Struggle First, Prompt Later: How Task Complexity Shapes Learning with GenAI-Assisted Pretesting
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908318199447552 |
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| author | Akgun, Mahir Toker, Sacip |
| author_facet | Akgun, Mahir Toker, Sacip |
| contents | This study examines the role of AI-assisted pretesting in enhancing learning outcomes, particularly when integrated with generative AI tools like ChatGPT. Pretesting, a learning strategy in which students attempt to answer questions or solve problems before receiving instruction, has been shown to improve retention by activating prior knowledge. The adaptability and interactivity of AI-assisted pretesting introduce new opportunities for optimizing learning in digital environments. Across three experimental studies, we explored how pretesting strategies, task characteristics, and student motivation influence learning. Findings suggest that AI-assisted pretesting enhances learning outcomes, particularly for tasks requiring higher-order thinking. While adaptive AI-driven pretesting increased engagement, its benefits were most pronounced in complex, exploratory tasks rather than straightforward computational problems. These results highlight the importance of aligning pretesting strategies with task demands, demonstrating that AI can optimize learning when applied to tasks requiring deeper cognitive engagement. This research provides insights into how AI-assisted pretesting can be effectively integrated with generative AI tools to enhance both cognitive and motivational outcomes in learning environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10249 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Struggle First, Prompt Later: How Task Complexity Shapes Learning with GenAI-Assisted Pretesting Akgun, Mahir Toker, Sacip Human-Computer Interaction This study examines the role of AI-assisted pretesting in enhancing learning outcomes, particularly when integrated with generative AI tools like ChatGPT. Pretesting, a learning strategy in which students attempt to answer questions or solve problems before receiving instruction, has been shown to improve retention by activating prior knowledge. The adaptability and interactivity of AI-assisted pretesting introduce new opportunities for optimizing learning in digital environments. Across three experimental studies, we explored how pretesting strategies, task characteristics, and student motivation influence learning. Findings suggest that AI-assisted pretesting enhances learning outcomes, particularly for tasks requiring higher-order thinking. While adaptive AI-driven pretesting increased engagement, its benefits were most pronounced in complex, exploratory tasks rather than straightforward computational problems. These results highlight the importance of aligning pretesting strategies with task demands, demonstrating that AI can optimize learning when applied to tasks requiring deeper cognitive engagement. This research provides insights into how AI-assisted pretesting can be effectively integrated with generative AI tools to enhance both cognitive and motivational outcomes in learning environments. |
| title | Struggle First, Prompt Later: How Task Complexity Shapes Learning with GenAI-Assisted Pretesting |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.10249 |