AI in Education

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. 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

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article