390/aieduc2030032

Authors: Brady D. Lund Kinza Alizai Eunice Amoje Anuradha Chandrasekaran Stefan Darvischi Jeanne Denmark Nishanth Joseph Paulraj Lalitha Nallamothula Antonio Paes Bavya Sri Vemulapalli

As artificial intelligence systems increasingly serve as a gateway for access to information, economic opportunity, and civic life, higher education programs training AI developers must evolve to prepare practitioners who are not only technically proficient in building AI solutions but also socially accountable in their work. This paper proposes a conceptual framework for integrating explainable AI (XAI) and social accountability into data science, computer science, and information science curricula. Based on accountability theory, information science, and recent XAI research, the proposed framework is organized around four interrelated pillars: answerability, responsibility, enforcement, and reflexivity. These pillars are further situated within technical, social, organizational, and political dimensions of XAI implementation, with particular focus on how XAI techniques such as LIME, SHAP, model cards, and counterfactual explanations can be operationalized as instruments of meaningful accountability. This paper then proposes a multi-level governance framework that links interpretability methods to institutional oversight, regulatory literacy, and participatory design, illustrated through a concrete scenario grounded in graduate data science education. Together, these elements represent a new pedagogical approach that can equip future AI developers to design and deploy AI systems that are accurate as well as transparent, justifiable, and responsive to the communities they serve.

AI in Education doi: 10.3390/aieduc2030031

Authors: Vishnu Kumar Akanksha Anand Robin Butler Krishna Bista

This study investigates psychological profiles associated with trust in artificial intelligence (AI) among university students using an interpretable machine learning (ML) approach. Although AI tools are becoming increasingly embedded in higher education, limited research has examined how students’ psychological resilience, stress levels, and trust in AI interact to shape their perceptions of AI systems. This study analyzes cross-sectional survey data from 107 students at a Historically Black College and University (HBCU) in the United States. K-means clustering was applied to identify student profiles based on psychological resilience, perceived stress, and AI trust. Clustering solutions were evaluated using multiple internal validation metrics and stability analysis, followed by ANOVA and chi-square analyses to characterize psychological and demographic differences across the identified profiles. The analysis identified three student profiles: (i) moderately stressed AI-positive students, (ii) high-resilience low-stress AI-adopters, and (iii) psychologically resilient AI skeptics. Significant differences were observed across all psychological variables (p < 0.001), with stress and AI trust demonstrating the greatest differentiation across profiles. Gender was significantly associated with profile membership (χ2 = 10.34, p = 0.006), whereas age group, academic level, employment status, and STEM background were not significantly associated with profile membership. Overall, the findings highlight heterogeneity in students’ psychological characteristics and AI-related perceptions, suggesting that psychological factors may provide additional insight into variations in AI trust beyond demographic characteristics.

AI in Education doi: 10.3390/aieduc2030030

Authors: Imad Elhajj Aline Germani Joanna Maria Farah Diana Sabbagh Jana Hajj Hassan

Given the rapid advancements in Artificial Intelligence (AI) tools, endless potential is reshaping how students learn, exceeding the boundaries of time, place and the variety of learning modalities. This study explores the ability of AI personas to replicate or refine the benefits of experiential learning, particularly in crisis-affected educational contexts. By conducting a literature review and qualitative analysis of student reflections and AI-generated dialogue, this research measures how effective the text-based AI personas, developed through carefully crafted prompts and outputs, can simulate real-world field interactions. The study centers on students participating in the Experiential Learning HEHI 303 course as part of the Certificate in Innovation Management in Contexts of Uncertainty at the American University of Beirut, who, as a result of the war outbreak in Lebanon, were unable to conduct fieldwork. In response, AI personas were introduced as alternatives for conducting virtual interviews and stakeholder engagement exercises. The findings imply that while AI personas can demonstrate a degree of emotional authenticity, cultural sensitivity, and contextual relevance, they also show limitations in fully capturing the spontaneity and unpredictability of humans. Instances of bias and lack of depth were observed in some responses. However, the study emphasizes the growing value of AI personas in educational settings especially when physical fieldwork is not feasible, while emphasizing the importance of prompt design and human oversight in AI-mediated learning.

AI in Education doi: 10.3390/aieduc2030029

Authors: Olzan Goldstein Nareman Marae-Haj Wafa Zidan

The rapid integration of artificial intelligence (AI) into educational settings presents a profound challenge for teacher education. This study examines how pioneering teacher educators in Israeli colleges of education perceive their role in training student teachers for AI-integrated teaching. Using a qualitative, interpretive phenomenological approach, semi-structured interviews were conducted with 13 participants—seven pedagogical advisors and six lecturers—from seven teacher training institutions representing diverse educational streams. Data were analyzed using a hybrid approach combining human thematic analysis and AI-assisted dialogic analysis. Findings revealed that participants perceived themselves as agents of change responsible for modeling critical AI use, while raising epistemic concerns regarding cognitive atrophy and the erosion of expertise. A crisis of trust triggered by students’ uncritical submission of AI-generated outputs prompted a shift toward process-based and in-class assessment. Pre-service teachers used AI for differentiated lesson planning, visual aids, and simulations, with experiences ranging from heightened self-efficacy to frustration. Persistent challenges included rapid technological change, the absence of institutional policy, ethical and privacy concerns, cultural factors, and economic barriers. Drawing on post-digital theory and AI literacy frameworks, this study argues that effective preparation for the AI era requires a fundamental reimagining of teacher education—one that redefines literacy, pedagogy, and human agency.

