mlsanityData to 14 Oct 2026

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

arXiv 2509.11851 ↗First posted 15 Sep 2025cs.HC cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 28 of its 76 references that are in the map. First posted on arXiv 15 Sep 2025.

Builds on

28 of 76 references.

References · 28

  1. 2019GLTR: Statistical Detection and Visualization of Generated Textcited by 496
  2. 2021All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Textcited by 287
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  5. 2025Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Taskcited by 106
  6. 2022Human heuristics for AI-generated language are flawedcited by 105
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16 more
  1. 2025People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated textcited by 40
  2. 2024The Effects of Generative AI on Design Fixation and Divergent Thinkingcited by 37
  3. 2023Writer-Defined AI Personas for On-Demand Feedback Generationcited by 36
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  6. 2023The HaLLMark Effect: Supporting Provenance and Transparent Use of Large Language Models in Writing with Interactive Visualizationcited by 24
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Co-Writing with AI: An Empirical Study of Diverse Academic Writing Workflowscited by 0
  2. 2026Beyond "ChatGPT Can Make Mistakes": Designing Interventions to Support Metacognitive Monitoring in AI-Assisted Workcited by 0