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

Fuente: arXiv
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Main Author: Osler, Lucy
Format: Preprint
Published: 2025
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author Osler, Lucy
author_facet Osler, Lucy
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2512_03682
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Knowing oneself with and through AI: From self-tracking to chatbots
Osler, Lucy
Computers and Society
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.
title Knowing oneself with and through AI: From self-tracking to chatbots
topic Computers and Society
url https://arxiv.org/abs/2512.03682