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

[2606.09041] Culturally-Aware AI for Cross-Boundary Community Learning: Undergraduate Innovation at the Intersection of Computation and Design Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computers and Society arXiv:2606.09041 (cs) [Submitted on 8 Jun 2026 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: Culturally-Aware AI for Cross-Boundary Community Learning: Undergraduate Innovation at the Intersection of Computation and Design Authors: Jiaojiao Zhao , Weisheng Zhang , Jiawen Cai , Haibin Gao , Luyao Zhang View a PDF of the paper titled Culturally-Aware AI for Cross-Boundary Community Learning: Undergraduate Innovation at the Intersection of Computation and Design, by Jiaojiao Zhao and 4 other authors View PDF HTML (experimental) Abstract: 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. Subjects: Computers and Society (cs.CY) ; Artificial Intelligence (cs.AI); Graphics (cs.GR); Human-Computer Interaction (cs.HC); Multimedia (cs.MM) Cite as: arXiv:2606.09041 [cs.CY] (or arXiv:2606.09041v2 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2606.09041 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Luyao Zhang [ view email ] [v1] Mon, 8 Jun 2026 05:14:01 UTC (1,627 KB) [v2] Sat, 15 Aug 2026 07:39:58 UTC (8,919 KB) Full-text links: Access Paper: View a PDF of the paper titled Culturally-Aware AI for Cross-Boundary Community Learning: Undergraduate Innovation at the Intersection of Computation and Design, by Jiaojiao Zhao and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CY < prev | next > new | recent | 2026-06 Change to browse by: cs cs.AI cs.GR cs.HC cs.MM References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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