Project description

Our interdisciplinary longitudinal study investigates the evolving dynamics of human-AI interaction over six waves spanning one year. By examining individual, behavioral, and task-related variables, the project aims to uncover how users' trust in, perceptions of, self-efficacy, and willingness to engage with AI systems develop and interrelate over time. The insights gained from this research are essential for better understanding human-machine interaction, a critical foundation for fostering effective collaboration between users and AI systems. This knowledge will inform user-centered AI design and guide the ethical integration of these technologies into various aspects of everyday life. 

We are mainly interested in four key research aspects:

• Understanding Human-AI-Interaction Dynamics: How individual factors (e.g., experience, knowledge, and personality), behavioral factors (e.g., trust, attitudes, perceived roles), and task contexts influence the willingness to delegate tasks and disclose information to AI.
• Role Perception and Self-Efficacy: How varying perceptions of AI (e.g., as tools, partners, or actors) affect users’ cognitive self-esteem and self-efficacy across creative and problem-solving tasks.
• Knowledge and Attitudes: Investigating the interplay between objective knowledge, perceived knowledge, and attitudes toward AI systems longitudinally.
• Exploring Temporal Effects: How for example changes in perceived trustworthiness, credibility, and anthropomorphism relate to reliance on AI, perceived intelligence, and social presence over time.

This interdisciplinary project is collaboratively conducted by ten members across four research groups at the IWM.

Project Partner

• Dr. Mike Prentice