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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
Language: English
Authors: Jionghao Lin (ORCID 0000-0003-3320-3907), Zifei Han, Danielle R. Thomas, Ashish Gurung, Shivang Gupta, Vincent Aleven, Kenneth R. Koedinger
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
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  Data: <searchLink fieldCode="AR" term="%22Jionghao+Lin%22">Jionghao Lin</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-3320-3907">0000-0003-3320-3907</externalLink>)<br /><searchLink fieldCode="AR" term="%22Zifei+Han%22">Zifei Han</searchLink><br /><searchLink fieldCode="AR" term="%22Danielle+R%2E+Thomas%22">Danielle R. Thomas</searchLink><br /><searchLink fieldCode="AR" term="%22Ashish+Gurung%22">Ashish Gurung</searchLink><br /><searchLink fieldCode="AR" term="%22Shivang+Gupta%22">Shivang Gupta</searchLink><br /><searchLink fieldCode="AR" term="%22Vincent+Aleven%22">Vincent Aleven</searchLink><br /><searchLink fieldCode="AR" term="%22Kenneth+R%2E+Koedinger%22">Kenneth R. Koedinger</searchLink>
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  Data: 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/
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  Data: 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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