Dijitalden Akıllı Eğitime: Yapay Zeka Entegreli Programlama Eğitimi İçin AIPER Çerçevesi

Öz

ChatGPT ve GitHub Copilot gibi üretken yapay zekâ kodlama asistanları, programlama eğitiminin yürütüldüğü koşulları değiştirmiştir; yükseköğretim kurumları ise günümüzde müfredat, ölçme-değerlendirme ve eğitmen hazırlığı süreçlerini kapsayan tasarım sorunlarıyla karşı karşıyadır. Kurumların bugüne kadarki tepkileri, yasaklama ile pasif bir izin verme eğilimi arasında gidip gelmiştir ki bu iki yaklaşım da pedagojik bir duruş teşkil etmemektedir. Bu çalışma, 37 ampirik ve teorik kaynağın bütünleştirici bir incelemesi (integrative review) yoluyla geliştirilen "Yapay Zekâ Entegreli Programlama Eğitimi Yeniden Tasarımı" (AIPER) Çerçevesini sunmaktadır. Söz konusu çerçeve birbirine bağımlı beş bileşenden oluşmaktadır: müfredatın yeniden kalibre edilmesi, yapay zekâ destekli aktif öğrenme uygulamaları, ölçme-değerlendirmenin yeniden tasarımı, akademik dürüstlük ve etik çerçeveleme ve eğitmen rolünün dönüşümü. Bu bileşenlerin birbirine bağımlı olduğu iddiası, yanlışlanabilir altı önerme ve kısmi benimseme durumunda ortaya çıkacağı öngörülen başarısızlık senaryolarını (failure modes) içeren bir tablo aracılığıyla test edilebilir kılınmıştır. AIPER, yapay zekâ araçlarını bilişsel çabanın bir alternatifi olarak değil, kişiselleştirilmiş öğrenme için yapı iskeleleri (scaffolds) olarak ele almakta ve bu araçların benimsenmesini reaktif bir politika geliştirmek yerine, bilinçli bir pedagojik yeniden tasarım fırsatı olarak çerçevelemektedir. Sonuç bölümünde müfredat tasarımcıları, ölçme-değerlendirme uygulayıcıları ve kurumsal yöneticiler için doğuracağı çıkarımlar tartışılmaktadır.

Anahtar Kelimeler

• Üretici yapay zeka
• kişiselleştirilmiş öğrenme
• yapay zeka destekli aktif öğrenme
• dönüştürücü pedagoji

Etik Beyan

Bu çalışma insan katılımcı, anket, mülakat veya kişisel veri toplama içermemektedir. Çalışma yalnızca bütünleştirici literatür incelemesi ve kavramsal çerçeve geliştirmeden oluştuğundan etik kurul onayı gerektirmemektedir.

From Digital to Intelligent Education: The AIPER Framework for AI-Integrated Programming Education

Öz

Generative AI coding assistants like ChatGPT and GitHub Copilot have changed the conditions under which students learn to program. Most institutions have responded by banning the tools or quietly tolerating them, and neither does much for learning. This paper presents AIPER (Artificial Intelligence-Integrated Programming Education Redesign), a conceptual framework synthesised from 37 studies. It organises the institutional response around five interdependent components: curriculum recalibration, AI-augmented learning practices, assessment redesign, academic integrity and ethical framing, and instructor role transformation. The components are not a menu to pick from; redesigning one while leaving the others alone tends to undo the gains. Running through all of them is a single premise, that AI should personalise and support learning rather than stand in for the thinking a course is meant to develop, which calls for genuine pedagogical redesign rather than a policy patch. We set out six testable propositions about how the components depend on one another, together with a table of the failure modes that should appear under partial adoption, and we draw out the implications for curriculum designers, assessment practitioners, and administrators.

Anahtar Kelimeler

• Generative AI
• personalised learning
• AI-augmented active learning
• transformative pedagogy

Etik Beyan

This study does not involve human participants, surveys, interviews, or the collection of personal data. As the study consists solely of an integrative literature review and the development of a conceptual framework, ethical approval is not required.

