How Do Students Interact with an LLM-powered Virtual Teaching Assistant in Different Educational Settings?
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913446627377152 |
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| author | Maiti, Pratyusha Goel, Ashok K. |
| author_facet | Maiti, Pratyusha Goel, Ashok K. |
| contents | Jill Watson, a virtual teaching assistant powered by LLMs, answers student questions and engages them in extended conversations on courseware provided by the instructors. In this paper, we analyze student interactions with Jill across multiple courses and colleges, focusing on the types and complexity of student questions based on Bloom's Revised Taxonomy and tool usage patterns. We find that, by supporting a wide range of cognitive demands, Jill encourages students to engage in sophisticated, higher-order cognitive questions. However, the frequency of usage varies significantly across deployments, and the types of questions asked depend on course-specific contexts. These findings pave the way for future work on AI-driven educational tools tailored to individual learning styles and course structure, potentially enhancing both the teaching and learning experience in classrooms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17429 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How Do Students Interact with an LLM-powered Virtual Teaching Assistant in Different Educational Settings? Maiti, Pratyusha Goel, Ashok K. Computers and Society Artificial Intelligence Jill Watson, a virtual teaching assistant powered by LLMs, answers student questions and engages them in extended conversations on courseware provided by the instructors. In this paper, we analyze student interactions with Jill across multiple courses and colleges, focusing on the types and complexity of student questions based on Bloom's Revised Taxonomy and tool usage patterns. We find that, by supporting a wide range of cognitive demands, Jill encourages students to engage in sophisticated, higher-order cognitive questions. However, the frequency of usage varies significantly across deployments, and the types of questions asked depend on course-specific contexts. These findings pave the way for future work on AI-driven educational tools tailored to individual learning styles and course structure, potentially enhancing both the teaching and learning experience in classrooms. |
| title | How Do Students Interact with an LLM-powered Virtual Teaching Assistant in Different Educational Settings? |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2407.17429 |