This chapter advances a sustainable model for distance higher education by integrating Generative Artificial Intelligence within the Trialogical Learning Approach. TLA centers on the co-creation of knowledge through collaboration around shared objects. This focus aligns with sustainability goals that privilege meaningful participation, equity, and durable learning outcomes. First, the chapter shows how AI supports personalized and adaptive learning. AI delivers real-time feedback, curates resources, and automates routine tasks, which increases student engagement and frees educator time for high-value teaching. These functions also widen access and enable inclusive practices in distributed settings. Second, the chapter examines how AI embedded in TLA strengthens collaborative knowledge creation. Drawing on Cultural-Historical Activity Theory and the knowledge-creation metaphor, it explains how AI tools mediate social interaction, scaffold joint projects, and document iterative improvement of shared artifacts. The result is a more cohesive learning community, where shared artifact development becomes traceable and reflective through the raise model’s ethically grounded approach to AI interaction. Bringing AI and TLA together yields a holistic, human-centered model of sustainability. It supports collaboration, connects people and artifacts across contexts, and aligns with frameworks for responsible AI in education.

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Empowering Sustainable Distance Education: Integrating AI-Driven Conversational Agents in the Trialogical Learning Approach

  • Nadia Sansone

摘要

This chapter advances a sustainable model for distance higher education by integrating Generative Artificial Intelligence within the Trialogical Learning Approach. TLA centers on the co-creation of knowledge through collaboration around shared objects. This focus aligns with sustainability goals that privilege meaningful participation, equity, and durable learning outcomes. First, the chapter shows how AI supports personalized and adaptive learning. AI delivers real-time feedback, curates resources, and automates routine tasks, which increases student engagement and frees educator time for high-value teaching. These functions also widen access and enable inclusive practices in distributed settings. Second, the chapter examines how AI embedded in TLA strengthens collaborative knowledge creation. Drawing on Cultural-Historical Activity Theory and the knowledge-creation metaphor, it explains how AI tools mediate social interaction, scaffold joint projects, and document iterative improvement of shared artifacts. The result is a more cohesive learning community, where shared artifact development becomes traceable and reflective through the raise model’s ethically grounded approach to AI interaction. Bringing AI and TLA together yields a holistic, human-centered model of sustainability. It supports collaboration, connects people and artifacts across contexts, and aligns with frameworks for responsible AI in education.