Abstract
Virtual Exchange (VE) has traditionally positioned human-to-human interaction as the foundation for intercultural learning, dialogue, and collaborative knowledge construction. The emergence of generative AI, however, invites institutions to reconsider VE as a Human–AI–Human learning ecology, in which AI does not replace human exchange but functions as an orchestrated mediating layer that supports more inclusive, reflective, and dialogic participation.
This presentation introduces a pedagogical model developed through the second phase of the Japan Hub for Innovative Global Education (JIGE). Drawing on COIL-based implementations and design experiments with partners across Japan, Southeast Asia, and Europe, it examines how AI can be embedded across the VE learning cycle. Before exchange, AI can enable discourse rehearsal, linguistic preparation, contextual orientation, and confidence-building. During interaction, it can provide language support, participation prompts, and scaffolding for collaboration across linguistic and cultural differences. After exchange, it can facilitate reflection, perspective expansion, and critical examination of assumptions, while prompting students to interrogate both their own thinking and AI-generated responses.
The session addresses persistent inequities in VE, including linguistic hierarchy, uneven participation, confidence gaps, and power asymmetries. It proposes learning ecology design strategies that align human relationships, facilitation, digital tools, and reflective practice to deepen—not dilute—global learning.
Learning objectives
- Understand the Human–AI–Human (HI–AI–HI) model as a learning ecology in which generative AI augments, rather than replaces, intercultural dialogue and collaborative learning.
- Identify practical ways to integrate AI across the virtual exchange cycle, including pre-exchange preparation, live interaction, collaboration support, and post-exchange reflection.
- Examine how AI-enabled design can help address persistent inequities in Virtual Exchange, such as linguistic hierarchy, uneven participation, confidence gaps, and power asymmetries.
- Gain practical design principles for aligning human facilitation, AI tools, intercultural learning objectives, and reflective assessment in next-generation COIL and Virtual Exchange initiatives.
- Consider how to encourage students to engage critically with AI-generated content while maintaining human agency, ethical awareness, and meaningful intercultural meaning-making.
Target audience
This session will benefit academic staff, instructional designers, international education practitioners, and institutional leaders involved in COIL, Virtual Exchange, or blended mobility. It is especially relevant to those exploring ethical and inclusive uses of generative AI in intercultural learning.