"I Would Never Trust Anything Western": Kumu (Educator) Perspectives on Use of LLMs for Culturally Revitalizing CS Education in Hawaiian Schools

TL;DR

This study explores Hawaiian educators' perspectives on using large language models to support culturally responsive computer science education, highlighting benefits, challenges, and design recommendations for culturally aligned AI tools.

Contribution

It provides novel insights into the cultural considerations and educator perceptions of LLMs in Indigenous Hawaiian educational contexts, an area previously understudied.

Findings

AI saves educators' time in curriculum development

Cultural misalignment and reliability are key challenges

Recommendations for culturally grounded AI tool design

Abstract

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.