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New AI method enhances educational assessment scoring with human feedback

Researchers have developed CoTAL, a new approach using large language models for formative assessment scoring and feedback in educational settings. This method integrates Evidence-Centered Design, human-in-the-loop prompt engineering with chain-of-thought prompting, and iterative refinement based on teacher and student feedback. CoTAL has demonstrated significant improvements in GPT-4's scoring accuracy across various academic domains, outperforming baseline methods. AI

IMPACT This approach could improve the efficiency and accuracy of educational assessments, providing better feedback to students and teachers.

RANK_REASON The cluster contains an academic paper detailing a new methodology for AI in education. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Clayton Cohn, Ashwin T S, Naveeduddin Mohammed, Gautam Biswas ·

    CoTAL: Human-in-the-Loop Prompt Engineering for Generalizable Formative Assessment Scoring and Feedback

    arXiv:2504.02323v4 Announce Type: replace Abstract: Large language models (LLMs) have created new opportunities to assist teachers and support student learning. While researchers have explored various prompt engineering approaches in educational contexts, the degree to which thes…