Learning Behavior & AI Agency

Over the past decade, Learning Analytics has established robust methods for analyzing digital learning traces — reading behavior, navigation, duration, and annotation patterns from digital textbooks and e-book systems — supporting at-risk identification, performance prediction, and evidence-based instructional interventions. Concurrently, Large Language Models and Generative AI are transforming educational AI from reactive, prompt-based tools into proactive Agentic AI systems that act as active collaborators rather than passive tools.

Yet performance gains do not guarantee learning: AI can improve task outcomes without producing meaningful learning unless guided by pedagogical principles. This defines a new design challenge at the intersection of AI autonomy and pedagogical alignment, and motivates deeper integration of learning analytics into research on human-AI interaction — from structured learning logs to dynamic interaction data such as conversational processes, prompt refinement, help-seeking, and collaboration patterns.

Aligned with the ICCE 2026 theme "Reimagining Learning Ecologies in the Age of Intelligent Technologies", this full-day workshop bridges ICCE's sub-conferences on AIED/ITS (C1) and Advanced Learning Technologies and Learning Analytics (C3), convening researchers across learning analytics, AI in education, and human-AI interaction.