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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.21490 |
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| _version_ | 1866908796468592640 |
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| author | Albers, Fabian Strauß, Sebastian Rummel, Nikol Köbis, Nils |
| author_facet | Albers, Fabian Strauß, Sebastian Rummel, Nikol Köbis, Nils |
| contents | Mutual trust between teachers and students is a prerequisite for effective teaching, learning, and assessment in higher education. Accurate predictions about the other group's use of generative artificial intelligence (AI) are fundamental for such trust. However, the disruptive rise of AI has transformed academic work practices, raising important questions about how teachers and students use these tools and how well they can estimate each other's usage. While the frequency of use is well studied, little is known about how AI is used, and comparisons with similar practices are rare. This study surveyed German university teachers (N = 113) and students (N = 123) on the frequency of AI use and the degree of delegation across six identical academic tasks. Participants also provided incentivized cross-sample predictions of the other group's AI use to assess the accuracy of their predictions. We find that students reported higher use of AI and greater delegation than teachers. Both groups significantly overestimated the other group's use, with teachers predicting very frequent use and high delegation by students, and students assuming teachers use AI similarly to themselves. These findings reveal a perception gap between teachers' and students' expectations and actual AI use. Such gaps may hinder trust and effective collaboration, underscoring the need for open dialogue about AI practices in academia and for policies that support the equitable and transparent integration of AI tools in higher education. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21490 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Are they just delegating? Cross-Sample Predictions on University Students' & Teachers' Use of AI Albers, Fabian Strauß, Sebastian Rummel, Nikol Köbis, Nils Human-Computer Interaction Mutual trust between teachers and students is a prerequisite for effective teaching, learning, and assessment in higher education. Accurate predictions about the other group's use of generative artificial intelligence (AI) are fundamental for such trust. However, the disruptive rise of AI has transformed academic work practices, raising important questions about how teachers and students use these tools and how well they can estimate each other's usage. While the frequency of use is well studied, little is known about how AI is used, and comparisons with similar practices are rare. This study surveyed German university teachers (N = 113) and students (N = 123) on the frequency of AI use and the degree of delegation across six identical academic tasks. Participants also provided incentivized cross-sample predictions of the other group's AI use to assess the accuracy of their predictions. We find that students reported higher use of AI and greater delegation than teachers. Both groups significantly overestimated the other group's use, with teachers predicting very frequent use and high delegation by students, and students assuming teachers use AI similarly to themselves. These findings reveal a perception gap between teachers' and students' expectations and actual AI use. Such gaps may hinder trust and effective collaboration, underscoring the need for open dialogue about AI practices in academia and for policies that support the equitable and transparent integration of AI tools in higher education. |
| title | Are they just delegating? Cross-Sample Predictions on University Students' & Teachers' Use of AI |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2601.21490 |