Conference Agenda

Mobile learning and microlearning have become central pillars of technology‑enhanced learning in higher education, driven by the ubiquity of mobile devices and evolving learner expectations. More recently, Generative Artificial Intelligence (GenAI) has introduced unprecedented opportunities to automate, personalize, and scale the design of microlearning objects for mobile delivery. However, existing research remains fragmented across technological, pedagogical, and ethical perspectives, offering limited guidance on how GenAI‑generated microlearning objects should be systematically designed, validated, and integrated into mobile learning environments. This paper addresses this gap by capturing the current state of the art in GenAI‑enabled microlearning design and proposing a multi‑step, pedagogically grounded design process intended for application and iterative refinement within interdisciplinary research projects. A design‑oriented scoping review was conducted following established methodological frameworks to synthesize evidence on mobile learning and microlearning effectiveness, GenAI applications in higher education, and corresponding ethical and governance considerations. Building on this synthesis, the paper presents an eight‑step design process that integrates learning sciences, AI system design, human oversight, accessibility, and assessment alignment. The resulting framework provides a theoretically informed and ethically grounded foundation for designing, generating, and researching GenAI‑based microlearning objects in mobile learning ecosystems.

Abstract—The paper presents a novel framework for design of educational scenarios based on a mobile system for registration and analysis of mental activity from both behavioral and neurocognitive (EEG) recordings. It is intended for classes where robots, emulating human presence, are used as tutors to provide guidance and rehearsal, tailored to the needs of the individual learner, and assisting the human teacher. An experimental study was performed for registration of event-related potentials (ERPs) by Emotiv Insight 2.0 during the first 750 msec after the onset of a cognitive task, presenting agents with varying degree of anthropomorphism. The main results reveal different effects of face and novelty detection of the agents based on data from the AF4 electrode, allowing for online interpretation of attentional processes of the learner. The proposed methodology brings closer to the format of classroom activities the newly acquired knowledge about mental and attentional regularities, which can help design lessons, better adapted to inclusive education.

The rapid advancement of Generative Artificial Intelligence (GAI), particularly large language models such as ChatGPT, has introduced new possibilities for supporting learning in cross-cultural educational contexts. However, limited research has systematically examined how GAI reshapes learners’ cognitive processes, learning strategies, and participation patterns in short-term study abroad environments. Addressing this gap, this study investigates the role of GAI in mediating the learning experiences of non-native graduate students engaged in cross-cultural academic settings. This study involved 30 Taiwanese graduate students participating in a two-month study abroad program at a university on the U.S. West Coast. Adopting a qualitative research design, data were collected through classroom observations, semi-structured interviews, and learner-generated artifacts, and analyzed using thematic analysis grounded in an interpretive approach. The findings indicate that GAI functions not merely as a supplementary tool but evolves into a distributed cognitive support system that extends learners’ cognitive capacities. Specifically, GAI facilitates cross-linguistic comprehension, knowledge organization, and real-time academic preparation, enabling learners to engage more actively in classroom interactions. The integration of GAI also leads to a transformation in learning strategies, characterized by increased reliance on AI-assisted scaffolding alongside reflective and self-regulated learning practices. Furthermore, a human–AI collaborative learning model emerges, in which learners dynamically negotiate the balance between AI-mediated support and independent cognitive processing. This shift contributes to reduced language-related anxiety, enhanced participation, and reconfigured classroom interaction patterns. This study contributes to the literature by conceptualizing GAI as a form of distributed cognition in cross-cultural learning contexts and by illustrating how AI-mediated learning reshapes the ecology of classroom participation. The findings suggest that while GAI holds substantial pedagogical potential for enhancing comprehension, engagement, and learner autonomy, its effectiveness depends on intentional instructional design that positions AI as a cognitive partner rather than a substitute for human learning. This study aligns with the theme of ITHET 2026, which emphasizes the role of information technology and artificial intelligence in shaping the future of education. By examining generative AI as a cognitive support tool, this research contributes to ongoing discussions on personalized learning, intelligent tutoring systems, and human–AI collaborative learning in cross-cultural contexts.