Culturally-Aware AI for Cross-Boundary Community Learning: Undergraduate Innovation at the Intersection of Computation

Synthesis: Reports on cross-boundary Community-Based Learning where undergraduate students develop AI-enabled solutions for cultural heritage preservation and sustainable development. The paper argues that AIED research often lacks human-centered grounding and adequate attention to cultural context, and that Community-Based Learning — a Pedagogies and Teaching Strategies rooted in social work — remains underrepresented in AIED, particularly within Asia-Pacific contexts.

Contributions: Examines how community-engaged computing operationalizes human-centered AIED across three dimensions: education, technology, and culture. Proposes a collaborative framework for culturally-aware AIED that fosters multi-stakeholder collaboration while widening participation by dissolving disciplinary silos between social work and computational science.

The framework emphasizes cultural contextualization of AI tools, community-driven problem identification, and student-centered design processes. Relevant to higher education contexts seeking to integrate AI education with socially meaningful projects.

What this means for practice

• Instructors. Sequence community-engaged AI design as three explicit events rather than an open-ended project: Week 3 field co-design at the museum partner, Week 4 alignment of proposals with named UN Sustainable Development Goals (the project used SDG 4, SDG 8, SDG 11 and SDG 17), and Week 7 mutual validation in front of community partners, staff and peers.
• Instructors. Recruit community partners as co-designers with a defined role — the museum contributed three bilingual (Mandarin/English) educators — so that validation criteria come from the community rather than from the course rubric.
• Learners. Design for the partner's real audience and constraints: the students shipped a bilingual, high-contrast culturally contextualized interface because the museum's audiences read Chinese and English and included visitors needing accessibility support.
• Instructors. Treat learners as knowledge producers by releasing student work as open-source artifacts and giving students a stated authorial role, which moved them from "data sources" to co-authors analyzing their own learning.
• Administrators. Keep human review in the loop for any AI-generated output that reaches the public or a partner, since the reported workflow assumes systematic review of AI outputs by the project team.

Limitations

• The evidence is a bounded case study of just 2 undergraduate students — the first two authors of the paper — so the researchers are also the participants, and the account of their agency is partly self-report.
• All design claims come from a single 7-week course section, one museum partner, and one interdisciplinary Computation and Design major at one university in China, even though the course cap is N = 18 students drawn from three divisions.
• The authors claim analytical rather than statistical generalization, and name multi-site and longitudinal validation as future work; as a single-site pilot, generalizability is unestablished.
• Only three museum educators acted as community partners and the prototype was validated through participatory feedback at Week 7 rather than through comparative evaluation against human-only baselines, which the authors also list as outstanding.

Citation

Zhao, J., Zhang, W., Cai, J., Gao, H., & Zhang, L. (2026). Culturally-Aware AI for Cross-Boundary Community Learning: Undergraduate Innovation at the Intersection of Computation and Design.