Search Results (92)

Background: Teachers routinely encounter children whose behaviour reflects emotional distress and dysregulation, yet they have limited opportunities to practise the relational skills required to respond effectively. These challenges are particularly pronounced in Alternative Provision (AP), which serves children who frequently present with histories of trauma, neurodevelopmental differences, and complex emotional and behavioural needs. Mentalization, the capacity to understand behaviour in terms of underlying mental states, is central to effective relational practice in such contexts. Conversational Artificial Intelligence (AI) may offer a scalable means of supporting this form of skills development, but its feasibility as a teacher-training modality remains largely unexplored. Methods: This mixed-methods proof-of-concept feasibility study evaluated MentaClassAI, a novel AI-based training tool in which educators engaged in simulated voice conversations with AI child characters portraying classroom dysregulation and subsequently received individualised, mentalization-informed feedback. Eleven staff members from a single AP school (four teachers and seven teaching assistants) completed a single training session and were allocated to either a psychoeducation video condition (n = 6) or a no-video condition (n = 5). The video condition received a brief introduction to mentalization and epistemic trust prior to engaging with the simulation. Pre- and post-engagement measures included the Reflective Functioning Questionnaire (RFQ-8) and a Teacher Self-Efficacy Scale. Post-engagement measures included an 18-item acceptability questionnaire, a Technology Acceptance Model scale, and open-ended questions analysed using thematic analysis. Results: Acceptability was high, with 84.8% of questionnaire responses falling within the positive range (overall M = 5.60/7). Feedback accuracy (M = 6.55) and clarity (M = 6.36) received the highest ratings. Participants reported higher teacher self-efficacy after the session than before (d = 1.20, p = 0.003), with 10 of 11 participants demonstrating improvement. Self-reported hypomentalizing was lower after the session (d = −0.86, p = 0.017). Between-condition differences (video versus no-video) were not statistically significant. The video condition scored numerically higher on the directional indicators. Qualitative analysis identified five themes: the value of consequence-free rehearsal; the specificity and usefulness of feedback; appreciation of the focus on the child’s emotional experience; limitations in the ecological diversity of AI child characters; and a desire for more naturalistic interaction. Conclusions: These findings provide preliminary support for the feasibility and acceptability of AI-based mentalization practice for AP staff. The principal value of the tool appears to lie not only in the simulation itself but in the quality of the reflective feedback generated. Although based on a small sample, the observed pre–post changes provide an encouraging signal that may justify a controlled trial. The contribution of pre-session psychoeducation to training outcomes remains an important question for future research. Full article

Cognitive offloading—delegating cognitive work to external tools—is basic to human cognition, but generative AI (GenAI) amplifies it radically: entire cognitive products can now be produced on request. This sharpens a question about agency over one’s own thinking: when cognition is habitually offloaded, does its product still feel like one’s own? We surveyed 239 pre-service teachers, operationalizing the felt loss of cognitive ownership as concern over the erosion of teaching-design subjectivity (TSC)—the metacognitive appraisal that AI-assisted work is not genuinely one’s own and that independent capacity is declining. Guided by the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, we tested whether AI anxiety/fear of missing out (affective) and impulsivity (self-regulatory) relate to this concern through behavioral GenAI dependency—habitual offloading. In a structural equation model with bias-corrected bootstrapping, dependency strongly predicted TSC and partially mediated the effect of AI anxiety; impulsivity raised dependency but showed a suppression pattern (a positive indirect effect offset by a null total effect), and the model explained 35% of the variance in the concern. Habitual offloading—that is, GenAI dependency—rather than generic AI use, accompanies the metacognitive loss of cognitive ownership. Full article

