Co-Designing Community-Centered AI Education for Adults: A Midwestern Case Study

Synthesis: This case study reports on a community-based participatory research project that co-designed an AI literacy program for 54 adults (48 in-person and 6 virtual) in a predominantly African American community in the Midwestern United States. The program covered fundamental AI concepts, societal implications, and practical applications, using hands-on activities and concrete examples over abstract technical explanations.

Key findings highlight that equity-oriented AI education for underserved adult populations must address foundational digital literacy gaps, build trust around data privacy, and connect AI concepts to everyday lived experiences. This challenges the dominant focus on formal higher education settings for AI literacy initiatives and points toward more inclusive lifelong learning models.

The study contributes design considerations for educational technology developers and educators seeking to reach adults outside traditional educational institutions. The community-centered approach offers a replicable model for equity-focused AI education that prioritizes relevance, trust, and Accessibility over technical depth.

What this means for practice

• Faculty developers. Anchor every activity in local, concrete material instead of abstract technical explanation: an image-spotting game using local landmarks and sports teams drew strong engagement, and attendees identified AI-generated images correctly in 82% of responses.
• Faculty developers. Design the event around participation-driven logistics — timing, location, hybrid access, transportation and room arrangement — because one session reached 48 attendees in person and 6 virtually only through active facilitation and accessibility support.
• Learners. Say your concern out loud in the room even if it feels unresolved: pre-session worries about privacy (21 of 28, 75%) and technology companies' ability to self-regulate (26 of 35, 76%) persisted afterwards, but became more specific and actionable.
• Faculty developers. Present AI as neither only beneficial nor only harmful and surface the tension directly: the session left underlying concerns intact while taking the share answering "don't know/not familiar enough to say (about AI)" from 23% (8 of 35) in the pre-survey to 0% (0 of 24) in the post-survey.
• Faculty developers. Treat interactive polls as a scaffold for discussion rather than an assessment, and follow the adult learning principle of building on what attendees already know rather than starting from technical mechanisms.

Limitations

• Single-site case study: one education session in a predominantly African American community on the east side of one Midwestern city, with 54 attendees (48 in person and 6 virtual), so no claim about other communities or about repetition over time is supported.
• Response coverage was partial and self-reported: 37 of the 54 attendees completed the pre-survey and roughly 22-25 completed the post-survey depending on the question, and only 25 participants provided demographic information.
• The sample skews older than typical adult learners: 22 participants reported ages, and 17 of them were 55 or above; prior schooling ranged from 10 with college-level education and 8 with some college to 6 with a high school education or less.
• Interpretations may reflect the research team's own commitments: the authors acknowledge their commitments, expertise and relationships shaped the analysis, and the senior author, who also served as PI, has maintained decade-long collaborative relationships with both community partners.

Citation

Yao Lyu, Leonymae Aumentado, Holden Winton, Jared Lee Katzman, Sparkle Berry, Zachary Rowe, Kimberly Sanders, Tawanna R. Dillahunt (2026). Co-Designing Community-Centered AI Education for Adults: A Midwestern Case Study. arXiv cs.HC.