The erasure of Indigenous cultures is a painfully enduring legacy of U.S. imperial history, producing disparities in culturally-relevant curricula, thus barring Indigenous students from resonant educational experiences. In Hawaiʻi, where Hawaiian instruction was banned for 91 years, culturally-relevant curricula gaps are most severe in Hawaiian-language education and technical fields like Computer Science (CS). Large Language Models (LLMs) are positioned to address these disparities, yet raise concerns of cultural misrepresentation and data sovereignty. We ask: how can LLMs serve educators of varying familiarity with Hawaiian culture while safeguarding Hawaiian knowledge? Addressing this question, we present Kumu Connect, an LLM-assisted culturally-revitalizing CS lesson plan generator, co-designed with Native Hawaiian educators. This work contributes design requirements, a situated case study, and “future imaginaries” paired with design provocations to highlight how community-engaged development can inform culturally-authentic and pedagogically useful AI tools that address CS and culture knowledge gaps and uphold Indigenous data sovereignty.

Although generative AI is being deployed into classrooms with promises of aiding teachers, educators caution that these tools can have unintended pedagogical repercussions, including cultural misrepresentation and bias. These concerns are heightened in low-resource language and Indigenous education settings, where AI systems frequently underperform. We investigate these challenges in Hawai‘i, where public schools operate under a statewide mandate to integrate Hawaiian language and culture into education. Through four co-design workshops with 22 public school educators, we surfaced concerns about using generative AI in educational settings, particularly around cultural misrepresentation, and corresponding designs for auditing tools that address these issues. We find that educators envision tools grounded in specific Hawaiian cultural values and practices, such as tracing the genealogy of knowledge in source materials. Building on these insights, we conceptualize AI auditing as a community-oriented process rather than the work of isolated individuals, and discuss implications for designing auditing tools.