Kaynakça

1. [1] Prather, J., Reeves, B. N., Leinonen, J., MacNeil, S., Randrianasolo, A. S., Becker, B. A., Kimmel, B., Wright, J. ve Briggs, B., The widening gap: The benefits and harms of generative AI for novice programmers, Proceedings, 2024 ACM Conference on International Computing Education Research V. 1, 469-486, (2024). https://doi.org/10.1145/3632620.3671116
2. [2] Lau, S. ve Guo, P., From “ban it till we understand it” to “resistance is futile”: How university programming instructors plan to adapt as more students use AI code generation and explanation tools such as ChatGPT and GitHub Copilot, Proceedings, 2023 ACM Conference on International Computing Education Research V. 1, 106-121, (2023). https://doi.org/10.1145/3568813.3600138
3. [3] Benda, K., Bruckman, A. ve Guzdial, M., When life and learning do not fit: Challenges of single-parent students in online computer science education, ACM Transactions on Computing Education, 12, 4, 1-38, (2012). https://doi.org/10.1145/2382564.2382567
4. [4] Charters, P., Lee, M., Ko, A. ve Loksa, D., Challenging stereotypes and changing attitudes: The effect of a brief programming encounter on adults’ attitudes toward programming, Proceedings, 45th ACM Technical Symposium on Computer Science Education, 653-658, (2014). https://doi.org/10.1145/2538862.2538938
5. [5] Becker, B. A., Denny, P., Finnie-Ansley, J., Luxton-Reilly, A., Prather, J. ve Santos, E. A., Programming is hard - or at least it used to be: Educational opportunities and challenges of AI code generation, Proceedings, 54th ACM Technical Symposium on Computer Science Education V. 1, 500-506, (2023). https://doi.org/10.1145/3545945.3569759
6. [6] Denny, P., Prather, J., Becker, B. A., Finnie-Ansley, J., Hellas, A., Leinonen, J., Luxton-Reilly, A., Reeves, B. N., Santos, E. A. ve Sarsa, S., Computing education in the era of generative AI, Communications of the ACM, 67, 2, 56-67, (2024). https://doi.org/10.1145/3624720
7. [7] Kazemitabaar, M., Chow, J., Ma, C. K. T., Ericson, B. J., Weintrop, D. ve Grossman, T., Studying the effect of AI code generators on supporting novice learners in introductory programming, Proceedings, 2023 CHI Conference on Human Factors in Computing Systems, Article 455, (2023). https://doi.org/10.1145/3544548.3580919
8. [8] Finnie-Ansley, J., Denny, P., Becker, B. A., Luxton-Reilly, A. ve Prather, J., The robots are coming: Exploring the implications of OpenAI Codex on introductory programming, Proceedings, 24th Australasian Computing Education Conference, 10-19, (2022). https://doi.org/10.1145/3511861.3511863

