2026 ASEE Annual Conference & Exposition

This work-in-progress report presents findings from a pilot study examining how novice end-user programmers solve problems through interactions with their preferred generative artificial intelligence (GenAI) platform in a first-year engineering (FYE) course. We introduce the term "novice end-user programmers" (abbreviated as NEUPs) to describe learners at the early stages of acquiring programming skills to address problems within their specific domains. This term extends the concept of end-user programmers described in the literature as non-computer science (CS) individuals who engage in programming activities but do not aim to become professional programmers or software developers. FYE students are a typical example of NEUPs, as they participate in computer programming education designed to teach them to solve engineering problems using specific programming languages. As GenAI tools increasingly facilitate learning in programming education, understanding how students communicate, reason, and develop solutions during GenAI-supported problem-solving is highly important. Guided by our student–GenAI interaction patterns framework, this study analyzes students’ prompt logs from their GenAI chat histories as they completed an in-class programming assignment. While quantitative content analysis was used to measure the descriptive distribution of students’ prompt-engineering techniques, qualitative content analysis was employed to identify interaction patterns and behaviors. Findings suggest various characteristics of students’ interactions with GenAI, including iterative feedback on GenAI’s output and varied interaction patterns and behaviors. This ongoing work offers insights into improving our instructional design and the subsequent phases of data collection and analysis in our larger project, which is expected to inform educators and decision-makers at the college level in managing GenAI integration into engineering educational environments.