How Can I Get It Right? Using GPT to Rephrase Incorrect Trainee Responses
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| Title: | How Can I Get It Right? Using GPT to Rephrase Incorrect Trainee Responses |
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| Language: | English |
| Authors: | Jionghao Lin (ORCID |
| Source: | International Journal of Artificial Intelligence in Education. 2025 35(2):482-508. |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 27 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Artificial Intelligence, Technology Uses in Education, Tutor Training, Trainees, Feedback (Response), Natural Language Processing, Intelligent Tutoring Systems, Error Patterns, Error Correction |
| DOI: | 10.1007/s40593-024-00408-y |
| ISSN: | 1560-4292 1560-4306 |
| Abstract: | One-on-one tutoring is widely acknowledged as an effective instructional method, conditioned on qualified tutors. However, the high demand for qualified tutors remains a challenge, often necessitating the training of novice tutors (i.e., trainees) to ensure effective tutoring. Research suggests that providing timely explanatory feedback can facilitate the training process for trainees. However, it presents challenges due to the time-consuming nature of assessing trainee performance by human experts. Inspired by the recent advancements of large language models (LLMs), our study employed the GPT-4 model to build an explanatory feedback system. This system identifies trainees' responses in binary form (i.e., correct/incorrect) and automatically provides template-based feedback with responses appropriately rephrased by the GPT-4 model. We conducted our study using the responses of 383 trainees from three training lessons ("Giving Effective Praise, Reacting to Errors," and "Determining What Students Know"). 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 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. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1488235 |
| Database: | ERIC |
| Abstract: | One-on-one tutoring is widely acknowledged as an effective instructional method, conditioned on qualified tutors. However, the high demand for qualified tutors remains a challenge, often necessitating the training of novice tutors (i.e., trainees) to ensure effective tutoring. Research suggests that providing timely explanatory feedback can facilitate the training process for trainees. However, it presents challenges due to the time-consuming nature of assessing trainee performance by human experts. Inspired by the recent advancements of large language models (LLMs), our study employed the GPT-4 model to build an explanatory feedback system. This system identifies trainees' responses in binary form (i.e., correct/incorrect) and automatically provides template-based feedback with responses appropriately rephrased by the GPT-4 model. We conducted our study using the responses of 383 trainees from three training lessons ("Giving Effective Praise, Reacting to Errors," and "Determining What Students Know"). 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 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. |
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| ISSN: | 1560-4292 1560-4306 |
| DOI: | 10.1007/s40593-024-00408-y |
