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📄 Research Article

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

● high · 2026-07-30higher-ed, stem-education, student-experience, affective-computing, personalized-learning, scaffolding, adaptive-learning, ai-literacy, formative-assessment, teacher-role

Survey of 206 engineering students: AI chatbots provide greatest perceived benefit as relief from competence frustration, smaller benefits for autonomy, weakest for relatedness. Baseline motivational states matter more than demographics; inattention moderates how baseline competence and autonomy relate to perceived AI benefits. Offers design principles for engineering-specific AI learning tools.

Relevance to AI in Education: This paper contributes to the understanding of Automated Assessment, Personalized Learning, and Student Experience. The findings have implications for Adaptive Learning systems, Formative Assessment design, and the broader Edtech Platform landscape. Future work should explore how these results generalize across STEM Education and Higher Ed contexts.

This research connects to the growing body of work on AI Literacy and Teacher Role, highlighting both the promise and limitations of AI tools in educational settings.

Connected Concepts

  • Automated Assessment
  • Personalized Learning
  • Student Experience
  • Adaptive Learning
  • Formative Assessment
  • Edtech Platform
  • STEM Education
  • Higher Ed
  • AI Literacy
  • Teacher Role

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    • Citation

    Kevin Zhongyang Shao, Denise Wilson, Yale Quan, Sep Makhsous (2026). Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes. arXiv:2607.26338. Under review.