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How Can I Get It Right? Using GPT to Rephrase Incorrect Trainee Responses
Our findings indicate that: 1) using a few-shot approach, the GPT-4 model effectively identifies correct/incorrect trainees' responses from three training lessons with an average F1 score of 0. 84 and an AUC score of 0. 85; and 2) using the few-shot approach, the GPT-4 model adeptly rephrases incorrect trainees' responses into desired responses, achieving performance comparable to that of human experts.
Improving Assessment of Tutoring Practices using Retrieval-Augmented Generation
The results indicate that the RAG prompt demonstrated more accurate performance (assessed by the level of hallucination and correctness in the generated assessment texts) and lower financial costs than the other strategies evaluated.
Using Large Language Models to Assess Tutors' Performance in Reacting to Students Making Math Errors
By analyzing 50 real-life tutoring dialogues, we find both GPT-3. 5-Turbo and GPT-4 demonstrate proficiency in assessing the criteria related to reacting to students making errors.
Using Large Language Models to Provide Explanatory Feedback to Human Tutors
Research demonstrates learners engaging in the process of producing explanations to support their reasoning, can have a positive impact on learning.
