How High-School Pressure and Autonomy Support Are Linked to Dual AI Learning Pathways: A Cross-Contextual SEM Analysis

Synthesis: Shen and Arunrugstichai propose a dual-pathway framework explaining why students' GenAI use ranges from constructive support to compulsive dependence. Drawing on Self-Determination Theory (SDT) and the Hook model, they examine how retrospective reports of high-school learning climate relate to university students' GenAI-use patterns and perceived AI-supported learning quality. Using cross-sectional survey data (overall N = 508; China-HS n = 305, non-China-HS n = 203; China-Uni n = 193, Thailand-Uni n = 315), they test the model with structural equation modeling and multi-group analysis.

Key Findings

• Dual pathways emerge linking learning climate to GenAI use: one toward constructive, autonomous use and another toward compulsive dependence.
• High-school pressure vs. autonomy support differentially predict which pathway students follow into university.
• Findings are consistent across cross-contextual groups (China vs. non-China high-school backgrounds; China vs. Thailand university contexts), supporting the framework's generalizability.
• The results link SDT-based motivational processes and behavioral mechanisms (Hook-model engagement) to perceived quality of AI-supported learning.

Implications for Practice

• For schools: Autonomy-supportive learning climates may steer students toward constructive GenAI use, while pressure-laden environments may foster dependence.
• For higher education: Understanding students' prior learning climate helps explain and respond to differential GenAI use patterns.
• For researchers: The dual-pathway model integrates SDT and behavioral engagement (Hook model) to explain both productive and problematic AI use.

Connected Concepts

• Self-Determination Theory
• Student Engagement
• Motivation
• Recommender Systems and Learning Paths

Connected Articles

• From disengaged to self-determined: a latent transition analysis of students' AI learning motivation — AI learning and motivation from an SDT lens
• Examining the Impact of Generative AI on Student Motivation and Engagement: The Mediating Role of Autonomy-Support and Autonomous Motivation in Education — GenAI, motivation, and engagement
• Why Put in This Much Effort?": How AI Availability Shapes Students’ Motivation in Introductory Programming — how AI availability shapes student motivation

Citation

How high-school pressure and autonomy support are linked to dual AI learning pathways: A cross-contextual SEM analysis — Shen, Q., & Arunrugstichai, W. (2026). Computers and Education: Artificial Intelligence, 10, 100621.