This track explores how learners, teachers, and AI systems can work together in productive and mutually complementary ways. It welcomes studies on teacher–AI orchestration, co-teaching systems, learner–AI interaction, collaborative decision-making, and mixed-initiative learning. Contributions may examine agency, trust calibration, role distribution, shared control, and the development of AI literacy. The track also considers how AI can augment professional expertise without weakening human judgement or learner autonomy. Empirical, theoretical, and design-oriented research across formal and informal education is encouraged.

Focus: Collaborative models in which humans and AI share roles, decisions, and responsibilities in educational settings. The goal is to strengthen human agency, expertise, and learning through transparent and complementary interaction.