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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.
How Can I Improve? Using GPT to Highlight the Desired and Undesired Parts of Open-ended Responses
To quantify the quality of highlighted praise components identified by GPT models, we introduced a Modified Intersection over Union (M-IoU) score.
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.
Comparative Analysis of GPT-4 and Human Graders in Evaluating Praise Given to Students in Synthetic Dialogues
We found that both zero-shot and few-shot chain of thought approaches yield comparable results.
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.
