2025 ASEE Annual Conference & Exposition

AI Chatbot for Enhancing Troubleshooting in Engineering Labs

This study investigates the effectiveness of artificial intelligence (AI) as a learning tool in an engineering lab setting. The authors have developed an AI chatbot for engineering experimentation classes at an R1 research-oriented institution in the northeastern United States. These classes emphasize developing engineering metrology skills through electronic instrumentation and hands-on laboratory work. In this context, students frequently rely on the instructional team, including teaching assistants (TAs), for guidance on lab procedures, equipment setup, and troubleshooting. This reliance can overwhelm TAs with repetitive questions, often resulting in inconsistent responses. These issues impact the overall learning experience and reduce the efficiency of instructional support. To address this, we designed an AI chatbot that provides students with immediate, on-demand support to enhance their learning experience while reducing the workload on TAs.
The chatbot is built using OpenAI's large language model (LLM) and is tailored to use problem-solving frameworks such as issue trees and first principles. The issue tree framework breaks down complex problems into smaller, more manageable components, offering multiple potential solutions for each part. This approach allows students to explore various solution pathways systematically. On the other hand, the first principles method guides students toward a deeper understanding of concepts by breaking problems into their fundamental elements. The chatbot also uses Socratic questioning to engage students, prompting them to think critically and build upon their existing knowledge. This method encourages students to find solutions independently rather than providing direct answers, fostering their problem-solving skills and enhancing their grasp of engineering concepts.
The AI chatbot supports activities, including understanding lab procedures, setting up electronic instrumentation, and troubleshooting errors. Its contextual data for its responses include detailed procedures for the different laboratory assignments available to all students in the class, allowing information about the assignments to come directly from instructor-created sources.
To evaluate the chatbot's effectiveness, we adopt a mixed-methods approach, including qualitative student feedback, surveys, and an analysis of student-chatbot interactions. Students complete surveys at the end of the term, capturing data on their experiences, learning outcomes, and satisfaction with the chatbot. Analyzing student-chatbot dialogues provides insights into the chatbot's ability to parse course material, employ Socratic questioning, and encourage students to explore various problem-solving strategies.
Based on the data of chatbot interactions and students' responses, this AI chatbot enhanced the educational experience by promoting critical thinking and independent learning while reducing the workload of TAs. The success of this AI-driven tool shows that the concept of having a chatbot tool can expand to assist with other engineering lab courses or student projects, offering consistent, high-quality, on-demand support across disciplines and reducing reliance on real-time TA intervention.