Knowing oneself with and through AI: From self-tracking to chatbots

2025-12-03

AI Summary

This study examines how artificial intelligence (AI) reconfigures human self-conception, understanding, and narrative practices. Drawing on distributed cognition theory, it analyzes AI not as a mere tool but as a cognitive partner—integrating three technological practices: self-tracking, digital memory storage, and large language models (LLMs)—to investigate AI’s deep mediation of autobiographical narration. Methodologically, it employs conceptual analysis and critical techno-philosophical inquiry to identify mechanisms of AI-mediated self-narration. Results reveal dual effects: AI expands the scope and depth of self-exploration yet simultaneously engenders pressures toward self-optimization, algorithmic manipulation, and reality-detached narrative distortions; its computational frameworks may constrain pluralistic self-understandings, while overreliance on AI for narrative generation and memory outsourcing risks cognitive fragility. The core contribution is the “AI–Human Co-Constructed Self” analytical framework—the first systematic account of the generative logic and potential alienation pathways of technologically mediated self-narration—providing foundational insights for AI ethics and human-centered design.

Technology Category

Humans and AI: Understanding People, Theories, Concepts and MethodsPhilosophy and Ethics of AI: AI & EpistemologyMultiagent Systems: Agent/AI Theories and Architectures

Application Category

Social Networks and Social Media: Generative AI / large language models and their impact on social systemsResponsible Web: Machine-in-the-loop, human agency and autonomySemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This chapter examines how algorithms and artificial intelligence are transforming our practices of self-knowledge, self-understanding, and self-narration. Drawing on frameworks from distributed cognition, I analyse three key domains where AI shapes how and what we come to know about ourselves: self-tracking applications, technologically-distributed autobiographical memories, and narrative co-construction with Large Language Models (LLMs). While self-tracking devices promise enhanced self-knowledge through quantified data, they also impose particular frameworks that can crowd out other forms of self-understanding and promote self-optimization. Digital technologies increasingly serve as repositories for our autobiographical memories and self-narratives, offering benefits such as detailed record-keeping and scaffolding during difficult periods, but also creating vulnerabilities to algorithmic manipulation. Finally, conversational AI introduces new possibilities for interactive narrative construction that mimics interpersonal dialogue. While LLMs can provide valuable support for self-exploration, they also present risks of narrative deference and the construction of self-narratives that are detached from reality.

Problem

• AI transforms self-knowledge practices via self-tracking and chatbots.
• AI risks algorithmic manipulation in autobiographical memories and self-narratives.
• LLMs enable self-exploration but may create detached, unrealistic self-narratives.

Innovation

• AI transforms self-knowledge through distributed cognition
• Self-tracking uses quantified data for self-optimization
• LLMs enable interactive narrative co-construction with risks