AI in Education doi: 10.3390/aieduc2030028

Authors: Oliver Christ Reinhard Riedl Jimmy Schmid Pascale Zürcher Friederike Thilo

Since the onset of the COVID-19 pandemic, children and adolescents across Europe have experienced a significant rise in mental health challenges, including anxiety, depression, and behavioural disorders. Schools have observed a marked shift in social behaviour, with increased emotional instability, social withdrawal, and attention-related issues. Simultaneously, educational systems face a growing shortage of skilled professionals, particularly in psychological support roles. While digital technologies offer partial relief, their effectiveness is limited by concerns such as screen fatigue and a lack of embodied interaction. This paper conceptualises the use of social robots as a novel intervention tool in education, grounded in the 4E cognition framework—embodied, embedded, enacted, and extended—and informed by interdisciplinary research. Using design thinking and insights from empirical and practical work, we developed five use cases for integrating robots into the Swiss MindMatters mental health promotion programme. These five use cases demonstrate how robots can facilitate emotional learning, simulate social interactions, support conflict mediation, and reduce teacher workload. By combining physical presence with adaptive behaviour, social robots, which will serve as digital twins of pedagogical partners, offer a promising, ethically sensitive extension of classroom environments, fostering deeper engagement, social competence, and cognitive development in ways traditional technologies cannot fully replicate.

AI in Education doi: 10.3390/aieduc2030027

Authors: Markus H. Hefter Benjamin Paaßen Kirsten Berthold

Critically using generative AI (genAI) tools, such as comparing their outputs with other sources or the literature, is vital for students to avoid risks of reduced cognitive processing and inaccurate results. We propose two potential protective factors against these risks: knowledge about genAI and the disposition to engage in critical thinking encompassing epistemic orientation (i.e., a tendency to move away from more absolutist beliefs toward more evaluativist beliefs) and intellectual values (i.e., viewing intellectual engagement as worthwhile). Our correlational study with 67 undergraduate psychology students (59 female; mean age = 22.58) aimed to investigate how these factors predict students’ critical use of genAI. Regression and correlation analyses revealed that both knowledge about genAI and the disposition to engage in critical thinking predicted the critical use of genAI. Exploratory analyses revealed that students’ knowledge about genAI partly comprises misconceptions (i.e., incorrect knowledge with high confidence) and their critical genAI use correlates with need for cognition (NFC). These findings suggest that higher education institutions should implement interventions targeting conceptual misconceptions as well as epistemic orientation and intellectual values. Consequently, future research should experimentally evaluate such interventions to foster responsible AI engagement in higher education.

AI in Education doi: 10.3390/aieduc2030026

Authors: Demetrios T. Venetsanos

Assessment feedback on complex written reports remains one of the most persistent and resource-intensive challenges in higher education. However, no principled framework exists for deciding which feedback tasks might appropriately involve artificial intelligence and which must remain human responsibilities. This paper addresses that gap by proposing a tripartite feedback framework that distinguishes three analytically distinct levels: low-level structural and presentational feedback, intermediate-level factual content validation, and high-level critical evaluation and synthesis. Grounded in established feedback theory, including Hattie and Timperley’s feedback model and Boud and Molloy’s sustainable feedback design principles, the framework provides pedagogically justified criteria for allocating tasks between AI systems and human assessors, rather than automating whatever technology can technically perform. Five non-negotiable boundary principles govern any AI involvement at the intermediate level, preserving human oversight, academic accountability, and assessment integrity. This paper examines current technological capabilities and limitations at each level, proposes a phased implementation pathway with explicit human-in-the-loop requirements, and addresses implications for feedback literacy, student agency, equity, and security. A comprehensive mixed-methods evaluation design specifying the evidence required for empirical validation is also presented. The framework’s contribution lies not in prescriptive solutions but in providing structured categories, explicit boundary conditions, and validation criteria to guide context-sensitive institutional decision-making about AI integration in assessment.

AI in Education doi: 10.3390/aieduc2030025

Authors: Sean Mochocki Mark Reith Laurence D. Merkle Paolo J. Singh Jonathan Zemmer Ralucca Gera Gilbert Peterson John Jasper Brett Borghetti

Educators increasingly rely on e-learning to supplement traditional classroom learning. Personalized learning paths (PLPs) have emerged as one type of supplement. A PLP is a sequence of learning materials (LMs) and activities that are selected according to LM fitness for the learner’s preferences and ordered according to prerequisite relationships. A problem that consistently emerges in PLP research is the use of pseudo-scientific learning theories to inform PLP design. This research presents LM and PLP rubrics, derived from the Cognitive Theory of Multimedia Learning (an experimentally validated learning theory) as a foundation that informs the PLP design process. The LM and PLP rubrics are decomposed into two problem domains, which include selecting and sequencing LMs to create PLPs. These problem domains are supported by proofs of NP-completeness. Next, real-world data are presented and used to derive instances of these multi-objective problems, which are then solved using the Non-Dominated Sorting Genetic Algorithm, a simulated annealing algorithm, and a Random Hill Climber algorithm. These metaheuristics produce satisfactory results, with the rubric scores of 12 student profiles ranging from 3.19 to 3.54 on a four-point scale. The rubrics, data, and algorithms used in this paper are publicly available.

AI in Education doi: 10.3390/aieduc2030024

Authors: Mahlatse Given Sevhake Costa Hofisi

Artificial intelligence (AI) is reshaping higher education worldwide, raising tensions between efficiency, equity, and autonomy. This paper examines these dynamics in South Africa and Kenya, two countries that illustrate distinct governance frameworks and infrastructural challenges within African higher education. Using qualitative document analysis of policy frameworks, scholarly literature, and institutional reports, the study investigates how AI integration offers opportunities for personalized learning, streamlined administration, and enhanced educational quality, while simultaneously exposing risks related to algorithmic bias, digital divides, and the erosion of student agency. The findings show that AI can improve efficiency and enrich student experiences, but without ethical safeguards it may reinforce existing inequalities and diminish learner autonomy. Through situating the analysis in South Africa and Kenya, the paper contributes to debates on AI in education by demonstrating that efficiency gains must be balanced with equity and autonomy considerations. The study concludes with recommendations for educators and policymakers on responsible AI adoption, emphasizing ethical literacy, inclusive infrastructure, and participatory approaches to ensure that technological innovation enhances rather than undermines social justice in higher education.

