🧠 AI Ed Wiki

📄 Research Article

Structured AI Demonstrations and Student LLM Use in Engineering Mechanics: Study Design and Preliminary Results

● high · 2026-08-03llm, stem-education, higher-ed, student-experience, teacher-role, scaffolding, efficacy-study, learning-analytics

Shuang Geng, Helen Lallos-Harrell, Jiya Ashar, Thomas J. McKenna, Annwesa Dasgupta, Caleb Farny, Emma Lejeune — arXiv preprint (2026).

Synthesis

This descriptive study documents student LLM use in an undergraduate engineering mechanics course (Spring 2026), responding to the lack of domain-specific empirical evidence for pedagogical policy in engineering education.

The authors contribute a reproducible survey instrument capturing student AI usage patterns, attitudes, and verification practices, linked to academic performance metrics — an open methodological framework for other instructors.

A deployable sequence of nine structured, instructor-led AI demonstrations models strategic LLM delegation and evaluation for students, treating AI use as a taught skill rather than an assumed behavior.

Preliminary data show shifting student behaviors and complex relationships between AI reliance and course outcomes, though the primary contribution is the study design itself; results are framed as preliminary.

Connected Concepts

  • Administrator
  • Socratic AI Dialogue
  • Pedagogical Agent
  • Automated Question Generation
  • Agentic AI
  • AI Tutoring
  • Math Education
  • Prompt Engineering

    • Connected Articles

  • Trio Ethnography LLM Programming Education — Beyond Perspectives: A Trio-Ethnography of Interpretation Evolution in LLM-Supported Programming Education
  • Edumirror Educational Social Dynamics — EduMirror: Modeling Educational Social Dynamics with Value-driven Multi-agent Simulation
  • AI Team Teaching Talk Analytics — AI-Driven Analytics of Team-Teaching Talk: Acoustic Patterns across Experience, Cohorts and the Learning Design
  • AI Engineering Education Balancing Act — Using AI in engineering education: a balancing act, driven by clear purpose
  • Bridging Instructional Design Framework Math — WIP: Bridging the Gap Between Instructional Design and Pedagogical Use: A Framework for Mathematics Educators
  • LLM Misconception Difficulty Easy Trap — The Easy Trap: Why LLMs Underestimate Misconception-Driven Difficulty

    • Citation

    Geng, S., Lallos-Harrell, H., Ashar, J., McKenna, T. J., Dasgupta, A., Farny, C., & Lejeune, E. (2026). Structured AI demonstrations and student LLM use in engineering mechanics: Study design and preliminary results. arXiv:2607.28710.