2026 ASEE Annual Conference & Exposition

Artificial intelligence (AI) has rapidly entered educational settings, prompting a shift among instructors from efforts to restrict its use to exploring practical ways to guide students in using it productively. In engineering education, this support is particularly critical to ensure that students’ engagement with AI enhances rather than hinders their learning. Yet, uncertainty remains about how AI can function as a self-tutoring aid that supports students’ independent exploration of complex course concepts and how students perceive its usefulness in this role.
This exploratory action research investigates the use of AI as a self-directed tutoring tool in an undergraduate electronics course. The study aims to understand how students use AI to support their comprehension of course concepts, how they perceive its usefulness, and how these perceptions relate to their course performance. The central research question guiding the study is: How can AI serve as a tutor to support students’ conceptual understanding in an electronics course? Students were provided with guided prompts to encourage purposeful engagement with AI tools. They were asked to reflect on their interactions with AI, focusing on how it influenced their understanding, learning strategy, and approach to problem-solving. These reflections were collected and analyzed through inductive analysis to identify recurring patterns that reveal how AI functioned as a tutor and shaped students’ conceptual learning experiences.
Preliminary findings suggest that while students acknowledged AI’s ability to clarify complex concepts and enhance their learning strategies, many found it challenging to translate AI suggestions into effective solutions for practical problems. It might take time for students to improve their learning methods for a long-term impact. However, evidence of critical thinking emerged in several reflections, indicating that structured guidance and reflective scaffolding play a key role in leveraging AI as a productive learning companion. The study contributes to ongoing conversations about integrating AI into engineering education by offering insights into its pedagogical value as a cognitive partner and by identifying instructional strategies that foster deeper, self-regulated learning.