AI in Education doi: 10.3390/aieduc2030023

Authors: Mireilla Bikanga Ada

This paper presents the Master’s Dissertation Marking Framework (MDMF), a longitudinally developed framework designed to support fairer and more transparent master’s dissertation assessment. The framework was developed through a multi-phase, design-based research framework, comprising a literature review, a survey and in-depth interviews (2022) conducted prior to the emergence of generative AI, and follow-up empirical phases between 2023 and 2025. Across these phases, the framework evolves from an initial focus on procedural consistency and bias mitigation to a broader sociotechnical perspective that incorporates ethical boundaries, professional judgement, institutional responsibility, and the disruptive effects of generative AI on assessment practice. The paper traces the progression of the framework to MDMF Version 5, the final iteration, which consolidates six interdependent components: ethical boundaries and AI policy clarity; fairness and equity issues; pre-marking tasks and calibration; marker allocation; marking processes, culture, and well-being; and technology as both enabler and disruptor. Drawing on empirical evidence from academic staff involved in MSc dissertation marking in the post-generative-AI context, the framework brings together these components to address both longstanding and emerging challenges in assessment. The findings demonstrate that fairness in dissertation marking cannot be achieved through procedural mechanisms or technological solutions alone. Instead, the MDMF supports fairer assessment by structuring human judgement, enabling calibration, and clarifying ethical boundaries in AI-mediated contexts. The framework offers a coherent yet adaptable model for institutions seeking to maintain valid and defensible assessment practices in the age of generative AI.

AI in Education doi: 10.3390/aieduc2030022

Authors: Javier Félix Merchán-Sánchez-Jara Sara González-Gutiérrez María Navarro-Cáceres Enrique González-Gutiérrez Juan José Navarro-Cáceres Carlos Sánchez García Javier Cruz-Rodríguez

The integration of artificial intelligence into music education represents a structural transformation that transcends the mere incorporation of technological tools to become central to the construction of knowledge. This paradigm shift moves pedagogy away from purely formalistic approaches toward an ecology of learning where technique, perception, and culture are dynamically intertwined. In this scenario, technology acts as a cognitive artifact capable of redistributing the student’s mental load, making abstract concepts such as harmonic structures or rhythmic hierarchies intelligible without the need for prior and exhaustive mastery of musical notation or advanced instrumental technique. By freeing up cognitive resources, it becomes easier for students to focus on higher-order processes such as critical listening, reflective interpretation, and informed aesthetic decision-making. This technological mediation intervenes directly in the continuum that links creation, the sound object, and the attribution of meaning, allowing the learning process to be transparent and decision-making to always be conscious. To this end, it is essential that these systems do not operate as opaque entities but are interpretable and put human agency at the center, ensuring that students maintain control over their own creative trajectory. By observing the decisions and strategies employed during sound experimentation, it is possible to map students’ cognitive development, valuing the learning process over the final result. Ultimately, creativity manifests itself as an essential dimension of understanding, where interaction with intelligent systems allows for the broadening of expressive horizons. However, this integration requires a critical and algorithmic perspective that avoids algorithmic biases and promotes learning that reconciles datacy with expressive sensitivity and the sociocultural context of each individual.

AI in Education doi: 10.3390/aieduc2020021

Authors: Ioana Diana Moldovanu Ecaterina Popa Simona Moldovanu

Background: The early identification of individuals with autism spectrum disorder (ASD) is crucial for their proper integration into the educational system and society. AI methods introduce novel approaches for the detection and classification of individuals diagnosed with ASD. Methods: We employed three custom-built Convolutional Neural Networks (CNNs) alongside two pretrained CNNs, specifically YOLO8 and ResNet18. The integrated Convolutional Block Attention Module (CBAM) was utilized to enhance feature representations for classifying individuals with ASD and non-ASD. Results: The results from the binary classification using the YOLO8-CBAM model demonstrated notable performance metrics: an accuracy of 77.6%, an F1-score of 72.6%, a Matthews Correlation Coefficient (MCC) of 60%, and an area under the curve (AUC) of 0.912. Conclusion: The backbone of the pretrained YOLO8-CBAM, enhanced by the integration of the CBAM after selected convolutional blocks, improved the feature refinement utilized in the classification process. Additionally, the gradient-weighted Class Activation Mapping (Grad-CAM) model provides interpretability by highlighting the regions that are most influential in distinguishing between individuals with ASD and those without.

AI in Education doi: 10.3390/aieduc2020020

Authors: Edwin Arthur Creely

The rapid integration of generative artificial intelligence (GenAI) into education across all sectors has prompted a proliferating body of scholarship addressing the ethical, social, and environmental implications of these technologies. This narrative literature review synthesises international empirical, conceptual, and policy literature published between 2022 and 2026 to trace the evolving story of ethical concerns surrounding GenAI in education. Drawing on the moral philosophy of consequentialism, particularly the utilitarian ethics of John Stuart Mill, the review analyses six interconnected domains of ethical concern: environmental sustainability and the carbon footprint of AI infrastructure; algorithmic bias, ideological encoding, and the reproduction of misinformation; user dependency and the erosion of learner agency; the displacement of critical and creative thinking; data privacy and surveillance; and the orientation of major GenAI platforms toward profit-driven and capitalistic outcomes. Unlike systematic reviews that privilege methodological replicability, this narrative review foregrounds interpretive synthesis, tracing how the ethical discourse has shifted from early alarm and prohibition toward more nuanced frameworks for responsible integration. The review identifies a consequentialist tension at the heart of the debate: while GenAI offers measurable benefits in personalisation, accessibility, and efficiency, these gains must be weighed against distributed harms that disproportionately affect vulnerable populations, the natural environment, and the epistemic foundations of education itself. The review concludes with a set of guidelines for the ethical use of GenAI in educational contexts, grounded in the literature synthesised in the article.

