mlsanityData to 7 Oct 2026

Belief Offloading in Human-AI Interaction

arXiv 2602.08754 ↗First posted 9 Feb 2026cs.AI cs.CY cs.HCcited by 3

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Abstract (arXiv)

What happens when people's beliefs are derived from information provided by an LLM? People's use of LLM chatbots as thought partners can contribute to cognitive offloading, which can have adverse effects on cognitive skills in cases of over-reliance. This paper defines and investigates a particular kind of cognitive offloading in human-AI interaction, "belief offloading," in which people's processes of forming and upholding beliefs are offloaded onto an AI system with downstream consequences on their behavior and the nature of their system of beliefs. Drawing on philosophy, psychology, and computer science research, we clarify the boundary conditions under which belief offloading occurs and provide a descriptive taxonomy of belief offloading and its normative implications. We close with directions for future work to assess the potential for and consequences of belief offloading in human-AI interaction.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 40 references that are in the map. First posted on arXiv 9 Feb 2026.

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7 of 40 references.

References · 7

  1. 2024On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trialcited by 105
  2. 2021Algorithmic Monoculture and Social Welfarecited by 98
  3. 2024Building Machines that Learn and Think with Peoplecited by 73
  4. 2024Objection Overruled! Lay People can Distinguish Large Language Models from Lawyers, but still Favour Advice from an LLMcited by 16
  5. 2025Epistemological Fault Lines Between Human and Artificial Intelligencecited by 11
  6. 2025A Longitudinal Randomized Control Study of Companion Chatbot Use: Anthropomorphism and Its Mediating Role on Social Impactscited by 10
  7. 2025How AI Responses Shape User Beliefs: The Effects of Information Detail and Confidence on Belief Strength and Stancecited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.