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

arXiv:2504.02323 · cs.CL · Submitted 2025-04-03 · Read on arXiv

cs.CL

Submitted: 2025-04-03

Updated: 2026-09-12

Code: https://github.com/claytoncohn/CEAI26_Supplementary_Materials

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Active Learning — 53× in this paper · explained in 6 episodes
• Chain-of-Thought Prompting — 33× in this paper · explained in 2 episodes
• Prompt Engineering — 29× in this paper · explained in 4 episodes
• Human-in-the-Loop — 22× in this paper · explained in 3 episodes
• Accuracy — 18× in this paper · explained in 1 episode
• Hallucination — 16× in this paper · explained in 3 episodes
• Alignment — 15× in this paper · explained in 1 episode
• Evidence-Centered Design — 9× in this paper
• Generalizability — 9× in this paper · explained in 6 episodes
• Explainability — 5× in this paper · explained in 3 episodes
• Chain-of-Thought (CoT) — 4× in this paper · explained in 4 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Iterative Refinement — 4× in this paper · explained in 3 episodes
• Actionability — 3× in this paper · explained in 3 episodes
• Chain-of-Thought (CoT) Prompting — 3× in this paper · explained in 3 episodes
• In-Context Learning — 3× in this paper · explained in 4 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• International Conference of the Learning Sciences — 2× in this paper
• LLM-as-a-Judge — 2× in this paper · explained in 3 episodes
• Sycophancy — 2× in this paper · explained in 4 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Agent Interactions — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Explainable AI (XAI) — 1× in this paper · explained in 7 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Hallucination Mitigation — 1× in this paper · explained in 2 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
• Multimodal approach — 1× in this paper · explained in 2 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Scaffolding — 1× in this paper · explained in 3 episodes
• Science Projects Integrating Computing and Engineering — 1× in this paper

Sources

• Chain-of-Verification Reduces Hallucination in Large Language Models
• Grading Massive Open Online Courses Using Large Language Models
• A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
• A large language model-assisted education tool to provide feedback on open-ended responses
• Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
• APE: Active Learning-based Tooling for Finding Informative Few-shot Examples for LLM-based Entity Matching
• Language Models are Few-Shot Learners
• Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models
• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
• A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

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