The excessive cost and complexity of Industry 4.0 laboratory infrastructure limit the adoption of Digital Twin concepts in engineering education. This paper proposes a low-cost Digital Twin framework for sustainable manufacturing education integrating SAP NetWeaver, Node-RED, and AI-based decision support. The framework adopts a layered architecture that connects PLC-based simulation, IoT middleware, enterprise resource planning systems, and intelligent decision-making components. Node-RED enables real-time data exchange, while SAP NetWeaver provides enterprise-level integration through OData services. An AI module supports decision-making for production and inventory management. The framework has been validated through the implementation of a functional prototype and a series of end-to-end integration tests that evaluated communication reliability, system interoperability, API response performance, and AI-assisted decision-support capabilities. Competency-based mapping aligns the framework with Industry 4.0 engineering skills, supporting its use in academic environments. A sustainability assessment highlights reductions in infrastructure cost, energy consumption, and resource usage compared to traditional laboratory approaches. The results indicate that the framework has the potential to provide a scalable and accessible solution for teaching Digital Twin concepts, pending further classroom-based validation. Full article

With the rapid advancement of educational digitalization and generative artificial intelligence, knowledge graphs and AI agents have emerged as key technologies supporting smart higher education. To address challenges in current AI-assisted teaching, including insufficient support from structured knowledge systems, inadequate coordination across teaching processes, and delayed evaluation and feedback, this study proposes a collaborative framework integrating knowledge graphs and AI agents within an integrated teaching platform, and illustrates its integrated operational mechanism for knowledge organization, learning support, learning analytics, and teaching evaluation. Using the core course Nautical Navigation in the Navigation Technology Specialty at WHUT as a case study, the framework was implemented on the Chaoxing Smart Course Platform and applied to 459 students across two cohorts. The implementation achieved knowledge structuring and learning process visualization. The constructed course knowledge graph includes 336 knowledge points and more than 2700 associated learning resources and assessment items, while 30 instructional AI agents were developed and deployed to support different teaching and learning scenarios. The results indicate that the framework improves course knowledge organization, enhances student engagement and self-directed learning, enables visualization of learning processes and precision in teaching evaluation, and promotes a shift from experience-based to data-driven instructional decision-making. Compared with the previous cohort, students’ average daily learning time increased from 564 s to 618 s, participation rates in chapter quizzes, group discussions, and assignment completion all exceeded 90%, and more than 80% of respondents expressed willingness to continue using this learning model. The study provides a practical reference for AI-enabled teaching reform and digital transformation in higher education. Full article

Drawing on Self-Determination Theory (SDT) and the Classroom Assessment Scoring System (CLASS) framework, this study tested a moderated mediation model in which two dimensions of GenAI-assisted teaching predict children’s creative learning engagement through autonomous motivation. Cross-sectional survey data collected from 486 preschool teachers across 52 kindergartens in three Chinese provinces were analysed with structural equation modelling and bias-corrected bootstrap procedures. AI-enhanced interactive pedagogy was positively associated with autonomous motivation, whereas AI-dependent passive instruction was negatively associated with it; only passive instruction exhibited a direct negative link with creative learning engagement. Autonomous motivation fully mediated the interactive pedagogy pathway and partially mediated the passive instruction pathway. Perceived teacher–child interaction quality significantly moderated the passive instruction to motivation path, attenuating the indirect association to non-significance at high levels of perceived interaction quality. Because all constructs were assessed via teacher self-report at a single time point, the observed associations reflect teachers’ perceptions rather than objectively measured behaviours, and causal inference is not warranted. The study offers theoretical and practical insights for designing developmentally appropriate, teacher-scaffolded GenAI integration in preschool education. Full article

