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Creating R5E Pattern of Using Artificial Intelligence for Developing Programming Skills |
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PP: 321-337 |
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doi:10.18576/amis/200202
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Author(s) |
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Abdullah A. Alanz,
Ahmed I. Taloba,
Hebah F. Al-Ruwaili,
Afrah S. Albalawi,
Loay F. Hussein,
Ayadi Rami,
Khaled Bedair,
Mohamed A. Elmasry,
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Abstract |
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| The integration of Artificial Intelligence (AI) in education has transformed teaching and learning practices, particularly in programming courses that demand both cognitive understanding and practical application. However, many existing approaches rely on unstructured AI usage, which often results in superficial learning, limited engagement, and inconsistent skill development among students. To address these drawbacks, this study introduces a structured R5E pattern—Recognition, Exploration, Engagement, Execution, Evaluation, and Editing—for guiding AI-assisted learning in programming education. Unlike traditional or unregulated AI usage, the R5E framework emphasizes systematic interaction with AI tools, fostering meaningful engagement, deeper comprehension, and genuine skill acquisition. The research adopts a mixed-methods approach involving two experimental groups of students from Al-Jouf University in Saudi Arabia: one applying the R5E pattern and the other relying on unstructured AI usage. Data collection tools include cognitive programming skills tests, performance observation cards, and engagement surveys to comprehensively assess learning outcomes. Preliminary findings reveal that students exposed to the R5E framework demonstrate superior cognitive understanding, higher programming proficiency, and stronger engagement compared to peers in the unstructured AI group. Furthermore, these students report greater satisfaction with their learning experience, indicating the potential of structured AI-driven methods to enrich educational outcomes. Nonetheless, the study highlights challenges such as technical limitations and resistance to adopting new pedagogical approaches, underscoring the need for training and support for educators. By providing empirical evidence on the effectiveness of structured AI integration, this research offers valuable insights for educators and policymakers seeking to harness AI to optimize programming education.
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