Yapay Zekâ ve Bilgi Teknolojileri Eğitimi: Sistematik Literatür İncelemesi

Year 2025, Volume: 12 Issue: 2, 143 - 154, 29.12.2025

Serdar Çiftci

https://doi.org/10.30803/adusobed.1822028

Abstract

Yapay zekâ bilişim teknolojileri eğitiminin paradigmalarında önemli değişimlere neden olmaktadır. Dördüncü Sanayi Devrimi'nin merkezindeki bu teknoloji, artık sadece müfredatın bir parçası değil, aynı zamanda pedagojik bir araç olarak da işlev görmektedir. Mevcut çalışma, yapay zekanın bilişim teknolojileri eğitimi üzerindeki çok yönlü etkilerini inceleyen mevcut literatürü kapsamlı bir şekilde analiz etmeyi amaçlamaktadır. Akıllı öğretim sistemleri, öğrenme analitiğiyle kolaylaştırılan kişiselleştirilmiş öğrenme yöntemleri, otomatik kod değerlendirme ve geri bildirim mekanizmaları ve üretken yapay zeka araçlarının (örneğin GitHub Copilot, ChatGPT) entegrasyonu gibi önemli değişimler derinlemesine incelenmiştir. Bulgular, yapay zekanın öğrenci performansını iyileştirme, bilişsel yükü yönetme ve öğrenci motivasyonunu ve katılımını güçlendirme potansiyelini vurgulamaktadır. Ayrıca öğretim görevlilerinin idari yüklerini azaltarak daha üst düzey pedagojik faaliyetlere odaklanmalarını sağlama potansiyelini de barındırmaktadır. Ancak, entegrasyonla ilgili önemli zorluklar tespit edilmiştir; bunlar arasında akademik dürüstlük, etik sonuçlar, algoritmik önyargı ve yapay zeka araçlarına aşırı bilişsel bağımlılıkla ilgili endişeler yer almaktadır.

Keywords

Yapay Zeka , Bilişim Teknolojileri Eğitimi , Bilgisayar Bilimleri Eğitimi , Akıllı Öğretim Sistemleri , Kişiselleştirilmiş Öğrenme , Üretken Yapay Zeka , Öğrenme Analitiği , Otomatik Değerlendirme

Artificial Intelligence and Information Technology Education: A Systematic Literature Review

Abstract

Artificial intelligence (AI) is fundamentally changing the paradigms of Information Technology (IT) education. This technology, at the heart of the Fourth Industrial Revolution, is no longer just a part of the curriculum but also functions as a pedagogical tool. The current study aims to comprehensively analyze the existing literature examining the multifaceted impacts of AI on IT education. Key shifts, such as intelligent tutoring systems, personalized learning paths facilitated by learning analytics, automated code evaluation and feedback mechanisms, and the integration of generative AI tools (e.g., GitHub Copilot, ChatGPT), are examined in depth. The findings highlight the potential of AI to improve student performance, manage cognitive load, and strengthen student motivation and engagement. It also demonstrates its potential to reduce instructors' administrative burdens, allowing them to focus on higher-level pedagogical activities. However, significant challenges associated with this integration have been identified, including concerns about academic integrity, ethical implications, algorithmic bias, and excessive cognitive reliance on AI tools.

Keywords

Artificial Intelligence , Information Technology Education , Computer Science Education , Intelligent Tutoring Systems , Personalized Learning , Generative Artificial Intelligence , Learning Analytics , Automatic Assessment

