Do Tutors Learn from Equity Training and Can Generative AI Assess It?
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915065309954048 |
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| author | Thomas, Danielle R. Borchers, Conrad Kakarla, Sanjit Lin, Jionghao Bhushan, Shambhavi Guo, Boyuan Gatz, Erin Koedinger, Kenneth R. |
| author_facet | Thomas, Danielle R. Borchers, Conrad Kakarla, Sanjit Lin, Jionghao Bhushan, Shambhavi Guo, Boyuan Gatz, Erin Koedinger, Kenneth R. |
| contents | Equity is a core concern of learning analytics. However, applications that teach and assess equity skills, particularly at scale are lacking, often due to barriers in evaluating language. Advances in generative AI via large language models (LLMs) are being used in a wide range of applications, with this present work assessing its use in the equity domain. We evaluate tutor performance within an online lesson on enhancing tutors' skills when responding to students in potentially inequitable situations. We apply a mixed-method approach to analyze the performance of 81 undergraduate remote tutors. We find marginally significant learning gains with increases in tutors' self-reported confidence in their knowledge in responding to middle school students experiencing possible inequities from pretest to posttest. Both GPT-4o and GPT-4-turbo demonstrate proficiency in assessing tutors ability to predict and explain the best approach. Balancing performance, efficiency, and cost, we determine that few-shot learning using GPT-4o is the preferred model. This work makes available a dataset of lesson log data, tutor responses, rubrics for human annotation, and generative AI prompts. Future work involves leveling the difficulty among scenarios and enhancing LLM prompts for large-scale grading and assessment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11255 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Do Tutors Learn from Equity Training and Can Generative AI Assess It? Thomas, Danielle R. Borchers, Conrad Kakarla, Sanjit Lin, Jionghao Bhushan, Shambhavi Guo, Boyuan Gatz, Erin Koedinger, Kenneth R. Human-Computer Interaction Artificial Intelligence Equity is a core concern of learning analytics. However, applications that teach and assess equity skills, particularly at scale are lacking, often due to barriers in evaluating language. Advances in generative AI via large language models (LLMs) are being used in a wide range of applications, with this present work assessing its use in the equity domain. We evaluate tutor performance within an online lesson on enhancing tutors' skills when responding to students in potentially inequitable situations. We apply a mixed-method approach to analyze the performance of 81 undergraduate remote tutors. We find marginally significant learning gains with increases in tutors' self-reported confidence in their knowledge in responding to middle school students experiencing possible inequities from pretest to posttest. Both GPT-4o and GPT-4-turbo demonstrate proficiency in assessing tutors ability to predict and explain the best approach. Balancing performance, efficiency, and cost, we determine that few-shot learning using GPT-4o is the preferred model. This work makes available a dataset of lesson log data, tutor responses, rubrics for human annotation, and generative AI prompts. Future work involves leveling the difficulty among scenarios and enhancing LLM prompts for large-scale grading and assessment. |
| title | Do Tutors Learn from Equity Training and Can Generative AI Assess It? |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2412.11255 |