AI as a Constrained Guide Measuring Knowledge Transfer

AI as a Constrained Guide: Measuring Knowledge Transfer in a Graduate Engineering Course

‍

As generative AI becomes increasingly integrated into higher education, educators face a critical question: How can students leverage AI as a learning partner without becoming overly reliant on it?

‍

This ASEE 2026 research study explores a novel approach in which AI functions as a constrained guide rather than a solution provider. Conducted in a graduate engineering course at NC State University, the study examined how students used AI to brainstorm, refine ideas, and receive structural feedback while relying on their own knowledge to develop project proposals.

‍

The findings suggest that thoughtfully designed AI constraints can support meaningful knowledge transfer, encourage independent reasoning, and help students apply newly acquired concepts to unfamiliar problems. The research also highlights how authorship transparency and peer assessment can provide deeper insights into student learning beyond traditional outcomes.

Download the Research Paper & Watch the Presentation

‍

Explore:

• How constrained AI influences student learning and knowledge transfer
• The role of AI guidance versus AI-generated solutions
• Student perceptions of AI-assisted learning
• Evidence from authorship transparency and peer assessment data
• Practical implications for engineering and higher education instructors

Access the full paper and conference presentation recording to learn how AI can be designed to support critical thinking, productive struggle, and authentic learning in the age of generative AI.

‍