2026 ASEE Annual Conference & Exposition

The integration of Artificial Intelligence (AI) tools into higher education represents a paradigm shift, evolving from a novel resource to a ubiquitous academic assistant. This rapid proliferation mirrors historical technological integrations, such as the calculator and the internet, which fundamentally altered student learning strategies outside the classroom. In engineering education, where mastering complex mathematical models and abstract concepts is challenging, AI presents a unique opportunity to provide on-demand, personalized support. Potential benefits include decomposing intricate problems, generating alternative explanations, and facilitating iterative debugging. However, the specific ways in which engineering students autonomously adopt and utilize these tools across different domains and course levels remain poorly characterized. Understanding these student-driven strategies, their perceived efficacy, and associated challenges is critical for developing informed pedagogical policies and effective AI-integration frameworks.
This work-in-progress study investigates how students leverage AI tools to support their learning across three distinct electrical engineering courses: a sophomore-level Linear Circuits and Systems, a junior-level Linear Control Systems, and an upper-division Introduction to Machine Learning. The study employs a qualitative and quantitative analysis of student survey responses collected at the conclusion of each course which are offered in Summer 25 and Fall 25. The survey instrument probes key dimensions of AI usage, including frequency, task-specific applications (e.g., concept explanation, code debugging, problem-solving), perceived helpfulness, fact-checking behaviors, and encountered challenges. This cross-course design allows for a comparative analysis to determine how AI usage patterns and perceptions vary with course content maturity and subject matter.
The full manuscript will present a comprehensive analysis of the aggregated and course-specific survey data. Expected results will delineate the primary tasks for which students use AI, identify common challenges such as over-reliance or output inaccuracies, and quantify the perceived utility of AI across different learning aspects. We anticipate a significant variation in AI usage patterns between the foundational circuits course and the more advanced machine learning course, reflecting differing student needs and tool applicability. Furthermore, the analysis of student opinions on formal AI integration will provide valuable insights for curriculum development. The findings from this study aim to provide empirical evidence to guide faculty in creating evidence-based guidelines, tailored AI-enabled assignments, and support structures that harness the benefits of AI while mitigating its risks, ultimately fostering a more adaptive and effective engineering learning environment.