The AI Memory Gap: Users Misremember What They Created With AI or Without
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arXiv
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| Format: | Preprint |
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2025
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| author | Zindulka, Tim Goller, Sven Fernandes, Daniela Welsch, Robin Buschek, Daniel |
| author_facet | Zindulka, Tim Goller, Sven Fernandes, Daniela Welsch, Robin Buschek, Daniel |
| contents | As large language models (LLMs) become embedded in interactive text generation, disclosure of AI as a source depends on people remembering which ideas or texts came from themselves and which were created with AI. We investigate how accurately people remember the source of content when using AI. In a pre-registered experiment, 184 participants generated and elaborated on ideas both unaided and with an LLM-based chatbot. One week later, they were asked to identify the source (noAI vs withAI) of these ideas and texts. Our findings reveal a significant gap in memory: After AI use, the odds of correct attribution dropped, with the steepest decline in mixed human-AI workflows, where either the idea or elaboration was created with AI. We validated our results using a computational model of source memory. Discussing broader implications, we highlight the importance of considering source confusion in the design and use of interactive text generation technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11851 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The AI Memory Gap: Users Misremember What They Created With AI or Without Zindulka, Tim Goller, Sven Fernandes, Daniela Welsch, Robin Buschek, Daniel Human-Computer Interaction Computation and Language H.5.2; I.2.7 As large language models (LLMs) become embedded in interactive text generation, disclosure of AI as a source depends on people remembering which ideas or texts came from themselves and which were created with AI. We investigate how accurately people remember the source of content when using AI. In a pre-registered experiment, 184 participants generated and elaborated on ideas both unaided and with an LLM-based chatbot. One week later, they were asked to identify the source (noAI vs withAI) of these ideas and texts. Our findings reveal a significant gap in memory: After AI use, the odds of correct attribution dropped, with the steepest decline in mixed human-AI workflows, where either the idea or elaboration was created with AI. We validated our results using a computational model of source memory. Discussing broader implications, we highlight the importance of considering source confusion in the design and use of interactive text generation technologies. |
| title | The AI Memory Gap: Users Misremember What They Created With AI or Without |
| topic | Human-Computer Interaction Computation and Language H.5.2; I.2.7 |
| url | https://arxiv.org/abs/2509.11851 |