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AI Literacy for Community Colleges: Instructors' Perspectives on Scenario-Based and Interactive Approaches to Teaching AI

Aparna Maya Warrier, Arav Agarwal, Jaromir Savelka, Christopher A Bogart, Heather Burte

TL;DR

AI literacy is increasingly essential across disciplines, yet scalable, non‑technical teaching tools are scarce. The study develops AI User, a web‑based, no‑code, scenario‑based curriculum, and analyzes four focus groups with community college instructors using reflexive thematic analysis. Key findings show instructors value visual narratives, animated simulations, and collaborative, iterative activities, while noting challenges in scaffolding, maintaining depth, and ensuring accessibility; interactive demonstrations are preferred over traditional materials. The work provides design recommendations to scale accessible AI literacy across diverse higher‑education contexts and non‑STEM disciplines.

Abstract

This research category full paper investigates how community college instructors evaluate interactive, no-code AI literacy resources designed for non-STEM learners. As artificial intelligence becomes increasingly integrated into everyday technologies, AI literacy - the ability to evaluate AI systems, communicate with them, and understand their broader impacts - has emerged as a critical skill across disciplines. Yet effective, scalable approaches for teaching these concepts in higher education remain limited, particularly for students outside STEM fields. To address this gap, we developed AI User, an interactive online curriculum that introduces core AI concepts through scenario - based activities set in real - world contexts. This study presents findings from four focus groups with instructors who engaged with AI User materials and participated in structured feedback activities. Thematic analysis revealed that instructors valued exploratory tasks that simulated real - world AI use cases and fostered experimentation, while also identifying challenges related to scaffolding, accessibility, and multi-modal support. A ranking task for instructional support materials showed a strong preference for interactive demonstrations over traditional educational materials like conceptual guides or lecture slides. These findings offer insights into instructor perspectives on making AI concepts more accessible and relevant for broad learner audiences. They also inform the design of AI literacy tools that align with diverse teaching contexts and support critical engagement with AI in higher education.

AI Literacy for Community Colleges: Instructors' Perspectives on Scenario-Based and Interactive Approaches to Teaching AI

TL;DR

AI literacy is increasingly essential across disciplines, yet scalable, non‑technical teaching tools are scarce. The study develops AI User, a web‑based, no‑code, scenario‑based curriculum, and analyzes four focus groups with community college instructors using reflexive thematic analysis. Key findings show instructors value visual narratives, animated simulations, and collaborative, iterative activities, while noting challenges in scaffolding, maintaining depth, and ensuring accessibility; interactive demonstrations are preferred over traditional materials. The work provides design recommendations to scale accessible AI literacy across diverse higher‑education contexts and non‑STEM disciplines.

Abstract

This research category full paper investigates how community college instructors evaluate interactive, no-code AI literacy resources designed for non-STEM learners. As artificial intelligence becomes increasingly integrated into everyday technologies, AI literacy - the ability to evaluate AI systems, communicate with them, and understand their broader impacts - has emerged as a critical skill across disciplines. Yet effective, scalable approaches for teaching these concepts in higher education remain limited, particularly for students outside STEM fields. To address this gap, we developed AI User, an interactive online curriculum that introduces core AI concepts through scenario - based activities set in real - world contexts. This study presents findings from four focus groups with instructors who engaged with AI User materials and participated in structured feedback activities. Thematic analysis revealed that instructors valued exploratory tasks that simulated real - world AI use cases and fostered experimentation, while also identifying challenges related to scaffolding, accessibility, and multi-modal support. A ranking task for instructional support materials showed a strong preference for interactive demonstrations over traditional educational materials like conceptual guides or lecture slides. These findings offer insights into instructor perspectives on making AI concepts more accessible and relevant for broad learner audiences. They also inform the design of AI literacy tools that align with diverse teaching contexts and support critical engagement with AI in higher education.

Paper Structure

This paper contains 23 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Challenges in Designing Accessible AI Education
  4. Challenges in Integrating AI into Existing Curricula
  5. Instructor Perspectives in AI Literacy Education
  6. Methodology
  7. Participants
  8. Focus Group Design
  9. Rose, Bud, Thorn Activity
  10. Ranking Activity
  11. Data Analysis
  12. Results
  13. Visual Narratives to Support Early Engagement and Understanding
  14. Simulated Environments and Animation to Support Explainability
  15. Interactive Activities for Iterative and Applied Learning
  16. ...and 8 more sections

Figures (3)

  • Figure 1: Geographic distribution of community college instructors who participated in the study.
  • Figure 2: Snapshot of the motivation task introducing the predictive maintenance scenario in AI User. The scenario presents a real-world context to learners in which an aviation company is exploring the use of AI to implement predictive maintenance for its airplanes.
  • Figure 3: An interactive activity in AI User where learners build an image dataset for autonomous vehicles that involves selecting and refining visual data to improve an AI system's ability to recognize stop signs.