Generative AI (GenAI) has transformed L2 writing, producing human-like prose but often impersonal feedback. This study explores the potential of GenAI–human collaborative feedback, focusing on the first author’s experience as a teaching assistant in a Hong Kong public university’s Bachelor of Education (English language track). Grounded in Ecological Languaging Competencies (ELC) and its affordance framework, this study employs an ethnographic approach informed by narrative inquiry and phenomenology. Data were drawn from Zoom tutoring sessions incorporating interview-style questions to investigate participants’ perspectives and experiences, GenAI-student conversation logs, and final assignments in order to analyze two multilingual students’ GenAI–human-mediated L2 writing processes. Findings are organized around three ELC-informed themes: (1) whole-body sense-making and the meshing of first-order languaging and second-order language; (2) individual languaging agency within a distributed ecosystem; and (3) environmental affordances and functional fit. In both cases, GenAI demonstrates consistent limitations in facilitating the situated, embodied, and affectively attuned dimensions of languaging that effective L2 writing entails. This study makes two contributions: it extends ELC’s affordance network to tertiary-level GenAI–human-mediated L2 writing, and it reconceptualizes writerly authorship as a distributed yet agentively orchestrated practice. Moreover, co-agentic GenAI–human feedback foregrounds ecological embeddedness, writerly agency, and ethical GenAI integration. Full article

While computational thinking (CT) is increasingly vital in K-12 education, teaching it through text-based coding remains challenging for teachers. To address this gap, this study presents and evaluates a self-paced professional development (PD) module, “Let’s Code with GenAI,” created for K-12 teachers to enhance text-based coding and CT. Using a one-group pretest-posttest design, 34 pre-/in-service teachers completed the module in 2025, engaging with instructional videos and hands-on coding activities using Micro:bit and MakeCode (v11.3.22), with a GenAI assistant providing explanations and debugging support. Pre- and post-intervention data were collected using the Teacher Beliefs about Coding and Computational Thinking (TBaCCT) scale and a coding/CT assessment. Posttest scores were higher than pretest scores on teaching efficacy and value beliefs (p < 0.001 for both), as well as in coding self-efficacy (p < 0.001) and CT self-efficacy (p = 0.015). Coding/CT assessment scores were also higher at posttest (p = 0.040). No statistically significant correlations were found between self-efficacy and performance measures. Overall, the findings offer preliminary insights into participants’ post-intervention outcomes in a GenAI-supported, self-paced PD module, including self-efficacy, value beliefs, and coding/CT performance, while underscoring the need for future controlled studies. Full article

The use of Artificial Intelligence (AI) in Formative and Shared Assessment (F&SA) processes refers to the application of AI-based technologies to support formative and continuous assessment in Higher Education (HE). F&SA systems involve the ongoing monitoring of students’ learning, the provision of feedback that enables them to regulate and improve their performance, and the collection of information that informs the continuous improvement of teaching practice. In this context, AI can serve a dual purpose: when orientated towards students, it enhances learning outcomes; when directed at educators, it supports the development of their pedagogical expertise through tools designed to assist in the creation of assessment instruments, the generation of automated feedback, the analysis of learning data, and the design of simulation environments that foster the development of professional competencies. The integration of AI into F&SA practices holds considerable potential to transform traditional assessment approaches by enabling more personalised, adaptive, and timely feedback for both students and educators. In this shared assessment framework, students may likewise draw on AI applications to support specific dimensions of their learning, including academic writing, knowledge organisation, and the generation of educational content, thereby becoming active participants in their own assessment processes. However, the incorporation of AI into F&SA also requires careful consideration of the pedagogical, ethical, and institutional challenges it entails, particularly those related to academic integrity, cognitive offloading, and the responsible use of AI tools. It is therefore essential to promote AI literacy in HE among both faculty members and students, fostering a critical and informed engagement with these technologies that ensures the pedagogical relationship, along with the shared, formative nature of assessment, remains at the core of meaningful learning processes. Full article

