On the Role of Domain Experts in Creating Effective Tutoring Systems
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914071398318080 |
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| author | Sreedharan, Sarath Sikes, Kelsey Blanchard, Nathaniel Mason, Lisa Krishnaswamy, Nikhil Zarestky, Jill |
| author_facet | Sreedharan, Sarath Sikes, Kelsey Blanchard, Nathaniel Mason, Lisa Krishnaswamy, Nikhil Zarestky, Jill |
| contents | The role that highly curated knowledge, provided by domain experts, could play in creating effective tutoring systems is often overlooked within the AI for education community. In this paper, we highlight this topic by discussing two ways such highly curated expert knowledge could help in creating novel educational systems. First, we will look at how one could use explainable AI (XAI) techniques to automatically create lessons. Most existing XAI methods are primarily aimed at debugging AI systems. However, we will discuss how one could use expert specified rules about solving specific problems along with novel XAI techniques to automatically generate lessons that could be provided to learners. Secondly, we will see how an expert specified curriculum for learning a target concept can help develop adaptive tutoring systems, that can not only provide a better learning experience, but could also allow us to use more efficient algorithms to create these systems. Finally, we will highlight the importance of such methods using a case study of creating a tutoring system for pollinator identification, where such knowledge could easily be elicited from experts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01432 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Role of Domain Experts in Creating Effective Tutoring Systems Sreedharan, Sarath Sikes, Kelsey Blanchard, Nathaniel Mason, Lisa Krishnaswamy, Nikhil Zarestky, Jill Artificial Intelligence Computer Vision and Pattern Recognition The role that highly curated knowledge, provided by domain experts, could play in creating effective tutoring systems is often overlooked within the AI for education community. In this paper, we highlight this topic by discussing two ways such highly curated expert knowledge could help in creating novel educational systems. First, we will look at how one could use explainable AI (XAI) techniques to automatically create lessons. Most existing XAI methods are primarily aimed at debugging AI systems. However, we will discuss how one could use expert specified rules about solving specific problems along with novel XAI techniques to automatically generate lessons that could be provided to learners. Secondly, we will see how an expert specified curriculum for learning a target concept can help develop adaptive tutoring systems, that can not only provide a better learning experience, but could also allow us to use more efficient algorithms to create these systems. Finally, we will highlight the importance of such methods using a case study of creating a tutoring system for pollinator identification, where such knowledge could easily be elicited from experts. |
| title | On the Role of Domain Experts in Creating Effective Tutoring Systems |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.01432 |