Learning AI Without a STEM Background: Mixed-Methods Evidence from a Diverse, Mixed-Cohort AIED Program

A new NSF-funded study, 'Learning AI Without a STEM Background,' by Valentina Kuskova, Dmitry Zaytsev, and Richard Johnson, presents a mixed-cohort AI education model that intentionally includes non-STEM undergraduates and adult learners. The program focuses on ethical reasoning, socio-technical judgment, and applied AI literacy rather than technical skills alone. Quantitative results showed significant gains in confidence and perceived relevance of AI across all participants, while qualitative analyses highlighted responsibility, judgment, and contextual reasoning as key outcomes. Instructors and mentors reported high engagement, especially during dialogic and scenario-based activities.

The study argues that ethical judgment should be a core learning outcome in AI education, alongside AI literacy. It offers design implications for expanding AI access in policy and workforce contexts, emphasizing human-centered supports like ethical scaffolding, mentorship, and structured discussion. This approach challenges the norm of targeting AI education only at STEM-prepared students, broadening participation for public-interest and career development.