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arXiv · 2607.26338

Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes

Kevin Zhongyang Shao·Denise Wilson·Yale Quan·Sep Makhsous
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Abstract

Artificial Intelligence (AI) is transforming higher education, but its benefits can vary depending on where, how, and how often it supports learning. While prior research emphasizes cognitive and academic outcomes, this study examines how AI chatbots support the psychological needs and motivational states of engineering students. A survey of college engineering students (n = 206) examined perceived effects of AI chatbots on autonomy, relatedness, and relief from competence frustration. Structural equation modeling with latent interaction effects examined how baseline autonomy, competence frustration, relatedness, and personal agency contributed to perceived AI outcomes. Results indicate that students perceived that AI provided the greatest benefits as relief from competence frustration, smaller benefits for autonomy, and the weakest benefits for relatedness. Baseline motivational states mattered more than demographic factors, and inattention moderated how baseline competence frustration and autonomy related to perceived AI-related benefits. These results offer insights into formulating design principles for engineering-specific AI-based tools.

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Kevin Zhongyang Shao, Denise Wilson, Yale Quan, Sep Makhsous. 2026-07-28. Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes. https://arxiv.org/abs/2607.26338

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