Community adults entering an AI education session held broad, generalized concerns about AI, including distrust of self-regulation and privacy worries¶
Claim · All claims
Evidence · 1 study · associationalr1·q2
Currency · no dated study recorded · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Pre-session survey responses showed broad and generalized concerns about AI, including distrust of technology companies' self-regulation (76%), fears of societal harm (74%), and privacy worries (75%). → pre-survey-1
Evidence¶
pre-survey-1¶
Yao Lyu, Leonymae Aumentado, Holden Winton, Jared Lee Katzman, Sparkle Berry, Zachary Rowe, Kimberly Sanders, and Tawanna R. Dillahunt. 2026. Co-Designing Community-Centered AI Education for Adults: A Midwestern Case Study. CSCW Companion '26. https://doi.org/10.1145/3785651.3831486
q2 · i? · associational · r1
Descriptive analysis of the pre-session survey completed by 37 of 54 attendees at a community AI education session. Responses showed "broad and generalized concerns about AI", with distrust of self-regulation at 76% (26 of 35) and privacy worries at 75% (21 of 28). No inferential tests were reported.
"In the pre-survey, attendees’ responses showed broad and generalized concerns about AI, including skepticism about trustworthiness (25 of 35, 71%), fears of large-scale societal harm (26 of 35, 74%), distrust of technology companies’ ability to self-regulate (26 of 35, 76%), and worries about privacy (21 of 28, 75%)."
Discussion¶
Related Claims¶
- After the co-designed AI session, community members' concerns about AI persisted but became more specific, concrete, and actionable — related
- Faculty members and students identify both user-related and algorithm-related ethical risks in AI use in education — related
- The review identifies data privacy, algorithmic fairness, academic integrity, and unequal access as interconnected ethical concerns in AI-supported mathematics education — related
- Students' reservations about AI feedback centre on trustworthiness (data privacy) and goodwill (impersonal, demotivating feedback) — related
- The AI education session was associated with a drop in attendees reporting they did not know enough to say about AI, from 23% to 0% — related
- Educators' use of AI in PBL is accompanied by persistent ethical concerns about data privacy, algorithmic bias, and educator autonomy — related
- Surveys show young adults are the heaviest educational ChatGPT users and most worry about data privacy — related
- Universities converge on privacy-oriented data guardrails, warning against entering sensitive data into consumer AI tools and encouraging institutionally approved services — related
- Institutional approval processes, privacy regulations, technology access, and limited classroom time constrained chatbot adoption beyond instructional value — related
- Adult learners raised data-practice concerns that did not map onto the Community of Inquiry framework, including not knowing what data is collected and where it goes — related
- Scholars raise three main concerns about AI chatbots in education: reliability and accuracy, fair assessment, and ethical issues — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
community-adults-broad-generalized-ai-concerns |
title |
Community adults entering an AI education session held broad, generalized concerns about AI, including distrust of self-regulation and privacy worries |
description |
Community adults entering an AI education session held broad, generalized concerns about AI, including distrust of self-regulation and privacy worries |
status |
review |
evidence_strength |
moderate |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — pre-survey-1 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.