What makes university students accept generative artificial intelligence? A moderated mediation model
Enterprise AI Analysis
This study delves into the factors influencing university students' acceptance of Generative AI, utilizing a moderated mediation model. It explores the relationships between AI attitude, AI literacy, AI self-efficacy, and AI learning anxiety. The findings provide critical insights into how to foster a positive AI adoption environment in higher education.
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The study is primarily grounded in the Technology Acceptance Model (TAM), extended to include AI literacy, self-efficacy, and anxiety. TAM emphasizes perceived usefulness (PU) and perceived ease of use (PEOU) as critical determinants of technology acceptance. In the context of AI, these perceptions are influenced by individual characteristics and prior experiences with AI tools.
AI Attitude (general predisposition towards AI), AI Literacy (understanding and ethical use of AI), AI Self-Efficacy (confidence in using AI), AI Learning Anxiety (fear/worry about learning AI), and Generative AI Acceptance (willingness to adopt and use). These variables interact to form a complex model of AI adoption.
A quantitative approach was used, collecting data from 356 Turkish university students via Google Forms. The study employed mediation and moderation analyses using Hayes' PROCESS macro to examine the proposed relationships. Validity of scales was confirmed using confirmatory factor analyses.
Moderated Mediation Model Flow
| AI Anxiety Level | Conditional Effect (β) | Implications for Acceptance |
|---|---|---|
| Low (-1 SD) | 0.96 |
|
| Moderate (Mean) | 0.78 |
|
| High (+1 SD) | 0.61 |
|
University-Wide AI Integration Initiative
A large public university in Türkiye, facing increasing student demand for AI-driven tools, implemented a comprehensive 'AI Fluency Program'. This program included mandatory AI literacy courses, workshops on AI tool usage, and peer-mentoring for students struggling with AI anxiety. Initial feedback indicated a significant increase in student self-efficacy and a reduction in reported AI learning anxiety.
Post-implementation surveys revealed a 25% increase in generative AI acceptance among students within the first year. Students reported feeling more confident in using AI for academic tasks and perceived AI as a beneficial tool rather than a threat. This demonstrates the effectiveness of a multi-faceted approach addressing both knowledge and emotional barriers to AI adoption.
Key Lesson: Proactive educational strategies and emotional support are crucial for successful AI integration in higher education.
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