The Relationship Between AI Anxiety and Academic Motivation Among University Students: The Mediating Role of Emotion Regulation and the Moderating Role of Gender

Yuanchang Zhang, Yantao Shi, and Jing Lu (2026) examined whether AI anxiety constitutes a psychological barrier to university students' motivational adaptation to AI-supported learning, using a cross-sectional survey of 1,484 Chinese undergraduates analysed with moderated-mediation models. Generative AI is increasingly embedded in students' writing, information retrieval, and knowledge organisation, yet the psychological responses to that integration remain under-studied relative to educational affordances and ethical challenges. The study found AI anxiety negatively associated with both emotion Regulation and academic motivation, while emotion regulation was positively associated with academic motivation; bootstrap analyses confirmed a significant negative indirect path from AI anxiety to academic motivation through emotion regulation. Gender moderated the emotion-regulation–motivation link, with a stronger positive association among male students.

Key Findings

• AI anxiety correlates with lower motivation and poorer regulation: AI anxiety was negatively correlated with emotion regulation (r=−0.172) and academic motivation (r=−0.175), whereas emotion regulation was positively correlated with academic motivation (r=0.457), all p<0.001.
• Emotion regulation partially mediates the path: Bootstrap analysis (5,000 resamples) confirmed a significant negative indirect association between AI anxiety and academic motivation through emotion regulation (indirect effect=−0.076, 95% CI −0.108 to −0.047), with the direct path also remaining significant.
• Gender moderates the regulation–motivation link: The positive association between emotion regulation and academic motivation was stronger among male students, making gender a boundary condition for this pathway.
• Weak and cautious use-duration signal: Daily AI use showed only a weak positive correlation with academic motivation (r=0.053), interpreted cautiously given most students reported under one hour of use per day — exposure duration alone does not capture purpose or quality of AI engagement.
• Practical implication for AI literacy education: Findings suggest AI literacy education should integrate emotion-regulation and metacognitive support with differentiated, gender-aware learning assistance.

Implications for AI in Education

The study positions emotional well-being as central to whether students thrive in AI-supported learning, arguing that reducing AI anxiety — rather than simply expanding tool access — is key to sustaining Motivation. For higher-education instructors and support services it suggests pairing AI literacy instruction with confidence-building and emotion-regulation strategies, and it cautions against reading usage time as evidence of productive engagement. Because the data are cross-sectional and self-reported, the authors call for longitudinal and experimental designs to test whether these relationships are causal and whether gender-differentiated support is warranted.

Connected Concepts

• Motivation
• Well Being
• Higher Ed
• Generative AI
• Self Efficacy
• Metacognition
• Student Experience

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• AI Anxiety Strategic Regulation Writing 2026 — From AI Anxiety to Strategic Regulation
• GenAI Motivation Engagement 2026 — Examining the Impact of Generative AI on Student Motivation and Engagement
• Liang AI Learning Motivation Sdt 2026 — From Disengaged to Self-Determined: A Latent Transition Analysis
• AI Perceptions Students Teachers Motivation 2026 — Exploring AI Perceptions in Education

Citation

Zhang, Y., Shi, Y., & Lu, J. (2026). The relationship between AI anxiety and academic motivation among university students: The mediating role of emotion regulation and the moderating role of gender. Frontiers in Psychology, 17, 1918525.