Tailored to you: longitudinal effects of personalising language models

Key Findings

• — 992 participants completed all five daily sessions (4,960 conversations); 1,381 took the intake survey
• — Several changes in human-AI interaction over the five days were driven primarily by repeated exposure rather than by personalisation itself
• — Memory-based personalisation: participants engaged in greater self-disclosure and rated the model as less creepy
• — Survey-based personalisation: participants reported higher regret about having shared personal information with the AI
• — Persona summaries for the survey condition were generated with Gemini 3.1 Pro; model instructions were revised after internal pilots to avoid emotional amplification

Methodology Notes

Between-subjects longitudinal design with three conditions and five consecutive daily advice-seeking sessions on assigned topics; Prolific sample paid $20 per hour; preregistration status not stated on the abstract page. Outcomes are self-report scales plus conversation-derived self-disclosure measures; effects on interpersonal relationships outside the interaction are framed as motivation rather than measured; effect sizes are in the body, not the abstract. All nine authors are affiliated with Google DeepMind, the developer of the models studied. Date is the arXiv v1 submission date (17 September 2026); announced 21 September 2026; CC BY.

Sources