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

2025-11-07

AI Summary

Current AI literacy education often relies either on programming-based activities or abstract theoretical instruction, limiting accessibility for non-STEM learners. To address this, we designed and empirically validated a no-code, modular online course centered on authentic AI applications—including natural language processing, computer vision, and responsible AI—delivered through contextualized case studies, role-playing simulations, and exploratory tasks to reduce cognitive load and foster experiential learning. Our approach uniquely integrates iterative, teacher-feedback-driven pedagogical refinement and establishes a scalable, interdisciplinary AI education framework. A web-based interactive platform enables low-barrier, high-engagement learning pathways. Empirical evaluation with in-service teachers demonstrated strong acceptance, high classroom applicability, and pedagogical effectiveness—validating the viability and scalability of the no-code, contextually grounded design across diverse learner populations.

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Applications
• Machine Learning: Efficient ML / Green AI

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Developing accessible AI literacy curricula for non-STEM learners
• Exploring experiential teaching methods for AI concepts without coding
• Evaluating instructor perspectives on interactive AI learning activities

Innovation

• Web-based curriculum with no-code interactive projects
• Real-world scenarios for teaching AI concepts
• Modular design adaptable for diverse learners