Editorial: Artificial intelligence for technology enhanced learning

Résumé

Together, these papers show that technical innovation in TEL is increasingly centered on adaptive architectures, multimodal knowledge representation, efficient assessment generation, LLM evaluation, and human-centered interaction design.A second group of papers examines how AI systems are applied in authentic educational contexts to support personalized learning, adaptive feedback, self-regulated learning, and assessment.Zhang et al. review the current status and future prospects of AI in residency training. They highlight the role of AI-assisted systems in personalized teaching, adaptive learning pathways, and real-time feedback, especially in skills-based medical education.Ou, Nogueira, and Qin investigate AI-assisted music practice applications for trainee violinists.Their study shows that intelligent practice tools can improve technical performance, selfefficacy, and self-regulated learning behaviors. The paper demonstrates how AI can support learner autonomy and sustained engagement in artistic training. Hadzhikoleva, Hadzhikolev, Gaftandzhieva, and Pashev propose a conceptual framework for multi-component summative assessment in e-learning environments. By integrating Bloom's taxonomy, fuzzy logic, and generative AI, they aim to improve transparency, adaptability, and pedagogical grounding in digital assessment.Nguyen and Nguyen examine AI-assisted academic cheating among postgraduate students. Their conceptual model identifies policy ambiguity, academic pressure, and insufficient ethical guidance as major factors contributing to AI misuse. The authors argue for AI literacy, ethics training, and redesigned assessment practices.Gao et al. study adaptive cognitive diversity and attention in human-AI collaborative discussion. Their findings show that AI-generated diverse viewpoints can broaden discussion, while similar viewpoints can deepen it. However, learner attention to AI-generated ideas can both support and inhibit idea generation, emphasizing the need for careful design of AImediated discussion environments.Lan, Liu, and Chen analyze AI dependence, AI addiction, and learning burnout through the I-PACE model. Their study shows that perceived usefulness, perceived enjoyment, and inert thinking may contribute to dependence and addiction, which in turn increase learning burnout. This paper provides an important cautionary perspective on psychological risks in AI-assisted learning.Jun, Lazic, and Woodruff examine the relationship between emotions, conscientiousness, and understanding. Their findings show that both positive and negative emotions influence understanding, reinforcing the importance of emotional and contextual dimensions in TEL research.Akhter and Shaheen study AI adoption, academic motivation, peer support, and psychological well-being among university students in Pakistan. Their findings suggest that AI adoption can positively support motivation, well-being, and student success, especially when combined with strong peer support.Chen and Lou examine translation students' engagement with LLM-generated feedback. Their study shows that learners do not accept AI feedback passively; instead, they selectively evaluate suggestions according to accuracy, fluency, cultural sensitivity, and contextual appropriateness. This highlights learner agency and critical evaluation as essential elements of AI-supported learning.Overall, these studies show that adaptive TEL environments must be understood as sociotechnical systems. Their effectiveness depends not only on algorithmic performance, but also on learner agency, emotional engagement, ethical use, self-regulation, and pedagogical alignment.Finally, a group of papers also highlights domain-specific advances in healthcare education, language learning, communication training, forensic medicine, traditional Chinese medicine, and nursing management.Chen and Wang examine the integration of virtual reality simulation with multilevel team-based pedagogy in anesthesiology residency training. Their randomized controlled trial shows improvements in technical skills, non-technical competencies, procedural accuracy, teamwork, and long-term skill retention. This study supports immersive and collaborative simulation as an important direction for healthcare education.Ji, Xiao, and Li investigate an AI-driven intelligent training framework for oncology residency. Their model integrates dynamic knowledge graphs, AI mentors, mixed-reality collaboration, and learning analytics dashboards. The study reports improvements in theoretical mastery, clinical reasoning, procedural competence, multidisciplinary collaboration, and knowledge retention, while reducing cognitive workload.Wu and Du review AI-driven transformation in forensic medicine education. They argue that AI can reshape forensic education through virtual anatomy, 3D reconstruction, intelligent case repositories, simulation-based learning, personalized pathways, and multimodal assessment. Their work frames AI as a driver of pedagogical transformation rather than merely a technical supplement. Younas et al. evaluate AI-generated scenario-based English language teaching at university level. Their study finds improvements in communication fluency, pronunciation, and interactive participation, while also noting challenges related to emotional interaction, technological adaptation, and overreliance on AI-generated content. Soundarraj, Anantharajan, and Loganathan present PhonoMetric, a dual-metric framework for real-time accent assessment and personalized speech training for Indian English learners. By combining pronunciation assessment with speaker-aware modelling, the framework demonstrates AI's potential for adaptive language and communication training.Liu, Zhu, Zhang, and Ding examine Chinese undergraduates' perceived acceptance of AI technology and its relationship to academic achievement. Their findings show that perceived usefulness and ease of use positively influence achievement and critical thinking, with critical thinking mediating the relationship between AI acceptance and academic performance.Li et al. provide a bibliometric study of AI in nursing management from 1990 to 2025. They identify an increase in publication output after 2017 and major research clusters in decision support, leadership, informatics, behaviour, and disease-specific applications. Their study situates AI-enhanced learning and management within broader health-sector research trends. Across these domains, AI is shown to support simulation, professional training, disciplinary reasoning, language development, assessment, and curriculum transformation. However, we should emphasize that AI adoption must remain sensitive to institutional context, professional identity, learner psychology, and domain-specific pedagogical needs.Out of presented works, we can conclude that the future of AI in TEL depends on connecting technical innovation with pedagogical clarity, ethical governance, domain expertise, and human-centered design.Several future directions emerge from the collection. First, AI-enhanced learning systems need to become more explainable, reliable, context-aware, and resource-efficient. The studies on Node-Sampling, multimodal LLM evaluation, knowledge graphs, and retrieval-augmented tutoring show that technical sophistication must be accompanied by transparency and pedagogical usability.Second, AI systems should support learner autonomy rather than replace human judgment. The papers on LLM-generated feedback, collaborative discussion, academic integrity, and AI dependence show that learners need AI literacy, critical thinking, and self-regulation skills to use intelligent systems responsibly.Third, future TEL research must address psychological and emotional consequences. Studies on AI dependence, burnout, emotions, motivation, peer support, and well-being demonstrate that AI-supported education can both empower and burden learners. Human-centered TEL must therefore consider cognitive load, emotional engagement, agency, and sustainable learning practices.Fourth, institutions need stronger ethical and governance frameworks. The collection stresses issues of academic integrity, cultural sensitivity, trust, fairness, professional competence, transparency, and human oversight. Generative AI challenges traditional assumptions about authorship, expertise, assessment, and professional identity, requiring redesigned curricula and assessment systems.Finally, the papers call for interdisciplinary research that connects computer science, educational technology, psychology, medicine, language education, learning analytics, and human-computer interaction. The future of AI in TEL should not be driven by technological enthusiasm alone, but by evidence-based, inclusive, ethical, and sustainable educational models.

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