As conversational agents powered by large language models become more human-like, users are starting to view them as companions rather than mere assistants. Our study explores how changes to a person’s mental model of an AI system affects their interaction with the system. Participants interacted with the same conversational AI, but were influenced by different priming statements regarding the AI’s inner motives: caring, manipulative or no motives. Here we show that those who perceived a caring motive for the AI also perceived it as more trustworthy, empathetic and better-performing, and that the effects of priming and initial mental models were stronger for a more sophisticated AI model. Our work also indicates a feedback loop in which the user and AI reinforce the user’s mental model over a short time; further work should investigate long-term effects. The research highlights the importance of how AI systems are introduced can notably affect the interaction and how the AI is experienced.
Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
Latest posts by Ryan Watkins (see all)
- Limitations of the LLM-as-a-Judge Approach for Evaluating LLM Outputs in Expert Knowledge Tasks - June 7, 2025
- Neural and Cognitive Impacts of AI: The Influence of Task Subjectivity on Human-LLM Collaboration - June 5, 2025
- From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agents - May 30, 2025