"I Would Never Trust Anything Western": Kumu (Educator) Perspectives on Use of LLMs for Culturally Revitalizing CS Education in Hawaiian Schools
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As large language models (LLMs) become increasingly integrated into educational technology, their potential to assist in developing curricula has gained interest among educators. Despite this growing attention, their applicability in culturally responsive Indigenous educational settings like Hawai`i's public schools and Kaiapuni (immersion language) programs, remains understudied. Additionally, `Olelo Hawai`i, the Hawaiian language, as a low-resource language, poses unique challenges and concerns about cultural sensitivity and the reliability of generated content. Through surveys and interviews with kumu (educators), this study explores the perceived benefits and limitations of using LLMs for culturally revitalizing computer science (CS) education in Hawaiian public schools with Kaiapuni programs. Our findings highlight AI's time-saving advantages while exposing challenges such as cultural misalignment and reliability concerns. We conclude with design recommendations for future AI tools to better align with Hawaiian cultural values and pedagogical practices, towards the broader goal of trustworthy, effective, and culturally grounded AI technologies.
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These failures were seen as evidence that systems built on opaque, extractive pipelines cannot align with Hawaiian educational sovereignty without radical redesign.
“These failures were seen not as glitches but as evidence that systems built on opaque, extractive pipelines cannot align with Hawaiian educational sovereignty without radical redesign (Mhasakar et al., 2025).”
From Towards a decolonial AI sovereignty model for indigenous educational settings (2026) · cites this paper as Mhasakar et al., 2025
Section 4.2 (page unavailable) reports educators’ concerns about opaque sources, Western-biased training data, and outputs that miss Hawaiian schools’ needs. Section 5.1 recommends transparent sourcing and culturally aligned defaults. The paper does not establish that the systems use extractive pipelines or that alignment with Hawaiian educational sovereignty is impossible without radical redesign; those are stronger claims than its findings and recommendations. (LLM, conf=0.86)
Experimental — accuracy not yet measuredKaiapuni educators described LLMs as epistemically unreliable-producing incorrect, unsafe, or culturally inauthentic outputs, omitting Indigenous knowledge, and risking algorithmic commodification of cultural heritage, threatening language revitalisation work.
“Educators described LLMs as epistemically unreliableproducing incorrect, unsafe, or culturally inauthentic outputs, omitting Indigenous knowledge, and risking algorithmic commodification of cultural heritage, threatening language revitalisation work (Mhasakar et al., 2025).”
From Towards a decolonial AI sovereignty model for indigenous educational settings (2026) · cites this paper as Mhasakar et al., 2025
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredWhere sovereignty is absent, engagement collapses into critique without control, as Hawaiian educators' experience with unreliable LLM outputs illustrates.
“Where sovereignty is absent, by contrast, engagement collapses into critique without control, as Hawaiian educators' experience with unreliable LLM outputs illustrates (Mhasakar et al., 2025).”
From Towards a decolonial AI sovereignty model for indigenous educational settings (2026) · cites this paper as Mhasakar et al., 2025
Section 4.2 (page unavailable) reports educators’ hesitation about opaque sources and concern that AI outputs reflect colonized perspectives. Section 4.5 (page unavailable) documents their criticism of inaccurate and culturally misaligned outputs. The paper does not establish that absent sovereignty caused engagement to collapse into critique without control; educators also described potential benefits and proposed changes to the tools. (LLM, conf=0.85)
Experimental — accuracy not yet measuredCritical digital pedagogy emphasises dialogue and reflexivity, practices evident in Hawaiian educators' use of LLM failures to cultivate critical literacy.
“Critical digital pedagogy emphasises dialogue and reflexivity, practices evident in Hawaiian educators' use of LLM failures to cultivate critical literacy (Mhasakar et al., 2025).”
From Towards a decolonial AI sovereignty model for indigenous educational settings (2026) · cites this paper as Mhasakar et al., 2025
Section 4.5 (page unavailable) reports that Hawaiian educators identify LLM misinformation and cultural misrepresentation and stress checking AI-generated work. It does not report educators using those failures in dialogue with students to cultivate critical literacy; nor does it establish the claim about dialogue and reflexivity in critical digital pedagogy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredHawaiian Kaiapuni educators engaged LLMs in culturally grounded computer science education, framing epistemically unreliable outputs as opportunities for critical digital literacy.
“Hawaiian Kaiapuni educators engaged LLMs in culturally grounded computer science education, framing epistemically unreliable outputs as opportunities for critical digital literacy (Mhasakar et al., 2025).”
From Towards a decolonial AI sovereignty model for indigenous educational settings (2026) · cites this paper as Mhasakar et al., 2025
Section 3 describes surveys and interviews with educators in schools with Kaiapuni programs, and Section 4.5 reports their interest in AI-assisted, culturally relevant lessons. But Section 4.5 treats inaccurate or culturally misrepresentative outputs as risks to identify and prevent; it does not say educators framed those outputs as opportunities for critical digital literacy. No page numbers are available in our copy. (LLM, conf=0.87)
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Towards a decolonial AI sovereignty model for indigenous educational settings
Mbah, M. F., & Jarvis, L. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2626631 · Cited by 7 · see citing works
- Authors
- Mhasakar, M., Baker-Ramos, R., Carter, B., Helekahi-Kaiwi, E.-B., & Hester, J.
- Affiliations
- Georgia Institute of Technology (US)
- Lau By Keha LLC, Phoenix, Arizona, USA
- Venue
- Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2025-04-25
- Pages
- 1-10
- OpenAlex
- W4409720519
- Cited by
- 6
- References
- 9
- Citations by year
- 2026: 5 · 2025: 1
- FWCI
- 68.71
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Science Foundation (2137784)
- Topics
- Social Sciences › Education › Indigenous and Place-Based Education
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- large language models, indigenous education, Hawaiian language, artificial intelligence in education
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