"I Like That You Have to Poke Around": Instructors on How Experiential Approaches to AI Literacy Spark Inquiry and Critical Thinking

TL;DR

To address AI literacy beyond computer science, the paper evaluates AI User, a modular no-code curriculum featuring scenario-based, interactive projects. Focusing on Projects 5–8, the study collects four focus groups with 15 community college instructors and analyzes responses via reflexive thematic analysis to assess instructional value, realism of role-based simulations, and design trade-offs. The findings indicate that exploratory, adaptive activities with professional roles promote engagement, critical thinking, and interdisciplinary reasoning, while highlighting needs for onboarding, scaffolding, and careful management of cognitive load. These insights inform scalable, inclusive AI education resources across disciplines and learner backgrounds, with classroom pilots proposed for future validation.

Abstract

As artificial intelligence (AI) increasingly shapes decision-making across domains, there is a growing need to support AI literacy among learners beyond computer science. However, many current approaches rely on programming-heavy tools or abstract lecture-based content, limiting accessibility for non-STEM audiences. This paper presents findings from a study of AI User, a modular, web-based curriculum that teaches core AI concepts through interactive, no-code projects grounded in real-world scenarios. The curriculum includes eight projects; this study focuses on instructor feedback on Projects 5-8, which address applied topics such as natural language processing, computer vision, decision support, and responsible AI. Fifteen community college instructors participated in structured focus groups, completing the projects as learners and providing feedback through individual reflection and group discussion. Using thematic analysis, we examined how instructors evaluated the design, instructional value, and classroom applicability of these experiential activities. Findings highlight instructors' appreciation for exploratory tasks, role-based simulations, and real-world relevance, while also surfacing design trade-offs around cognitive load, guidance, and adaptability for diverse learners. This work extends prior research on AI literacy by centering instructor perspectives on teaching complex AI topics without code. It offers actionable insights for designing inclusive, experiential AI learning resources that scale across disciplines and learner backgrounds.

Figures (5)

• Figure 1: An example of the visual storyboard style format, introducing a computer vision scenario for wildlife monitoring.
• Figure 2: An example of an interactive activity where learners evaluate different types of computer vision models for animal identification and counting.
• Figure 3: An interactive red-teaming activity where learners interact engage directly with a simulated medical chat-bot to uncover confidential patient data.
• Figure 4: An exploratory activity where learners take on the role of a junior analyst and interact with simulated characters, such as a data scientist and project manager, to investigate real-world use cases of text-based AI applications.
• Figure 5: An interactive activity in which learners evaluate whether a given client should be considered for housing support based on an AI-generated risk score.