Education Technology
Muslim students' acceptance of artificial intelligence in Islamic religious education: an extended TAM approach
This study investigates Muslim students' perceptions and acceptance of Artificial Intelligence (AI) in Islamic religious education, utilizing an extended Technology Acceptance Model (TAM). Based on a survey of 224 students from 12 Indonesian universities, findings indicate that Perceived Ease of Use significantly impacts Perceived Usefulness. Both Perceived Usefulness and Attitude Toward Using AI directly influence Behavioural Intention to Use AI, with attitude being a key determinant. Despite limited current AI use in religious learning, students exhibit positive attitudes, highlighting that ease and usefulness are primary drivers for AI adoption. The research suggests developing AI systems aligned with Islamic values and ethics to enhance acceptance and address ethical concerns.
Executive Impact: AI in Islamic Education
This research provides critical insights for educational institutions and AI developers, revealing the drivers and barriers to AI adoption in Islamic religious education.
0.668
PEOU to PU Path Coefficient
0.536
PU to AT Path Coefficient
0.407
AT to BI Path Coefficient
0.272
PEOU to AT Path Coefficient
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Muslim Student AI Acceptance
Muslim students generally exhibit a neutral to moderately positive attitude towards AI in Islamic learning. While not strongly rejecting AI, their interest and positive views are evident, though not yet robust. This suggests a foundational openness that can be cultivated. Factors like weak trust in AI information, biased responses, and accuracy issues, particularly concerning religious beliefs, contribute to this nuanced acceptance. Developers should focus on building AI systems that are transparent and ethically aligned with Islamic values to bolster trust and improve overall acceptance.
Perceived Ease of Use (PEOU)
The study found that Perceived Ease of Use (PEOU) significantly impacts Perceived Usefulness (PU) (Path Coefficient = 0.668, t = 18.662) and also influences Attitude Toward AI (AT) (Path Coefficient = 0.272, t=4.708). This indicates that the simpler and more intuitive AI technology is to use, the more useful students find it for Islamic learning, and the more positive their attitude towards it becomes. This underscores the importance of user-friendly interfaces and clear, reliable functionality in AI tool design for this educational context. Prioritizing ease of use can directly lead to higher perceived usefulness and more positive attitudes among students.
Perceived Usefulness (PU)
Perceived Usefulness (PU) emerged as a critical factor, significantly influencing both Attitude Toward AI (AT) (Path Coefficient = 0.536, t = 9.475) and Behavioural Intention to Use AI (BI) (Path Coefficient = 0.315, t = 3.918). Students who perceive AI as tangibly beneficial for Islamic learning are more likely to develop positive attitudes and intentions to integrate it into their studies. This highlights the necessity for AI tools to demonstrate clear, practical advantages such as aiding comprehension, research, and task fulfillment. Focusing on the utility and value proposition of AI can effectively drive its adoption and positive reception in religious education.
0.668
Strongest Path: Perceived Ease of Use to Perceived Usefulness (t=18.662)
Enterprise Process Flow
Perceived Ease of Use (PEOU)
→
Perceived Usefulness (PU)
→
Attitude Toward AI (AT)
→
Behavioral Intention (BI)
| Factor | Impact on Acceptance | Key Considerations |
|---|---|---|
| Perceived Ease of Use (PEOU) |
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| Perceived Usefulness (PU) |
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| Attitude Toward AI (AT) |
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Calculate Your Potential AI ROI
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Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A typical phased approach to integrating AI solutions, tailored for educational contexts focusing on religious studies.
Phase 1: Needs Assessment & Ethical Framing
Identify specific learning challenges in Islamic education where AI can add value. Establish a strong ethical framework, ensuring AI tools align with Islamic values and address concerns regarding bias and accuracy in religious content. Pilot small-scale AI tools with a focused group of students and educators.
Phase 2: Pilot Deployment & User Experience Refinement
Deploy AI tools for specific use cases (e.g., language learning, textual analysis support) to a larger pilot group. Collect detailed feedback on Perceived Ease of Use and Perceived Usefulness. Iterate on UI/UX to enhance usability and integrate features that resonate with student needs for religious learning, such as context-aware content generation or ethical reasoning aids.
Phase 3: Integration & Value Demonstration
Integrate refined AI tools into broader curricula. Develop training programs for both students and educators, emphasizing how AI enhances learning outcomes while maintaining critical thinking skills. Continuously monitor the impact on student attitudes and behavioural intentions, showcasing tangible benefits in academic performance and engagement with religious texts.
Phase 4: Scaling & Continuous Improvement
Expand AI adoption across various departments and educational levels, guided by positive feedback and demonstrated ROI. Establish mechanisms for ongoing ethical review and technical updates. Foster a community of practice to share best practices and drive innovation in AI-enhanced Islamic religious education, ensuring long-term sustainability and positive impact.
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