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The AI Memory Gap : Users Misremember What They Created With AI or Without

Title data

Zindulka, Tim ; Goller, Sven ; Fernandes, Daniela ; Welsch, Robin ; Buschek, Daniel:
The AI Memory Gap : Users Misremember What They Created With AI or Without.
In: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. - New York, NY : Association for Computing Machinery , 2026 . - 61
ISBN 9798400722783
DOI: https://doi.org/10.1145/3772318.3791494

Official URL: Volltext

Project information

Project title:
Project's official title
Project's id
Computergestützte Schreibwerkzeuge
525037874

Project financing: Deutsche Forschungsgemeinschaft

Abstract in another language

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.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: Ideation; Writing; AI; LLM; Source Memory
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IX > Chair Applied Computer Science - Univ.-Prof. Dr. Daniel Buschek
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 28 Apr 2026 07:30
Last Modified: 28 Apr 2026 07:30
URI: https://eref.uni-bayreuth.de/id/eprint/96940