AI in Education doi: 10.3390/aieduc2020019

Authors: Panagiota Mantalia Charalampos M. Liapis Epameinondas Panagopoulos Vaggelis Kapoulas Michael Paraskevas

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.

AI in Education doi: 10.3390/aieduc2020018

Authors: Theodoros Vavouras Alexandros Gazis Vasileios Mellos Nikolaos Ntaoulas Nikos E. Mastorakis

This paper examines the intersection between digital learning environments and multilingual education policies, with a focus on the linguistic integration of migrant students in Europe. It explores how technology, particularly mobile-assisted learning, artificial intelligence, and immersive tools, can strengthen language acquisition and promote social inclusion. Drawing on European and Greek policy frameworks, the study shows how digital pedagogies operationalize multilingualism as both an educational objective and a social justice priority. Based on a qualitative review of contemporary research and institutional reports, the findings indicate that digitally enhanced learning environments act as catalysts for equity, intercultural dialogue, and active participation when supported by coherent pedagogical design. The paper concludes by outlining policy recommendations for the development of multilingual digital ecosystems that align technological innovation with democratic, inclusive, and human-centred education. Overall, the analysis highlights that technology-mediated multilingualism can effectively reinforce participation, inclusion, and linguistic integration when embedded within robust policy structures and sound pedagogical practice.

AI in Education doi: 10.3390/aieduc2020017

Authors: Yu Lu Avinash Shashikala Rajendra Jun Zhang Tian Zhao

Early identification of at-risk students is crucial for timely pedagogical intervention. Determining which assessments instructors should prioritize is complicated by the fact that different eXplainable-AI (XAI) methods can produce conflicting rankings for the same predictive model. We develop a framework combining a sequential GRU model with two complementary XAI techniques, Gradient SHAP (attribution) and DiCE (counterfactuals), and evaluate it in a foundational Data Structures and Algorithms course. The framework produces predictions and explanations for every prefix length throughout the semester and quantifies inter-method agreement and intra-method stability using three disagreement metrics. Intersecting the top-k features identified by both methods isolates a compact subset of assessments whose predictive role is confirmed across two fundamentally different explanation mechanisms. We interpret this cross-method agreement as a heuristic that increases confidence in identified features relative to single-method results, though not as evidence of causal validity. For individual students, the framework uses the intersection of the two types of explanations when it is non-empty; otherwise, the instructor chooses between SHAP’s diagnostic view and DiCE’s prescriptive view, with an optional check against the top-k list. The resulting guidance is less susceptible to method-specific biases than analyses relying on a single method.

AI in Education doi: 10.3390/aieduc2020016

Authors: Hyeji Kim Jongyoul Park

Emotion is increasingly incorporated into AI-supported feedback in education, yet less is known about how learners interpret emotion-related messages once they are presented. This paper reports an exploratory classroom-based study comparing three learner-facing strategies for presenting emotion-aware feedback: inference with explanation, inference without explanation, and deliberate non-inference. Using a Wizard-of-Oz procedure embedded in a web-based classroom activity, 78 undergraduate students completed a conceptual quiz, a brief reflection task, and an applied data-analysis task during a 90-min course session. Following the activity, participants evaluated the system on six 7-point Likert outcomes: Perceived Accuracy, Interpretability, Emotional Comfort, Willingness to Reuse, Perceived Usefulness, and Trust. Significant differences were observed across all six outcomes. Across every dimension, the same ordinal pattern emerged: feedback with explanation received the highest ratings, no inference occupied an intermediate position, and inference without explanation was rated lowest. Notably, deliberate non-inference was evaluated more favorably than unexplained inference across all six outcomes. These findings suggest that the learner-facing value of emotion-aware educational AI depends not only on whether emotion is inferred, but on how such inference is presented and contextualized. The study contributes classroom-based evidence that learner interpretation should be treated as an important criterion in evaluating emotion-aware educational AI and that deliberate non-inference can function as a legitimate response strategy when affective claims cannot be presented in an intelligible and contextually grounded way.

AI in Education doi: 10.3390/aieduc2020015

Authors: Ean Teng Khor Arunaksh Kapoor

The study examines how secondary school students interacted with an AI-powered educational chatbot, MyBotBuddy, while working on a programming task, and how observed dialogue structures were associated with differences in pre- to post-test performance. Fifty students first completed an unassisted pre-test, then attempted a chatbot-supported programming task, and finally completed an unassisted post-test. Based on score change, students were grouped into learning gain, no gain, and learning loss categories. Dialogue transcripts were analyzed using Epistemic Network Analysis to identify co-occurring discourse patterns, alongside descriptive sentiment analysis to characterize lexical tone. Students in the learning gain group showed more connected multi-turn patterns involving solution attempts, feedback uptake, knowledge-related contributions, and clarification following feedback. In contrast, the no gain and learning loss groups showed less iterative and less systematically connected interaction structures. Average sentiment polarity differed only slightly across groups and is interpreted cautiously because the dialogue was technical and programming focused. The findings are associational and exploratory rather than causal and suggest that learner engagement with a chatbot may be more informative than interaction frequency alone. We discuss implications for educational chatbot design, especially the potential value of multi-turn scaffolding and reflective prompting, while outlining the need for future validation, baseline-controlled analyses, and experimental work.

AI in Education doi: 10.3390/aieduc2020014

Authors: Yulia V. Sevryugina Kevyn Collins-Thompson Nils G. Walter

Rapid adoption of general-purpose generative AI (GenAI) tools, such as ChatGPT, is reshaping teaching, learning, and assessment in chemical education. In this study, we expanded the implementation of GenAI tools within an upper-level undergraduate biochemistry course, providing students access to four distinct platforms: commercial chatbots (ChatGPT and LearningClues) and in-house tools developed at the University of Michigan (U-M GPT and U-M Maizey). We analyzed student learning outcomes from GenAI-enhanced writing assignments using pre- and post-surveys. Our results show that integrating GenAI into biochemistry coursework promoted effective and responsible usage, enhanced students’ prompt literacy, built ethical awareness, and increased confidence in utilizing these tools. The study specifically examined factors influencing GenAI acceptance: familiarity, perceived usefulness, ease of use, and trust. Trust emerged as the most significant criterion, with a majority of students recommending in-house chatbots for future cohorts due to strong privacy and ethical standards. Over the last year, we observed a shift in student sentiment from excitement about efficiency to emerging concerns about creativity silencing. This highlights the importance of addressing both capabilities and risks of using AI-tools through teaching AI literacy.

AI in Education doi: 10.3390/aieduc2020013

Authors: Konstantinos Liakopoulos Anastasios Liapakis

Learning is fundamentally human, even as Artificial Intelligence (AI) challenges human exclusivity. AI, along with Virtual Reality (VR), emerges as a powerful tool that is set to transform higher education, the institutional embodiment of this pursuit at its highest level. These technologies offer the potential not to replace the human factor, but to enhance our ability to create more adaptive, immersive, and truly human-centric learning experiences, aligning powerfully with the emerging vision of Education 5.0, which emphasizes ethical, collaborative learning ecosystems. This research maps how AI and VR tools act as a disruptive force, examining additionally their capabilities and limitations. Moreover, it explores how AI and VR interact to overcome traditional pedagogy’s constraints, fostering environments where technology serves human learning goals. Employing a comprehensive two-month audit of over 60 AI, VR, and AI-VR hybrid tools, the study assesses their functionalities and properties such as technical complexity, cost structures, integration capabilities, and compliance with ethical standards. Findings reveal that AI and VR systems provide significant opportunities for the future of education by providing personalized and captivating environments that encourage experiential learning and improve student motivation across disciplines. Nonetheless, numerous challenges limit widespread adoption, such as advanced infrastructure requirements and strategic planning. By articulating a structured evaluative framework and highlighting emerging trends, this paper provides practical guidance for educational stakeholders seeking to select and implement AI and VR tools in higher education.

AI in Education doi: 10.3390/aieduc2020012

Authors: Matthew Montebello

The widespread adoption of generative artificial intelligence in higher education has intensified debates around assessment, authorship, and academic integrity. This paper argues that such debates obscure a more fundamental pedagogical shift, namely, the decoupling of assessment performance from cognitive engagement. Historically, assessment functioned not only as a measure of learning, but also as a structural mechanism that implicitly enforced cognitive engagement. With the advent of GenAI, learners can increasingly produce assessment outputs without necessarily engaging in the cognitive processes traditionally associated with learning. As a result, cognitive engagement has shifted from being a pedagogical necessity to an intentional learner choice. This paper conceptualises this shift as the cognitive engagement gap, wherein successful assessment completion no longer reliably indicates learning or epistemic development. Through a theory-informed conceptual analysis, the paper examines how GenAI reconfigures learning processes, challenges the validity of assessment as a proxy for learning, and exposes long-standing assumptions embedded in assessment-centred pedagogies. In response, the paper proposes a Cognitive Engagement-Centred Assessment (CECA) framework, offering principled guidance for designing assessment that foregrounds cognitive processes, metacognition, and learning assurance in AI-mediated environments. The paper concludes by positioning GenAI not as a threat to assessment, but as a catalyst for more intentional, transparent, and learning-centred pedagogical design.

AI in Education doi: 10.3390/aieduc2020011

Authors: Md Zainal Abedin Ahmad Hayajneh Bijan Raahemi

Generative Artificial Intelligence (GenAI) is transforming higher education in terms of pedagogy, student involvement, and academic management. This systematic literature review examines 30 peer-reviewed articles published from 2019 to 2025, adhering to PRISMA 2020 and Kitchenham’s methodologies. Descriptive and thematic analyses highlight five opportunities: (a) tailored and adaptive education; (b) deliberate fostering of critical thinking; (c) enhanced accessibility for varied learners; (d) teaching innovation via multimodal content development and feedback; and (e) collaborative methods that regard AI as a co-teacher. Four ongoing challenge categories also surface: (a) risks to academic integrity; (b) excessive dependence on GenAI that may hinder learner independence; (c) inconsistent faculty preparedness and change-management abilities; and (d) differences in infrastructure and policy both regionally and globally. Intersecting ethical issues, such as data privacy, algorithmic bias, transparency, and accountability, highlight the necessity for governance that aligns with institutional risk and reflects societal values. Analyzing the recent literature, this systematic review offers four contributions: (a) a recommendation model for responsible GenAI implementation in higher education institutions; (b) a framework for sustainable integration of GenAI; (c) a highlight of the future research recommendations; and (d) an integrated policy and pedagogical recommendations roadmap. These models emphasize the integration of AI literacy, ethical considerations, and critical thinking goals into educational programs. The review advocates for a strategic, stakeholder-focused approach to implementation that enhances rather than replaces human instruction, thus connecting GenAI’s educational potential with ethical, context-aware avenues for institutional transformation.

