Date
Publisher
arXiv
Research on artificial intelligence in education (AIED) is rapidly expanding, yet technical progress often lacks human-centered grounding and adequate attention to cultural context. Community-Based Learning, a pedagogy rooted in social work, remains underrepresented in AIED research, particularly within Asia-Pacific contexts. This paper reports on cross-boundary Community-Based Learning where undergraduate students develop AI-enabled solutions for cultural heritage preservation and sustainable development. We examine how community-engaged computing operationalizes culturally aware, human-centered AIED through participatory elicitation of cultural knowledge, bilingual representation, and stakeholder validation across education, technology, and culture. We contribute a collaborative framework for culturally aware AIED designed to support multi-stakeholder collaboration and widen participation by bridging social work and computational science.
Study at a glance
- Question: How can community-engaged student projects integrate local stakeholder input to build culturally aware AI tools?
- Setting and data: Duke Kunshan University, China, Fall 2025; bounded case study of two undergraduate students partnering with three museum educators.
- Method: Participatory action research within a bounded case study.
Why it matters
Technical progress in AI in education often lacks human-centered grounding and cultural context. Community-based learning pedagogies remain underrepresented in the field, especially within Asia-Pacific contexts.
Key findings
- Collaborative fieldwork with museum educators enabled undergraduate students to identify local culinary heritage and develop a bilingual interactive cultural map interface.
- Presenting prototypes to community partners fostered student accountability and shifted learners from passive data sources to active knowledge producers and co-authors.
- Releasing the prototype codebase under an open-source MIT license enabled longitudinal community reuse beyond course boundaries without needing semantic consensus among stakeholders.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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