Summary of How Can I Get It Right? Using Gpt to Rephrase Incorrect Trainee Responses, by Jionghao Lin et al.
How Can I Get It Right? Using GPT to Rephrase Incorrect Trainee Responses
by Jionghao Lin, Zifei Han, Danielle R. Thomas, Ashish Gurung, Shivang Gupta, Vincent Aleven, Kenneth R. Koedinger
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this study, researchers developed an AI-powered system that provides explanatory feedback to novice tutors (trainees) in real-time, allowing for more effective training. By employing the GPT-4 large language model, the system identifies and corrects trainee responses, providing template-based feedback that matches human expert performance. The study tested the system on 410 responses from trainees across three training lessons, achieving an average F1 score of 0.84 and AUC score of 0.85 in identifying correct/incorrect responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study created a special machine learning model that helps people who are new to teaching get better at giving feedback. The model uses a big language database called GPT-4, which can understand human speech and generate text. Researchers tested the model on lots of examples from three different training lessons, showing that it’s good at identifying when trainees’ responses are correct or not, and even providing helpful hints to improve their answers. |
Keywords
» Artificial intelligence » Auc » F1 score » Gpt » Large language model » Machine learning
