A Theoretical Framework of Student Agency in AI- Assisted Learning: A Grounded Theory Approach

2025-12-07

AI Summary

This study addresses the conceptual ambiguity and weak empirical foundation of student agency in AI-augmented higher education learning environments. Employing grounded theory methodology, it integrates generative AI dialogue logs with cognitive interview data to systematically develop a theoretical framework of student agency. The study introduces, for the first time, a dynamic process model comprising four interrelated dimensions: “initiation and redirection,” “deliberate adoption,” “soliciting external support,” and “reflective learning”—capturing agency as proactive, intentional, adaptive, and iterative. This framework advances theoretical understanding of agency within technologically mediated learning contexts and provides empirically grounded, actionable insights for instructional design, AI-educational tool development, and educational policy formulation. It thus bridges theoretical innovation with practical applicability in AI-integrated pedagogy.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Generative AI(GenAI) is a kind of AI model capable of producing human-like content in various modalities, including text, image, audio, video, and computer programming. Although GenAI offers great potential for education, its value often depends on students' ability to engage with it actively, responsibly, and critically - qualities central to student agency. Nevertheless, student agency has long been a complex and ambiguous concept in educational discourses, with few empirical studies clarifying its distinct nature and process in AI-assisted learning environments. To address this gap, the qualitative study presented in this article examines how higher education students exercise agency in AI-assisted learning and proposes a theoretical framework using a grounded theory approach. Guided by agentic engagement theory, this article analyzes the authentic experiences of 26 students using data from their GenAI conversation records and cognitive interviews that capture their thought processes and decision-making. The findings identify four key aspects of student agency: initiating and (re)directing, mindful adoption, external help-seeking, and reflective learning. Together, these aspects form an empirically developed framework that characterizes student agency in AI-assisted learning as a proactive, intentional, adaptive, reflective, and iterative process. Based on the empirical findings, theoretical and practical implications are discussed for researchers, educators, and policymakers.

Problem

• Develops a theoretical framework for student agency in AI-assisted learning
• Examines how higher education students exercise agency with generative AI
• Identifies key aspects of student agency through qualitative grounded theory

Innovation

• Grounded theory approach to analyze student agency
• Analyzes GenAI conversation records and cognitive interviews
• Identifies four key aspects of student agency