References

• Abbas, S., Seol, H., Abbas, Z., & Lee, S. W. (2025). Exploring the Role of Artificial Intelligence in Smart Healthcare: A Capability and Function-Oriented Review. Healthcare, 13(14), 1642. https://doi.org/10.3390/healthcare13141642
• Adiyono, A., Jasiah, J., Ritonga, M., & Al-Matari, A. S. (2024). ChatGPT and Active Learning: A New Paradigm for Student Participation in the Classroom. İçinde Empowering Digital Education with ChatGPT. Chapman and Hall/CRC.
• Aldoseri, A., Al-Khalifa, K. N., Hamouda, A. M., Aldoseri, A., Al-Khalifa, K. N., &
• Hamouda, A. M. (2024). AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact. Sustainability, 16(5). https://doi.org/10.3390/su16051790
• Baidoo-anu, D., & Owusu Ansah, L. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. Journal of AI, 7(1), 52-62. https://doi.org/10.61969/jai.1337500
• Bernacki, M. L., Greene, M. J., & Lobczowski, N. G. (2021). A Systematic Review of Research on Personalized Learning: Personalized by Whom, to What, How, and for What Purpose(s)? Educational Psychology Review, 33(4), 1675-1715. https://doi.org/10.1007/s10648-021-09615-8
• Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa
• Brown, T. B., Mann, B. F., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J. C. S., Winter, C., … Amodei, D. (2020). Language Models Are Few-Shot Learners. https://doi.org/10.48550/arxiv.2005.14165
• Brusilovsky, P. (2003). Adaptive navigation support in educational hypermedia: The role of student knowledge level and the case for meta‐adaptation. British Journal of Educational Technology, 34(4), 487-497. https://doi.org/10.1111/1467-8535.00345
• Dochia, I. (2025). The Changing Role of Teachers in The Digital Age: From Knowledge Transmitter to Learning Facilitator. International Journal of Social and Educational Innovation (IJSEIro), 12(23), 109-118.
• Falloon, G. (2020). From digital literacy to digital competence: The teacher digital competency (TDC) framework. Educational Technology Research and Development, 68(5), 2449-2472. https://doi.org/10.1007/s11423-020-09767-4
• Francisco, R., & Silva, F. (2022). Intelligent Tutoring System for Computer Science Education and the Use of Artificial Intelligence: A Literature Review. International Conference on Computer Supported Education. https://api.semanticscholar.org/CorpusID:248509721
• Guzdial, M., Kay, A., Norris, C., & Soloway, E. (2019). Computational thinking should just be good thinking. Communications of the ACM, 62(11), 28-30. https://doi.org/10.1145/3363181
• Jian, M. J. K. O. (2023). Personalized learning through AI. Advances in Engineering Innovation. https://api.semanticscholar.org/CorpusID:266558453
• Joshi, S. (2025). Introduction to Diffusion Models, Autoencoders and Transformers: Review of Current Advancements. https://doi.org/10.20944/preprints202503.1431.v1
• Lee, H.-Y., Chen, P.-H., Wang, W.-S., Huang, Y.-M., & Wu, T.-T. (2024). Empowering ChatGPT with guidance mechanism in blended learning: Effect of self-regulated learning, higher-order thinking skills, and knowledge construction. International Journal of Educational Technology in Higher Education, 21(1), 16. https://doi.org/10.1186/s41239-024-00447-4
• Messer, M., Brown, N. C. C., Kölling, M., & Shi, M. (2024). Automated Grading and Feedback Tools for Programming Education: A Systematic Review. ACM Trans. Comput. Educ., 24(1). https://doi.org/10.1145/3636515
• Mundlamuri, R., Gunnam, G. R., Mysari, N. K., & Pujuri, J. (2025). The Evolution of AI: From Classical Machine Learning to Modern Large Language Models. IEEE Access, 13, 178302-178341. https://doi.org/10.1109/ACCESS.2025.3621344
• Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71
• Parekh, A., Vitsakis, N., Suglia, A., & Konstas, I. (2024). Investigating the Role of Instruction Variety and Task Difficulty in Robotic Manipulation Tasks. İçinde Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Ed.), Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (ss. 19389-19424). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.1080
• Russell, S., Norvig, P., Popineau, F., Miclet, L., & Cadet, C. (2021). Intelligence artificielle: Une approche moderne (4e édition). Pearson France.
• Shihab, M. I. H., Hundhausen, C., Tariq, A., Haque, S., Qiao, Y., & Mulanda, B. W. (2025). The Effects of GitHub Copilot on Computing Students’ Programming Effectiveness, Efficiency, and Processes in Brownfield Coding Tasks. Proceedings of the 2025 ACM Conference on International Computing Education Research V.1, 407-420. https://doi.org/10.1145/3702652.3744219
• Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252-254. https://doi.org/10.1145/2330601.2330661
• Tan, T. F., Thirunavukarasu, A. J., Campbell, J. P., Keane, P. A., Pasquale, L. R., Abramoff, M. D., Kalpathy-Cramer, J., Lum, F., Kim, J. E., Baxter, S. L., & Ting, D. S. W. (2023). Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges. Ophthalmology Science, 3(4), 100394. https://doi.org/10.1016/j.xops.2023.100394
• Tseng, J. C. R., Chu, H.-C., Hwang, G.-J., & Tsai, C.-C. (2008). Development of an adaptive learning system with two sources of personalization information. Computers & Education, 51(2), 776-786. https://doi.org/10.1016/j.compedu.2007.08.002
• Wenger, E. (1987). Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge. Morgan Kaufmann Pub.
• Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7