Academic Journal
Balancing Innovation and Integrity in Using Generative AI for a New Computer Programming Assignments Approach
| Τίτλος: | Balancing Innovation and Integrity in Using Generative AI for a New Computer Programming Assignments Approach |
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| Γλώσσα: | English |
| Συγγραφείς: | Shubair Abdullah (ORCID |
| Πηγή: | Journal of Education and e-Learning Research. 2026 13(1):140-149. |
| Διαθεσιμότητα: | Asian Online Journal Publishing Group. 244 Fifth Avenue Suite D42, New York, NY 10001. Fax: 212-591-6094; e-mail: info@asianonlinejournals.com; Web site: http://www.asianonlinejournals.com |
| Peer Reviewed: | Y |
| Page Count: | 10 |
| Ημερομηνία έκδοσης: | 2026 |
| Τύπος εγγράφου: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Programming, Foreign Countries, Learner Engagement, Problem Solving, Skill Development, Experiential Learning, Active Learning, Ethics, Technology Uses in Education, Instructional Effectiveness, Expertise, Undergraduate Students, College Faculty, Computer Science Education, Assignments, Teacher Attitudes, Models, Universities |
| Γεωγραφικοί όροι: | Oman |
| ISSN: | 2518-0169 2410-9991 |
| Περίληψη: | This study presents a new structured approach to AI-assisted learning designed to support students in actively engaging with programming tasks, progressively developing independent problem-solving skills, and effectively utilizing Generative Artificial Intelligence (GenAI) in programming education. The framework, based on constructivist and experiential learning theories, aims to guide students in interacting with GenAI tools to enhance algorithmic, critical, and analytical reasoning rather than replace cognitive effort. The research employs a mixed-methods design, incorporating quantitative data from laboratory performance assignments and paper-based assessments of programming skills, as well as qualitative data from semi-structured expert interviews. Three experts from well-known Omani universities verified and confirmed the model's consistency with established learning theories and instructional design principles. Thirty-two undergraduate students at Sultan Qaboos University were divided into experimental and control groups. Quantitative analysis indicated that the experimental group, which adopted the new approach, significantly outperformed the control group, which used GenAI without restrictions, on assignments assessing code analysis, debugging, optimization, and problem-solving skills (p = 0.009). These findings suggest that the proposed model effectively balances creativity and academic integrity. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Αριθμός Καταχώρησης: | EJ1507239 |
| Βάση Δεδομένων: | ERIC |
| ISSN: | 2518-0169 2410-9991 |
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