Artificial intelligence (AI)-based support systems are transforming the educational landscape by enhancing teaching efficiency, personalized learning, and accessibility. Despite rapid technological progress, educational institutions face persistent challenges such as unequal access to quality learning resources, limited teacher support, and the need for individualized student engagement. These issues hinder effective learning outcomes and inclusivity in modern classrooms. This study presents a comprehensive literature review and a methodology grounded in constructivist learning theory to develop an AI-based educational support framework. The study is situated within the context of a higher education course integrating AI-supported learning. The proposed framework is developed by synthesizing theoretical and empirical evidence and is subsequently evaluated by experts in educational technology and artificial intelligence. Data are collected through structured expert questionnaires and qualitative feedback. Quantitative data are analyzed using descriptive statistics, while qualitative responses are examined through thematic analysis to inform framework refinement. The study adheres to established ethical principles, including informed consent, voluntary participation, confidentiality, anonymity, and secure data management. Moreover, the paper explores the design and implementation of sustainable and AI-based educational support systems that address these challenges through intelligent tutoring, adaptive learning analytics, and automated feedback mechanisms. By integrating natural language processing, machine learning, and predictive modelling, the proposed framework provides real-time assistance to educators and learners, fostering data-driven decision-making and inclusive pedagogy. Qualitative expert evaluation suggests that an AI-based educational support framework has the potential to improve teaching support, learner engagement, and personalized learning while providing a scalable and equitable approach for higher education. Full article

Large language models (LLMs) have introduced new challenges to academic integrity, particularly regarding the appropriation of AI-generated outputs as original human authorship and the difficulty of verifying independent work. While some universities and academic publishers increasingly require explicit disclosure of the use of artificial intelligence (AI), the scope and implementation of these requirements remain inconsistent. This paper examines current practices related to AI use, focusing on LLM-based ghostwriting and the reliability of disclosed interactions as evidence of authentic use. The study includes an experimental component involving AI-assisted essay generation, highlighting practical and ethical dilemmas associated with academic integrity. It further explores the possibility of mimicking authentic interactions, which raises concerns about the effectiveness of current approaches. To investigate these questions, a survey was conducted among teaching staff at the Faculty of Computer Science and Engineering (FCSE) in Skopje to assess their ability to identify AI-generated essays and their trust in disclosed interactions. Among the 28 respondents, a majority (82.14%) indicated that it is possible to identify AI-generated content based solely on language style, while 64.29% reported detecting linguistic inconsistencies that could result from the use of LLMs. Despite noticing AI-related linguistic markers, only 53.57% concluded that the essay was not human-written. This view was shared by just 27.27% of assistants, compared to 70.59% of professors, whose extensive experience appeared to help them recognize that a substantial portion of the text had been AI-generated. The findings are discussed in the context of teaching experience and existing policies, leading to recommendations for improving student assessment and strengthening the ethical use of generative artificial intelligence (GenAI). Full article

Artificial intelligence is increasingly shaping educational practices; however, its use by university supervisors (USs) to provide feedback to preservice teachers (PSTs) during field experiences remains underexplored. This collaborative action research study examined USs’ perceptions of implementing a generative AI (GenAI)-supported feedback protocol during classroom observations. Supervisors used a researcher-developed protocol in which observational notes, lesson objectives, and teaching competencies were input into ChatGPT to generate draft feedback for post-observation conferences. Data sources included annotated protocol documents, individual interviews, and a focus group, supplemented by existing PST interview data. Inductive thematic analysis indicated that GenAI supported alignment with teaching competencies and enhanced the structure and specificity of feedback. At the same time, findings highlighted important limitations, as AI-assisted feedback required careful human interpretation to ensure contextual accuracy and relevance. Supervisors noted that, while GenAI provided objective-aligned instructional guidance, it did not fully capture the complexity of classroom interactions. These findings suggest that GenAI functions as a support tool rather than an autonomous feedback mechanism, underscoring the importance of human judgment in AI-assisted supervisory feedback within teacher education. Full article

To increase teaching effectiveness in the context of veterinary ultrasonography, we employed a flipped classroom combined with a mind mapping methodology for ultrasound use in animal husbandry education. A total of 61 students (experimental class, n = 30; control class, n = 31) participated in this study. Students in the experimental class prepared and presented group topics over a two-week pre-class period and subsequently engaged in question-and-answer sessions. Next, mind maps were generated with the assistance of AI tools, thus establishing a systematic knowledge framework. An in-class test (the Mann–Whitney U test) was administered after the lesson, and a 6-item questionnaire was designed to evaluate students’ levels of satisfaction. The combination of a flipped classroom with mind mapping was associated with a rightward shift in the score distribution, with more students in the experimental group scoring 90–100 (7 vs. 2). The Mann–Whitney U test revealed no statistically significant differences between the groups (U = 361.0, p = 0.135, r = 0.22), although the experimental group had a higher mean score (83.53 vs. 79.74). Most students endorsed the teaching model. The observed trends and positive student feedback from this preliminary assessment suggested that this approach may be a promising method for veterinary ultrasound education. Full article

