How AI Literacy and Social Influence Drive University Students’ Adoption of Artificial Intelligence in China

Bridging AI Literacy and UTAUT Constructs: Understanding AI Adoption Among Chinese University Students

Artificial intelligence (AI) is increasingly becoming part of educational environments, offering tools like intelligent tutoring systems and adaptive learning platforms. Yet, how students adopt and use these technologies varies widely. This article examines how AI literacy interacts with key factors from the Unified Theory of Acceptance and Use of Technology (UTAUT) to influence Chinese university students' intentions to adopt AI tools.

Why AI Literacy Matters in Education

AI literacy goes beyond basic tech skills. It includes understanding AI’s algorithms, ethical concerns, biases, and broader societal effects. For students, building AI literacy is essential to use AI tools effectively and responsibly. As AI technologies become more embedded in education, fostering this literacy helps students recognize both the benefits and limitations of such tools.

The UTAUT Model and AI Adoption

The UTAUT framework identifies four main factors influencing technology adoption:

• Performance Expectancy: Belief that using technology will improve outcomes.
• Effort Expectancy: Perceived ease of using the technology.
• Social Influence: Impact of peers, instructors, and social norms on usage.
• Facilitating Conditions: Availability of resources and support to use the technology.

These factors shape behavioral intention, which predicts actual use.

Key Findings From the Study

Data from 359 Chinese university students revealed several insights:

• AI Literacy strongly predicts behavioral intention. Students with higher AI literacy are more likely to intend to use AI tools.
• Social Influence is the strongest predictor. Peer and educator opinions significantly affect students' willingness to adopt AI.
• Performance Expectancy drives adoption. Students adopt AI technologies when they see clear benefits.
• Effort Expectancy plays a moderate role. Ease of use matters, but less than social and performance expectations.
• Facilitating Conditions showed little effect. Access to resources and support didn't significantly influence adoption intentions in this context.

What This Means for Educators and Institutions

Building AI literacy should be a priority. This means going beyond technical skills to include ethical understanding and critical evaluation of AI. Programs should emphasize how AI tools can improve learning outcomes and encourage positive social norms around their use.

Since social influence strongly impacts adoption, educators can leverage peer networks and role models to promote AI use. Highlighting successful AI integration examples and facilitating collaborative learning around AI can increase acceptance.

Given the limited role of facilitating conditions, institutions might focus more on cultivating motivation and competence rather than only investing in infrastructure or technical support.

Research Questions Addressed

• How does AI literacy directly impact UTAUT constructs like Performance Expectancy and Social Influence?
• How do these UTAUT constructs influence students' intentions to adopt AI?
• Do these constructs mediate the relationship between AI literacy and adoption intentions?

Methodology at a Glance

The study surveyed undergraduate students at a large Chinese university, collecting 359 valid responses. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the relationships between AI literacy, UTAUT factors, and behavioral intention were explored.

Practical Steps for Enhancing AI Adoption

• Develop comprehensive AI literacy programs combining knowledge, skills, critical thinking, and ethics.
• Encourage social environments where AI usage is normalized and positively reinforced.
• Demonstrate clear benefits of AI tools to students to boost their perceived usefulness.
• Provide user-friendly AI tools to reduce effort barriers, even if this is a secondary factor.

For educators interested in deepening AI knowledge and practical skills, Complete AI Training offers a variety of courses that support building AI literacy in education settings.

Conclusion

Adoption of AI technologies by university students depends largely on their AI literacy and the social and performance expectations surrounding the technology. Facilitating conditions, while important, play a lesser role in this context. Educational institutions should focus on boosting AI literacy and shaping positive social influences to encourage meaningful AI integration.