2025 ASEE Annual Conference & Exposition

In this paper, we study the impact of a pre-college summer education program on students’ self-efficacy as they progressed from high school to college. Specifically, we study how learning about neural networks and artificial intelligence in the pre-college program affects the professional formation of students in engineering and computer science undergraduate programs. We measure changes in students’ self-efficacy, emotional learning, and readiness to join and contribute to the Artificial Intelligence (AI) workforce in this two-year impact study from Fall 2022 to Fall 2024. Thus, our findings are relevant for optimizing pre-college to college education pipelines to meet workforce needs in engineering, AI, and the Computer Science (CS) industry.

To study the impact of the pre-college AI education program on student progression, we conducted focus group interviews in Fall 2024, two years after the pre-college program. With thematic analysis, we quantify student and program outcomes by synthesizing four themes: social and emotional learning, self-efficacy, career readiness, and program impact. To formally validate human thematic analysis, we ask: (RQ1) What methods can validate heuristic thematic analysis for reliable study of qualitative data? To quantify the two-year impact of the program, we study (RQ2) whether the pre-college program enhanced students' confidence and readiness for a college major in computer science or related engineering disciplines. For a deeper understanding of students’ perceptions and change in psychosocial behavior, we also study: (RQ3) Which specific aspects of self-efficacy and social and emotional learning are most affected among students who participated in the summer program? Our measurement instruments are pre-/post-course Likert surveys, thematic analysis of student focus groups, and a codebook-based quantitative analysis of student reflections. We report the correlations of our thematic analysis results with the pre- and post-course Likert surveys conducted when students were enrolled in the pre-college program. Our findings provide important insights on designing teaching approaches and future pre-college programs that enhance students' preparation for first-year engineering programs and careers in CS and AI.