Bridging Psychometric and Content Development Practices with AI: A Community-Based Workflow for Augmenting Hawaiian Language Assessments

Abstract: This paper presents the design and evaluation of a community-based AI workflow developed for the Kaiapuni Assessment of Educational Outcomes (KĀ'EO) program, the only native language assessment used for federal accountability in the United States. The project explored whether document-grounded LLMs could ethically and effectively augment human analysis of item performance while preserving the cultural and linguistic integrity of the Hawaiian language. Operating under the KĀ'EO AI Policy Framework, the workflow used NotebookLM for cross-document synthesis of psychometric data and Claude 3.5 Sonnet for developer-facing interpretation, with human oversight at every stage. Fifty-eight flagged items across Hawaiian Language Arts, Mathematics, and Science were reviewed during Round 2 of the AI Lab, producing six interpretive briefs that identified systemic design issues such as linguistic ambiguity, Depth-of-Knowledge (DOK) misalignment, and structural overload. The findings demonstrate that AI can serve as an ethically bounded amplifier of human expertise, accelerating analysis while simultaneously prioritizing fairness, human expertise, and cultural authority. This work offers a replicable model for responsible AI integration in Indigenous-language educational measurement.