Zachary Schultz

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2025

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Examining decoding items using engine transcriptions and scoring in early literacy assessment
Zachary Schultz | Mackenzie Young | Debbie Dugdale | Susan Lottridge
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress

We investigate the reliability of two scoring approaches to early literacy decoding items, whereby students are shown a word and asked to say it aloud. Approaches were rubric scoring of speech, human or AI transcription with varying explicit scoring rules. Initial results suggest rubric-based approaches perform better than transcription-based methods.
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