Belief Offloading in Human-AI Interaction

2026-02-09

AI Summary

This study addresses the cognitive and behavioral consequences of human overreliance on large language models (LLMs) in belief formation, introducing the novel concept of “belief offloading”—the delegation of belief construction and maintenance to AI systems. Drawing on an interdisciplinary theoretical framework integrating philosophy, psychology, and human-computer interaction, the work systematically delineates the conditions, typologies, and boundaries under which belief offloading occurs, and proposes a descriptive taxonomy for this phenomenon. By extending the theory of cognitive offloading into the context of artificial intelligence, the research not only clarifies the conceptual scope of belief offloading but also highlights its potential risks. The findings lay a theoretical foundation for assessing the societal and cognitive impacts of this emerging behavior and suggest directions for future inquiry.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Knowledge Representation and Reasoning: Reasoning with Beliefs
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• belief offloading
• cognitive offloading
• human-AI interaction
• large language models
• belief formation

Innovation

• belief offloading
• cognitive offloading
• human-AI interaction
• LLM
• belief formation