2025 ASEE Annual Conference & Exposition

Self-efficacy of high school students after an AI-focused pre-college program: A two year impact study (Fundamental)

Presented at Pre-College Engineering Education Division (PCEE) Technical Session 22

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.

Authors

Dr. S. Shailja http://orcid.org/0000-0002-5056-9989 Stanford University [biography]

Shailja is a post-doctoral fellow at Stanford University. She completed her Ph.D. in the Electrical and Computer Engineering (ECE) Department with interdisciplinary emphasis on College and University teaching at the University of California, Santa Barbara (UCSB) in 2024. She graduated with a bachelor’s degree from the Electrical Engineering Department at the Indian Institute of Technology, Kharagpur in 2016. Shailja received the Winifred and Louis Lancaster Dissertation Award for her PhD thesis. She has also been awarded the Fiona and Michael Goodchild best graduate student mentor award during her PhD. Shailja’s research vision is to develop AI methods for healthcare that ”close-the-loop” between surgeons, research scientists, educators, and engineers.
Mr. Thomas John Williams University of California Merced [biography]

Thomas Williams is a first-year undergraduate student at University of California Merced. Thomas has extensive experience in the tool and die trade and is currently pursuing a bachelors degree in electrical engineering. Thomas enjoys teaching others and is actively interested in improving educational processes to make them more engaging and ideally inspire students to further their education beyond the coursework being taught. Thomas is also interested in computer engineering, chip design, and power electronics.
Dr. Ayush Pandey http://orcid.org/0000-0003-3590-4459 University of California Merced [biography]

Ayush Pandey is an assistant professor of teaching in Electrical Engineering at UC Merced. He is interested in research on control theory and artificial intelligence for the formal design of physical systems. Over the past few years, his research has focused on the development of robustness metrics, safety guarantees, and new inference tools for nonlinear dynamical systems in various application areas. He is also actively extending his research on computational tools to build scalable and open-source educational technologies that make classroom learning more interactive and engaging. He is leading the pedagogy and curriculum design effort for the new Electrical Engineering department at UC Merced.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025

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