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Fostering Epistemic Insights into AI Ethics through a Constructionist Pedagogy: An Interdisciplinary Approach to AI Literacy
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Presents three constructionist AI-literacy activities (Scratch rule-based classifier, Google Teachable Machine image classifier, Poe generative AI poetry) for 10-12-year-olds to foster AI ethics understanding and epistemic insight, evaluated qualitatively with 34 fifth graders.
Problem
Teaching AI ethics to upper primary students is challenging because it requires interdisciplinary knowledge and epistemic insight into how AI acquires, processes, and applies knowledge differently from humans, and existing hands-on tools lack sustained ethical reflection.
Approach
Grounds a sequence of hands-on activities (rule-based, ML-based, and generative AI) in constructionism and embeds guided reflection questions after each activity so students continuously reflect on fairness, bias, transparency, accountability, non-maleficence and human-vs-AI knowledge differences.
Key results
- Study with 34 fifth-grade students (ages ~11) in an eight-lesson curriculum at a Hong Kong primary school found students recognized bias from unbalanced data and contrasted transparency of rule-based versus ML-based AI.
- Students articulated epistemic differences between humans and machines, e.g., noting that humans learn from few examples while machines need many images.
- Findings are qualitative, drawn from thematic analysis of video, screen recordings, worksheets and cognitive interviews, with no quantitative comparison.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Scratch rule-based banana classification with guided reflection | Upper-primary students with no prior AI experience · AI ethics curriculum, Activity 1 | understanding of transparency | Helped |
| Train and test a banana image classifier using Google Teachable Machine, with guided reflection | Upper-primary students with no prior AI experience · AI ethics curriculum, Activity 2 | understanding of fairness and bias | Helped |
| Compare generative AI poem creation with human poem writing and supervised learning | Upper-primary students with no prior AI experience · AI ethics curriculum, Activity 3 | epistemic insight into human and machine intelligence | Helped |
| Integrate hands-on AI construction with guided ethical reflection across rule-based, ML-based, and generative AI | The eight-lesson AI curriculum · AI ethics curriculum | ethical understanding and epistemic insight | Helped |
Limitations
- Single-site study with one class of 34 students and no control group
- Qualitative evaluation only, without quantitative learning gain measures
- Generative AI activity was shorter (15 minutes) than the other two activities
Experimental setup
Datasets: 60 banana images supplied for Google Teachable Machine classifier training
Metrics: thematic analysis of student interviews and worksheets
Builds on: constructionism epistemic insight
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