AI in Education doi: 10.3390/aieduc2020010

Authors: Jimmie Manning Md Mahmudur Rahman Ngozi Oguejiofor

Generative artificial intelligence in higher education is typically framed as either a student productivity tool or an institutional disruption. This agenda-setting essay advances a third position: mobile generative AI functions as relational infrastructure—a persistent communicative presence that mediates identity, meaning-making, and belonging during institutional transition. Focusing on international graduate student onboarding, we abductively “think through” two complementary theoretical lenses. Constitutive Artificial Intelligence Identity Theory (CAIIT) conceptualizes AI as a co-constitutive participant in identity formation through recursive communicative feedback loops. Language Convergence/Meaning Divergence (LC/MD) theory explains how shared institutional language masks interpretive gaps across intercultural and bureaucratic contexts. Reading narrative vignettes through these frameworks, we argue that generative AI is neither simple curricular tool nor personal aid, but both relational and organizational infrastructure, redistributing translational, emotional, and interpretive labor in higher education. We outline four design principles for AI-integrated onboarding: distinguish communicative scaffolding from cognitive replacement; design systems that assume meaning divergence; center equity in AI-mediated transitions; and anticipate ethical risk. Reframing AI as relational infrastructure shifts AI-in-education research toward relational accountability and institutional care.

AI in Education doi: 10.3390/aieduc2020009

Authors: Raffaella Borasi Jonathan Herington Karen J. DeAngelis Yu Jung Han Sharon Mason Patricia Vaughan-Brogan David E. Miller

This article contributes to current debates around the ethics of using AI in K-12 education by extending an ethical framework based on the constructs of wellbeing, autonomy and justice to examine how AI may differentially impact specific stakeholders. Data about K-12 building and district leaders’ perceptions of AI risks were collected during the 2023–24 school year in Western New York as part of an exploratory sequential mixed methods study, which included semi-structured interviews with a diverse group of 36 K-12 leaders, followed by a survey (n = 160). Survey findings confirm K-12 leaders’ widespread recognition, although at varying levels of concern, of AI risks related to (a) students cheating, (b) students’ other questionable AI uses, (c) educators’ questionable AI uses, (d) increasing inequities due to AI, (e) cybersecurity and privacy breaches, and to a much lesser extent, the (f) potential for job replacement. The ethical analysis reveals major differences in the implications of each of these six kinds of AI risk for the wellbeing, autonomy, and justice of K-12 educators, K-12 students, and society, respectively, as well as tensions between competing needs and values, which in turn call for risk-specific strategies as well as inevitable tradeoffs. A comparison with a study of musicians’ perceptions of AI using the same ethical framework reveals interesting similarities and differences in ethical concerns about AI in different fields, suggesting the value of more cross-disciplinary studies.

AI in Education doi: 10.3390/aieduc2020008

Authors: Cristo Leon Michelle Kudelka

Generative AI systems increasingly mediate how students retrieve literature and generate citations, shifting methodological rigor toward the maintenance of an auditable evidence trail. This study audits the search stage of AI-assisted literature review work, focusing on retrieval performance and citation traceability rather than downstream screening or synthesis. Four widely accessible tools were compared across two retrieval postures, and Boolean queries were executed against Scopus and evaluated against a DOI-verified librarian baseline built from Scopus, Web of Science, and Google Scholar. Using a canonical prompt and a bounded top-k capture rule (k = 20), each bibliographic record was evaluated for DOI traceability, DOI resolution integrity, metadata accuracy, and run-to-run drift. Records were screened through staged title/abstract and full-text eligibility review, and the final set included 37 studies after quality appraisal was 37 studies. Across sixteen audit runs, natural-language prompting frequently produced under-target yields, recurrent integrity failures, and low overlap with the librarian benchmark. Boolean translation improved run completion and increased the proportion of auditable records, but reproducibility remained unstable across repeated runs. These findings show that correctness at the record level does not ensure stability at the evidence-set level. Limitations include the bounded tool set, the search-stage focus, and the absence of downstream screening or synthesis evaluation. Retrieval posture, therefore, emerges as a practical governance lever for AI-assisted literature review workflows and supports the use of a student-facing verification checklist anchored in DOI verification and transparent protocol capture. This research received no external funding. OSF registration: Open Science Framework, 10.17605/OSF.IO/U8NHT. The manuscript reports the final included set as n = 37, states no external funding, and lists the OSF registration DOI.

AI in Education doi: 10.3390/aieduc2010007

Authors: Peter Tiernan Enda Donlon Mahmoud Hamash James Lovatt

Generative artificial intelligence (GenAI) has rapidly emerged as a transformative educational technology, raising questions about how educators and pre-service teachers critically engage with AI-produced content. This case study investigates how WebQuests, a long-established, inquiry-based pedagogical model, can foster critical engagement with GenAI tools. Situated within an initial teacher education programme, a WebQuest, incorporating GenAI sources, was implemented with 24 pre-service language teachers, who engaged with curated resources alongside ChatGPT and Copilot to produce infographics for secondary school audiences. Data were collected through semi-structured interviews and were analysed using Braun and Clarke’s thematic analysis. Findings indicate that scaffolded engagement with GenAI encouraged participants to compare AI-generated outputs with trusted sources, critically evaluate accuracy and reliability, and reflect on integration into their future practice. Whilst pre-service teachers valued GenAI’s accessibility and efficiency, they expressed concerns about clarity, verbosity, and trustworthiness. The WebQuest model effectively supported synthesis of multiple information sources, fostering functional AI engagement and critical evaluation of its affordances and limitations. This case study concludes that integrating GenAI within structured, inquiry-based pedagogies advances digital and AI literacy in initial teacher education, whilst highlighting the need for institutional guidance, professional development, and further research in this area.

