Concern 614 · Working draft · editorial review in progress
Reality-testing erosion
AI-related reality confusion occurs when convincing generated material or misleading conversations make it harder to judge what actually happened.
Understanding the concern
A realistic image can be mistaken for a record, and a repeated suggestion can affect how an event is remembered. The concern includes ordinary errors in belief and memory; it should not automatically be described as a psychiatric condition. The form and context of the interaction matter.
A controlled study found that misleading chatbot conversations affected recall in a specific task. That does not show that users generally lose contact with reality. Preserving original records, checking sources and making generated reconstructions visible can help. When an interaction is causing distress or serious confusion, support from trusted people matters more than continuing to seek reassurance from the same system.
An example
A person becomes unsure whether a detail came from an original event or a later chatbot explanation.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
Evidence and its limits
The experiment concerns false recall under defined conditions, not a general claim that AI causes psychosis or lasting loss of reality testing.
Pataranutaporn and colleagues · IUI 2025 · Controlled experiment
Slip Through the Chat: Subtle Injection of False Information in LLM Chatbot Conversations Increases False Memory Formation ↗ (opens in a new tab)Misleading conversations affected recall in a defined task; does not establish general loss of contact with reality.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
What records or checks could help separate an original experience from a generated reconstruction?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Dignity
Dignity means recognizing each person's worth regardless of usefulness, status, or a system's score. People deserve respect and a voice in matters that affect them.
Explain important decisions, avoid humiliating treatment, and keep room for a person's story. Do not treat a prediction as a complete account of who someone is.
Ask together: Does this use treat each person as someone whose experience matters?
Listening
Listening means trying to understand someone's experience before preparing an answer. People who use a service, work with it, or are judged by it may notice problems its designers miss.
Invite people to describe what happened in their own words. Repeat back what you understood and let them correct it. A quiet person or a complaint that is hard to measure may still reveal something important.
Ask together: Whose experience have we not heard yet?
Seeing potential
Seeing potential means recognizing people's capacity to learn, contribute, and grow. It resists reducing someone to a score or to the tasks a machine can perform faster.
Use tools to support people's development. Give learners time, feedback, and real opportunities to practice. A prediction about someone's performance should not close off their future.
Ask together: How could this help people develop abilities they value?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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