2026 ASEE Annual Conference & Exposition

AI-assisted instruction holds strong potential to enhance conceptual understanding and student confidence in engineering problem-solving. Domain-specific AI tutors, in particular, can serve as scalable and effective learning supports within the field of engineering education. As artificial intelligence becomes increasingly integrated into higher education, understanding how students engage with such systems is critical for ensuring that technology enhances, rather than replaces, human-centered learning.
This paper presents the design and evaluation of a suite of Artificial Intelligence (AI) tutors developed to improve student learning in an Engineering Statics course. A total of seven AI tutors were created using OpenAI GPTs, each programmed to guide learners through the process of solving a specific representative problem. Each tutor focuses on a different topic within the course, providing step-by-step guidance that promotes conceptual understanding, reflection, and problem-solving proficiency rather than merely arriving at a correct solution. The AI tutors are being implemented in an undergraduate Engineering Statics class during Fall 2025 as homework assignments, where students interact individually with the tutors to complete assigned problems. Students are encouraged to think critically about the prompts, verify their reasoning, and reflect on the feedback provided by the AI system.
To evaluate the effectiveness and student perceptions of this pedagogical tool, two types of data are being collected: (1) AI interaction transcripts, which offer insight into students’ reasoning processes, misconceptions, and engagement with the tutors, as well as the AI tutors’ adherence to their intended instructional design; and (2) post-session surveys that capture feedback on ease of use, clarity, perceived learning benefits, and comparisons with existing publisher-provided tutorial resources. Preliminary results indicate a generally positive response from students, who reported that the AI tutors helped them clarify difficult concepts and practice problem-solving strategies more confidently. A full qualitative and quantitative analysis will be completed at the end of the semester. The results will contribute to a growing understanding of how specialized AI tools can be effectively integrated into engineering curricula to enhance student learning and engagement.