Design strategies for artificial intelligence based future learning centers in medical universities

Area of Science:

• Medical Education
• Artificial Intelligence in Healthcare
• Technology Acceptance

Background:

• Investigating the adoption of artificial intelligence (AI) tools among medical students.
• Identifying key factors influencing AI tool acceptance in academic settings.

Purpose of the Study:

• To provide a theoretical foundation and practical recommendations for developing AI-integrated learning environments in medical universities.
• To understand the drivers behind medical students' continued use of AI technologies.

Main Methods:

• Utilized structural equation modeling (SEM) for data analysis.
• Integrated the Unified Theory of Acceptance and Use of Technology (UTAUT), Expectancy Confirmation Theory (ECT), and Innovation Diffusion Theory (IDT).

Main Results:

• Effort expectancy, facilitating conditions, social influence, and satisfaction significantly predict continuance intention to use AI tools.
• Relative advantage impacts satisfaction with AI tools.
• Personal innovativeness moderates the relationship between facilitating conditions, satisfaction, and continuance intention.

Conclusions:

• AI-centered learning centers require personalized learning paths and robust technical support.
• Fostering a collaborative, innovative environment and highlighting tool advantages are crucial for AI adoption.
• Effective implementation necessitates addressing student perceptions and support systems.