A Study on the Relationship Between College Students' Academic Stress, AI Emotional Support Use and Learning Motivation

Yongshun Jin

doi:10.6918/IJOSSER.202511_8(11).0017

Authors

Yongshun Jin

DOI:

https://doi.org/10.6918/IJOSSER.202511_8(11).0017

Keywords:

Academic stress, AI emotional support, Learning motivation, Buffering effect, mediation effect, Hierarchical regression, Robustness, SAES 2024

Abstract

This study examines how AI-based emotional support (AI-ES) affects the relationship between academic stress and learning motivation among college students. Drawing on the 2024 Student Academic Experience Survey (SAES 2024), a standardized analytical framework was applied using hierarchical regression (M1–M4) and mediation analysis along the pathway stress → AI satisfaction → motivation, with robustness verified through bootstrap (B=5000) and SEM/PROCESS methods. Results show that higher AI-ES usage is associated with lower anxiety and stronger learning motivation, supporting its buffering effect on the negative influence of stress. However, this effect weakens under high time pressure. Overall, AI-ES functions as a psychological moderator that alleviates stress-induced motivational decline, suggesting that universities should adopt differentiated AI-ES applications and literacy training to enhance students’ emotional resilience and learning engagement.

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