The rapid integration of Generative Artificial Intelligence (GenAI) into programming education has raised important questions regarding its impact on learning processes, conceptual understanding, and technological dependency. This study analyzed the effects of four GenAI-supported instructional strategies in an introductory programming course for undergraduate engineering students. A multi-group quasi-experimental pre-test–post-test design was implemented involving 686 students distributed across 53 class groups, from 10 campuses, taught by 32 professors. The instructional conditions included Quizzes for Self-Regulation, Github-Copilot-assisted learning, Prompt Problems with Iterative Refinement, and Flipped Learning enhanced with GenAI, which were compared against a traditional teaching approach. Learning outcomes were measured using normalized learning gain, while statistical analyses were conducted using non-parametric methods due to deviations from normality and heteroscedasticity. Results indicate that GenAI integration did not produce statistically significant overall differences in learning gain when all GenAI-supported strategies were analyzed as a single cluster compared to traditional instruction. However, differences emerged between specific strategies, with Quizzes and Copilot-based approaches having higher median learning gains than Prompt Problems and Flipped Learning strategies. No statistically significant differences associated with gender were identified. These findings suggest that the effectiveness of GenAI in programming education depends less on the mere presence of the technology and more on the pedagogical conditions under which it is integrated into the teaching–learning process. Full article

Artificial intelligence (AI) is increasingly being integrated into higher education. Among these innovations, AI teaching assistants have emerged as tools that can provide immediate academic support, personalized feedback, and improved access to learning resources. Despite the growing adoption, limited research has explored students’ knowledge, attitudes, and acceptance of AI teaching assistants in African higher education contexts. The Technology Acceptance Model was adopted as a theoretical lens to explore South African university students’ Knowledge, Attitudes and Acceptance of AI teaching assistants in a clinical learning environment. A qualitative study design within an interpretivist paradigm was employed. Data were collected through semi-structured interviews with six undergraduate students who had experienced both traditional teaching approaches and AI-assisted learning. The data were analysed using thematic analysis. The findings revealed three key themes: students’ understanding of AI teaching assistants, attitudes toward AI-assisted learning, and acceptance and concerns regarding AI in clinical education. The results indicate that students generally demonstrate positive attitudes toward AI teaching assistants and recognize their usefulness for supporting independent learning. However, participants also expressed concerns regarding the accuracy of AI-generated information and emphasized the continued importance of human educators in clinical training. The study contributes context-specific insights into technology acceptance in African higher education, highlighting how perceived usefulness may remain strong even in resource-constrained environments. Full article

Higher education increasingly addresses topics that are complex, interdisciplinary, and context-dependent, creating challenges for traditional lecture-based instruction. This study explores the potential of AI-assisted interactive storytelling as a pedagogical approach for such learning contexts, using healthy buildings as an instructional case relevant to architecture, engineering, and construction (AEC) education. Grounded in constructivist learning theory, a set of interactive stories was developed using generative AI and implemented in Twine to create a decision-based learning experience. The intervention was tested in a class using a pretest–posttest design along with a student perception survey. The results showed a significant improvement in knowledge following the intervention. Student feedback was also positive across all measured dimensions, including perceived learning, cognitive engagement, emotional engagement, motivation to learn, and comparison with traditional lectures. These findings suggest that interactive storytelling can support both learning and engagement when teaching complex, multidimensional topics. This study further indicates that generative AI can serve as a practical development partner by reducing the time and technical effort required to create interactive educational materials. Overall, this paper contributes to higher education research by positioning and demonstrating AI-assisted interactive storytelling as a promising instructional approach for complex learning areas. Full article