A Smart ChatGPT Mobile Application for Improving C# Programming Skills for Students in Educational Institutions
This study explores the integration of artificial intelligence (AI) into mobile learning to enhance programming education at the higher education level. Specifically, it presents the design, development, and evaluation of a mobile application that leverages ChatGPT's capabilities to support C# programming instruction. The primary objective was to create a user-friendly AI-powered learning tool that delivers personalized assistance and real-time debugging support. Adopting a user-centered design approach, the application was developed with active input from students and educators. To assess its impact, the study employed a mixed-method evaluation framework involving both quantitative and qualitative data. A comparative analysis between experimental and control groups was conducted to determine the app's effectiveness. Findings revealed a statistically significant improvement in the performance of students using the application (p < 0.001), with medium to large effect sizes and high statistical power (power > 0.95). Regular app usage was positively correlated with technical proficiency and favorable psychometric outcomes, indicating both educational and motivational benefits. The results underscore the potential of AI-integrated mobile platforms to transform programming education by offering personalized, adaptive learning experiences. This research contributes a practical model for incorporating AI into mobile programming instruction and demonstrates its capacity to enhance learning outcomes and user engagement. The proposed solution not only improves students’ programming skills but also sets a foundation for broader applications of AI in education.
Authors
- Wessam ElSaid
- Amira Atta
- Mona Esmat (ORCID: https://orcid.org/0000-0003-0967-4709)
- Nahed Amasha (ORCID: https://orcid.org/0000-0002-9022-8353)
- Eman Elayat
Institutions
- Mansoura University (EG)
Publication Details
- Journal
- Qubahan Academic Journal
- Published
- 2025-04-25
- DOI
- https://doi.org/10.48161/qaj.v5n2a1772
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00