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    <abstract>This study leverages deep learning, transformer models, and generative AI to streamline test development by automating metadata tagging and item generation. Transformer models outperform simpler approaches, reducing SME workload. Ongoing research refines complex models and evaluates LLM-generated items, enhancing efficiency in test creation.</abstract>
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%0 Conference Proceedings
%T Evaluating Deep Learning and Transformer Models on SME and GenAI Items
%A Betts, Joe
%A Muntean, William
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
%D 2025
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-218-84229-1
%F betts-muntean-2025-evaluating
%X This study leverages deep learning, transformer models, and generative AI to streamline test development by automating metadata tagging and item generation. Transformer models outperform simpler approaches, reducing SME workload. Ongoing research refines complex models and evaluates LLM-generated items, enhancing efficiency in test creation.
%U https://aclanthology.org/2025.aimecon-wip.17/
%P 141-146

Markdown (Informal)

[Evaluating Deep Learning and Transformer Models on SME and GenAI Items](https://aclanthology.org/2025.aimecon-wip.17/) (Betts & Muntean, AIME-Con 2025)

• Evaluating Deep Learning and Transformer Models on SME and GenAI Items (Betts & Muntean, AIME-Con 2025)

ACL

• Joe Betts and William Muntean. 2025. Evaluating Deep Learning and Transformer Models on SME and GenAI Items. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 141–146, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).