AI in Education doi: 10.3390/aieduc2010006

Authors: Riyan Hasan Yupei Zhang

Modeling the temporal dynamics of student learning is a central goal in educational data mining. Deep Knowledge Tracing (DKT) has emerged as a key approach, yet existing models are highly sensitive to out-of-distribution (OOD) inputs, such as those arising from curriculum changes, new assessment formats, or behavioral noise, which severely degrade predictive reliability. To address this challenge, we propose Energy-Based Out-of-Distribution Deep Knowledge Tracing (EB-OOD DKT), a unified framework that integrates energy-based uncertainty estimation and contrastive representation learning within a transformer-based DKT architecture. The model computes energy scores via the negative log-sum-exponential of prediction logits, serving as confidence indicators for detecting OOD inputs during inference. Additionally, an InfoNCE-based contrastive loss enhances representation robustness by aligning in-distribution samples and separating OOD cases in latent space. Temporal and behavioral context features, such as normalized response intervals and cumulative attempt counts, are incorporated to enrich cognitive-behavioral modeling. Experiments on four public educational datasets demonstrate consistent improvements in prediction accuracy and OOD detection. EB-OOD DKT provides a promising approach for more reliable student modeling across educational platforms with different content distributions.

AI in Education doi: 10.3390/aieduc2010005

Authors: Olivia G. Stewart

As artificial intelligence (AI) becomes increasingly integrated into higher education, instructors and institutions face urgent questions about its implications for teaching, learning, and scholarly practice as well as power, agency, and access. This study draws on a critical AI media literacy framework to analyze user-generated discussions in the two largest higher education subreddits on Reddit.com. Through thematic content analysis, I explore faculty perceptions, pedagogical tensions, and imaginative possibilities surrounding AI’s academic role in shaping the current and future landscape of higher education. Findings reveal that discussions of student cheating, AI policies, writing practices, and faculty labor are not merely technical debates but sites where surveillance regimes, accountability structures, and academic precarity are negotiated in real time. Ultimately, I argue that AI in higher education is not simply a technological shift but a structural transformation requiring deliberate, critically informed governance grounded in equity and human agency.

AI in Education doi: 10.3390/aieduc2010004

Authors: Michael Pin-Chuan Lin Jing-Yuan Huang Daniel H. Chang Gerald Tembrevilla G. Michael Bowen Eric Poitras Vasudevan Janarthanan Jeeho Ryoo

Open-source large language models (LLMs) are increasingly explored in educational contexts due to their transparency, adaptability, and alignment with institutional governance and equity considerations. Despite growing interest, empirical research on how open-source LLMs are deployed in education and what evidence currently supports their integration remains limited and fragmented. This paper presents a state-of-the-art narrative review of peer-reviewed, human empirical studies examining the use of open-source LLMs in education. Guided by three questions, the review synthesizes how open-source LLMs are deployed across instructional contexts, what learner-related evidence is reported, and how teachers engage in human–AI collaboration. The reviewed literature is concentrated in higher education, particularly within computer science and programming domains, with applications focused on post-class tutoring, guidance, and formative feedback. Learner perceptions are generally positive, but evidence linking open-source LLM use to measurable learning outcomes remains emerging and inconsistent. Through interpretive synthesis, the review articulates a four-role model—Designer, Facilitator, Monitor, and Evaluator—that captures how teacher agency is enacted across AI-supported instructional workflows. This review maps recurring orchestration dimensions, decision points, and tensions that characterize early implementations, and it proposes a minimal orchestration reporting scaffold (configuration, boundaries, logging, adjudication) intended to support auditability and cross-study comparison as the empirical base develops.

AI in Education doi: 10.3390/aieduc2010003

Authors: Zoi A. Traga Philippakos

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

AI in Education doi: 10.3390/aieduc2010002

Authors: Georgios P. Georgiou

This study provides direct evidence from university professors’ experiences regarding the key features they use to identify artificial intelligence (AI)–generated texts and ranks these features by their perceived importance. The research was conducted in two phases. In Phase 1, online interviews were used to identify the most salient features professors reported using to detect AI-generated texts. In Phase 2, an online survey asked professors to rate the extent to which each identified feature contributes to the successful detection of AI-generated text. The interview data yielded seven features that professors reported using when they suspected a text was AI-generated. Survey ratings varied across features, with hallucinated facts or explanations, nonexistent sources, and the absence of language errors receiving the highest mean ratings in this sample. The use of difficult words received the lowest mean rating. These results have important pedagogical implications, as they can inform the development of more effective detection tools and guide the design of academic integrity policies and instructional strategies to address the challenges posed by AI-generated content.

AI in Education doi: 10.3390/aieduc2010001

Authors: Kristine Zlatkovic Pavlo Antonenko Do Hyong Koh Poorya Shidfar

Understanding how learners process data visualizations with seductive details is essential for improving comprehension and engagement. This study examined the influence of task-relevant and task-irrelevant seductive details on attentional distribution and comprehension in the context of data story learning, using COVID-19 data visualizations as experimental materials. A gaze-based methodology was applied, using eye-movement data and saliency maps to visualize learners’ attentional patterns while processing bar graphs with varying embellishments. Results showed that task-relevant seductive details supported comprehension for learners with higher visuospatial abilities by guiding attention toward textual information, while task-irrelevant details hindered comprehension, particularly for those with lower visuospatial abilities who focused disproportionately on visual elements. Working memory capacity emerged as a significant predictor of attentional distribution. Additionally, repeated exposure to data visualizations enhanced participants’ ability to recognize visualization types, improving efficiency and reducing reliance on legends and supplementary text. Overall, this study highlights the cognitive mechanisms underlying visualization processing in data story learning and provides practical implications for education, human–computer interaction, and adaptive technology design, emphasizing the importance of tailoring visualization strategies to individual learner differences.

