Summary of How Can I Improve? Using Gpt to Highlight the Desired and Undesired Parts Of Open-ended Responses, by Jionghao Lin et al.
How Can I Improve? Using GPT to Highlight the Desired and Undesired Parts of Open-ended Responses
by Jionghao Lin, Eason Chen, Zeifei Han, Ashish Gurung, Danielle R. Thomas, Wei Tan, Ngoc Dang Nguyen, Kenneth R. Koedinger
First submitted to arxiv on: 1 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 A medium-difficulty summary of this research paper would explain how large language models, specifically Generative Pre-Trained Transformers (GPT), can be used to develop an automated explanatory feedback system for enhancing learning. The study leverages the capabilities of GPT models to identify components of desired and less desired praise in a tutor training dataset, with the aim of providing actionable, explanatory feedback during online training lessons. The authors employ two approaches: prompting and fine-tuning, and introduce a Modified Intersection over Union (M-IoU) score to quantify the quality of highlighted praise components identified by GPT models. The findings show that using two-shot prompting on GPT-3.5 results in decent performance in recognizing effort-based and outcome-based praise, while optimally fine-tuned GPT-3.5 model achieves high M-IoU scores for both types of praise, aligning with human judgments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses artificial intelligence to help teachers give better feedback to students during online learning lessons. The goal is to make the feedback more helpful and specific by identifying what students do well and what they need to improve on. To achieve this, the researchers use a type of AI called Generative Pre-Trained Transformers (GPT) to analyze tutor training data. They test two ways of using GPT: prompting it with examples or fine-tuning its performance through practice. The results show that GPT can be used to recognize different types of praise and provide feedback that is similar to what human teachers would give. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Online learning » Prompting
