From Prediction to Precision: Leveraging LLMs for Equitable and Data-Driven Writing Placement in Developmental Education

Accurate text classification and placement remain challenges in U.S. Gemini and Claude also demonstrated strong correlation with human ratings, with Claude achieving the highest Pearson scores (ρ = 0.75; 1-step, ρ = 0.73; 2-step) vs.

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• 1 higher education, with traditional automated systems like Accuplacer functioning as "black-box" models with limited assessment transparency.
• 2 This study evaluates Large Language Models (LLMs) as complementary placement tools by comparing their classification performance against a human-rated gold standard and Accuplacer.
• 3 A 450-essay corpus was classified using Claude, Gemini, GPT-3.5-turbo, and GPT-4o across four prompting strategies: Zero-shot, Few-shot, Enhanced, and Enhanced+ (definitions with examples).

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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