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.