AI in Education doi: 10.3390/aieduc1010005

Authors: Hongwei Wang Deepak Ganta Maria Vasquez Khaled Enab

Statistical education is a crucial yet often overlooked aspect of AI in higher education. However, traditional approaches usually focus heavily on procedural knowledge, leaving students anxious about statistics and less confident in applying concepts to real-world problems. This study examines a method that enhances statistical learning outcomes by integrating data visualization and gamification strategies. Students were randomly assigned to either a control group (CG) or an intervention group (IG), and each group was further divided into teams. The curriculum was enhanced in a college statistics course offered for both engineering and math majors. IG students applied data visualization and gamification in a hands-on group project aimed at solving a real-world problem and competed as they presented their results. The effectiveness of this approach was assessed through statistical analyses comparing the performance of IG and CG in surveys, final grades, and project grades. The results from evaluation methods indicated that IG students outperformed CG students, demonstrating a positive impact of gamification on statistics education.

AI in Education doi: 10.3390/aieduc1010004

Authors: Kamalanathan Kajan Wenyuan Shi Dariusz Wanatowski

We surveyed 297 STEM undergraduates at a single English-medium Sino–UK joint institution to document perceptions of AI chatbots for learning. Students reported high willingness to adopt AI chatbots (78%; 95% CI: 73.1–82.4) alongside concerns about over-reliance (67%; 95% CI: 61.4–72.1), content quality (52%; 95% CI: 46.2–57.5), and reduced human interaction (42%; 95% CI: 36.5–47.8). Over half (52%; 95% CI: 46.3–57.7) requested language/terminology support features, whereas only 16.8% reported language-related barriers. We attempted exploratory factor analysis and k-means clustering, but neither met the inclusion criteria; therefore, we report item-level frequencies only. The findings are descriptive and not generalisable (53% first-year, 80% male convenience sample). These patterns generate testable hypotheses about verification scaffolds, language support utility, and human–AI balance that warrant investigation through controlled studies.

AI in Education doi: 10.3390/aieduc1010003

Authors: Ana Luize Bertoncini Raul Matsushita Sergio Da Silva

This study examines how cognitive biases may shape ethical decision-making in AI-mediated environments, particularly within education and research. As AI tools increasingly influence human judgment, biases such as normalization, complacency, rationalization, and authority bias can lead to ethical lapses, including academic misconduct, uncritical reliance on AI-generated content, and acceptance of misinformation. To explore these dynamics, we developed an LLM-generated synthetic behavior estimation framework that modeled six decision-making scenarios with probabilistic representations of key cognitive biases. The scenarios addressed issues ranging from loss of human agency to biased evaluations and homogenization of thought. Statistical summaries of the synthetic dataset indicated that 71% of agents engaged in unethical behavior influenced by biases like normalization and complacency, 78% relied on AI outputs without scrutiny due to automation and authority biases, and misinformation was accepted in 65% of cases, largely driven by projection and authority biases. These statistics are descriptive of this synthetic dataset only and are not intended as inferential claims about real-world populations. The findings nevertheless suggest the potential value of targeted interventions—such as AI literacy programs, systematic bias audits, and equitable access to AI tools—to promote responsible AI use. As a proof-of-concept, the framework offers controlled exploratory insights, but all reported outcomes reflect text-based pattern generation by an LLM rather than observed human behavior. Future research should validate and extend these findings with longitudinal and field data.

AI in Education doi: 10.3390/aieduc1010002

Authors: Brady Lund Nishith Reddy Mannuru Zoë Abbie Teel Tae Hee Lee Nathanlie Jugan Ortega Sara Simmons Evelyn Ward

The rise of generative AI in higher education has disrupted our traditional understandings of academic integrity, moving our focus from clear-cut infractions to evolving ethical judgment. In this study, a survey of 401 students from major U.S. universities provides insight into how beliefs, behaviors, and policy awareness intersect in shaping how students interact with AI-assisted writing. The findings indicate that students’ ethical beliefs—not institutional policies—are the strongest predictors of perceived misconduct and actual AI use in writing. Policy awareness was found to have no significant effect on ethical judgments or behavior. Instead, students who believe AI writing is cheating were found to be substantially less likely to view it as ethical or engage with it. These findings suggest that many students do not treat AI use in learning activities as an extension of conventional cheating (e.g., plagiarism), but rather as a distinct category of academic conduct/misconduct. Rather than using punitive models to attempt to punish students for using AI, this study suggests that education about AI ethics and the risk of AI overreliance may prove more successful for curbing unethical AI use in higher education.

AI in Education doi: 10.3390/aieduc1010001

Authors: Savvas A. Chatzichristofis

The rapid expansion of Artificial Intelligence in Education (AIED) has created both remarkable opportunities and pressing concerns. Applications of intelligent tutoring systems, learning analytics, generative models, and educational robotics illustrate the transformative momentum of the field, yet they also raise fundamental questions regarding ethics, equity, and sustainability. The mission of AI in Education (MDPI) is to provide a rigorous, interdisciplinary, and inclusive platform where these debates can unfold. The journal bridges pedagogy and engineering, welcomes both empirical evidence of positive impacts and critical examinations of systemic risks, and advances responsible innovation in real educational settings. By integrating methodological standards, governance perspectives, and pedagogical ethics, including teacher-centered validation approaches, AI in Education positions itself as a space for constructive dialogue that values both enthusiasm and critique. Above all, the journal is committed to a human-centered vision for AIED, so that innovation in classrooms remains grounded in care, responsibility, and educational purpose.