Learning AI Without a STEM Background: Mixed-Methods Evidence from a Diverse, Mixed-Cohort AIED Program
For educators and policymakers, this work provides evidence that AI literacy can be effectively taught to non-STEM learners through human-centered instructional supports, though the findings are incremental as they confirm known principles in a new context.
This paper evaluates an NSF-funded AI education model that integrates non-STEM undergraduates and adult learners, finding significant gains in confidence and perceived relevance of AI across cohorts, with qualitative emphasis on ethical reasoning and contextual judgment over technical mastery.
Despite growing interest in AI education, most AIED initiatives remain narrowly targeted toward STEM-prepared students, limiting participation by non-STEM learners and adults seeking to engage with AI in public-interest, policy, or workforce contexts. This paper presents and evaluates an NSF-funded, innovative mixed-cohort AI education model that intentionally integrates non-STEM undergraduates and adult learners into a shared learning environment centered on ethical reasoning, socio-technical judgment, and applied AI literacy rather than technical proficiency alone. Drawing on mixed-methods data from course surveys, open-ended reflections, and educator reports, we examine learners' academic agency, confidence navigating AI concepts, critical engagement with ethical tradeoffs, and perceived expansion of postsecondary and career trajectories. Quantitative results indicate significant gains in confidence and perceived relevance of AI across cohorts' participants, while qualitative analyses reveal a consistent emphasis on responsibility, judgment, and contextual reasoning over technical mastery. Instructors and near-peer mentors corroborated high levels of engagement and productive challenge, particularly in dialogic and scenario-based learning activities. Our findings suggest that human-centered instructional supports, such as ethical scaffolding, mentorship, and structured discussion, are essential components of equitable AI education, especially in heterogeneous and non-traditional learner populations. We argue that ethical judgment should be treated as a core learning outcome in AIED alongside AI literacy, and we offer design implications for expanding access to AI education in policy-relevant and workforce-adjacent contexts.