9. [9] Wiredu, J., Abuba, N. ve Zakaria, H., Impact of generative AI in academic integrity and learning outcomes: A case study in the upper east region, Asian Journal of Research in Computer Science, 17, 8, 70-88, (2024). https://doi.org/10.9734/ajrcos/2024/v17i7491
10. [10] Güneş, A. ve Kaban, A., A Delphi study on ethical challenges and ensuring academic integrity regarding AI research in higher education, Higher Education Quarterly, 79, 4, (2025). https://doi.org/10.1111/hequ.70057
11. [11] Nam, B. ve Bai, Q., ChatGPT and its ethical implications for STEM research and higher education: A media discourse analysis, International Journal of STEM Education, 10, Article 66, (2023). https://doi.org/10.1186/s40594-023-00452-5
12. [12] Yan, Y., Liu, H. ve Chau, T., A systematic review of AI ethics in education, Journal of Global Information Management, 33, 1, 1-50, (2025). https://doi.org/10.4018/jgim.386381
13. [13] Triantafyllou, S., MOOCs and EdTech tools for distance learning, Proceedings, 4th World Conference on Future of Education, (2021). https://doi.org/10.33422/4th.wcfeducation.2021.12.05
14. [14] Chang, C., Shen, H. ve Liu, Z., University faculty’s perspectives on the roles of e-instructors and their online instruction practice, International Review of Research in Open and Distributed Learning, 15, 3, (2014). https://doi.org/10.19173/irrodl.v15i3.1654
15. [15] Laufer, M., Leiser, A., Deacon, B., Brichambaut, P., Fecher, B., Kobsda, C. ve Hesse, F., Digital higher education: A divider or bridge builder? Leadership perspectives on edtech in a COVID-19 reality, International Journal of Educational Technology in Higher Education, 18, 1, (2021). https://doi.org/10.1186/s41239-021-00287-6
16. [16] Alainati, S., Al-Hunaiyyan, A. ve Alkhatib, H., Instructors’ digital competencies for innovative learning: Human resource management perspectives, International Journal of Professional Business Review, 8, 10, e03750, (2023). https://doi.org/10.26668/businessreview/2023.v8i10.3750
17. [17] Marengo, A., Pagano, A., Pange, J. ve Soomro, K., The educational value of artificial intelligence in higher education: A 10-year systematic literature review, Interactive Technology and Smart Education, 21, 4, 625-644, (2024). https://doi.org/10.1108/itse-11-2023-0218
18. [18] Sweller, J., Cognitive load during problem solving: Effects on learning, Cognitive Science, 12, 2, 257-285, (1988). https://doi.org/10.1207/s15516709cog1202_4
19. [19] Paas, F., Renkl, A. ve Sweller, J., Cognitive load theory and instructional design: Recent developments, Educational Psychologist, 38, 1, 1-4, (2003). https://doi.org/10.1207/S15326985EP3801_1
20. [20] Mishra, P. ve Koehler, M. J., Technological pedagogical content knowledge: A framework for teacher knowledge, Teachers College Record, 108, 6, 1017-1054, (2006). https://doi.org/10.1111/j.1467-9620.2006.00684.x
21. [21] Hamilton, E. R., Rosenberg, J. M. ve Akcaoglu, M., The substitution augmentation modification redefinition (SAMR) model: A critical review and suggestions for its use, TechTrends, 60, 5, 433-441, (2016). https://doi.org/10.1007/s11528-016-0091-y
22. [22] Wiggins, G., The case for authentic assessment, Practical Assessment, Research & Evaluation, 2, 2, (1990). https://doi.org/10.7275/ffb1-mm19
23. [23] Herrington, J. ve Herrington, A., Authentic conditions for authentic assessment: Aligning task and assessment, in Bunker, A. ve Vardi, I. (Eds.), Research and Development in Higher Education: Critical Visions: Teaching and Learning in Higher Education, 29, 146-151, Higher Education Research and Development Society of Australasia, (2006).
24. [24] Boud, D. ve Falchikov, N., Aligning assessment with long-term learning, Assessment & Evaluation in Higher Education, 31, 4, 399-413, (2006). https://doi.org/10.1080/02602930600679050
25. [25] Nguyen, A., Ngo, H., Hong, Y., Dang, B. ve Nguyen, B., Ethical principles for artificial intelligence in education, Education and Information Technologies, 28, 4, 4221-4241, (2022). https://doi.org/10.1007/s10639-022-11316-w
26. [26] Torraco, R. J., Writing integrative literature reviews: Using the past and present to explore the future, Human Resource Development Review, 15, 4, 404-428, (2016). https://doi.org/10.1177/1534484316671606
27. [27] Jabareen, Y., Building a conceptual framework: Philosophy, definitions, and procedure, International Journal of Qualitative Methods, 8, 4, 49-62, (2009). https://doi.org/10.1177/160940690900800406
28. [28] Vesna, L., Digital divide in AI-powered education: Challenges and solutions for equitable learning, