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We Are AI: Taking Control of Technology
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Describes "We Are AI: Taking Control of Technology," a peer-learning (learning circle) public education course and comic series on AI and responsible AI, and reports pilot outcomes from offerings to librarians and staff at NYU and NYU Abu Dhabi.
Problem
Non-technical members of the public lack accessible, scalable entry points to learn the basics of AI and responsible AI (RAI), while existing RAI education mostly targets technical or organizational audiences.
Approach
A five-module, open-access RAI course run as a self-organizing learning circle with train-the-trainer facilitation and a companion multilingual comic series, requiring no technical background or instructors with deep expertise.
Key results
- Self-assessed RAI understanding rose from 4.08 to 6.33 (Summer 2023) and 4.19 to 5.89 (Fall 2023) on a 10-point scale (weighted averages).
- Two-thirds of respondents (66.7%) reported substantial or very substantial improvement in understanding of AI; all reported at least moderate improvement.
- Two Summer 2023 librarian participants successfully facilitated the Fall 2023 offering, supporting the train-the-trainer model.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Complete the We Are AI course | Non-technical participants in We Are AI · We Are AI, Summer 2023 | self-rated understanding of RAI concepts and techniques (out of 10): 4.08 -> 6.33 | Helped |
| Complete the We Are AI course | Non-technical participants in We Are AI · We Are AI, Fall 2023 | self-rated understanding of RAI concepts and techniques (out of 10): 4.19 -> 5.89 | Helped |
| Complete the We Are AI course | Course participants · We Are AI, Summer 2023 | reported substantial or very substantial improvement in understanding of AI: 66.7% | Helped |
Surprising findings
- Participants still rated their understanding of algorithms as fuzzy or moderate after the course despite overall gains.
Limitations
- Evaluation relies on small self-reported pre/post surveys (15 and 26 participants) from a single institution's librarians and staff.
- Course does not currently cover generative AI, and examples are written from a U.S. perspective.
- Rigorous evaluation of educational effectiveness is acknowledged as still needed.
Experimental setup
Metrics: pre/post self-assessed understanding rating (0-10) survey completion rate qualitative participant feedback
Builds on: learning circles model Teachable Machine
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