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Dr. R.O. Bott Will See You Now: Exploring AI for Wellbeing with Middle School Students
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Presents AI for Wellbeing, a culturally responsive K-12 curriculum on conversational AI and ethics, and evaluates a 5-day virtual pilot with middle school students and teachers using mixed methods.
Problem
Most people lack baseline AI knowledge, and marginalized youth face barriers to access to AI education, so AI curricula need to be effective, inclusive, and culturally relevant for K-12 learners.
Approach
Builds a conversational-AI curriculum on the Impact.AI framework (AI concepts, practices, and perspectives) that embeds ethics throughout, uses design justice, and lowers access barriers via a client-side block-based AI Playground and a Jibo social robot.
Key results
- Post-test AI knowledge score averaged 71% vs 61% pre-test, not significant (U=63.5, p=0.29; 11 post-test completers).
- Students' self-assessed ability to use AI for social good rose from median 2 to 3 (U=23, p=0.001).
- Of 20 final projects, 8 (40%) targeted medicine and 6 (30%) mental health; 11 submitted code and all ran correctly.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Participate in the AI for Wellbeing workshop | Middle school students · AI knowledge assessment | average score: 61% -> 71%; U = 63.5, p = 0.29 | No clear effect |
| Participate in the AI for Wellbeing workshop | Middle school students · Machine learning knowledge question | score: 32% to 64% | Helped |
| Participate in the AI for Wellbeing workshop | Middle school students · Chatbot knowledge question | score: 37% to 60% | Helped |
| Participate in the AI for Wellbeing workshop | Middle school students · Use AI for social good | self-assessment: pre-test mdn=2, post-test=3, U = 23, p = 0.001 | Helped |
| Participate in the AI for Wellbeing workshop | Middle school students · Analyze ethical consequences of AI artifacts | self-assessment: pre-test mdn=2, post-test=3, U = 38.5, p = 0.01 | Helped |
| Participate in the AI for Wellbeing workshop | Middle school students · Present AI | self-assessment: pre-test mdn=2, post-test=3, U = 39.5, p = 0.01 | Helped |
| Participate in the AI for Wellbeing workshop | Middle school students · Attitudes toward AI | survey scores | No clear effect |
| Use the AI for Wellbeing curriculum and tools to create a final project | Middle school students · Student final projects | projects with at least one potential harm identified: 80% | Mixed |
Surprising findings
- Students from underrepresented groups reported no agreement with being able to rely on their AI skills on the pre-test, though post-test shifted; differences between groups were not statistically significant.
- Knowledge gains were not statistically significant despite high pre-test scores on several items.
Limitations
- Small sample size (23 students, 5 teachers) in an informal virtual learning context.
- Only 11 of 23 students completed the post-test, limiting statistical power.
- Limited time for final projects led many students to closely follow exemplar code.
Experimental setup
Metrics: pre/post knowledge test score Mann-Whitney U test self-assessment medians final project rubric score interrater reliability
Builds on: Impact.AI Culturally Responsive Computing Five Big Ideas in AI Scratch
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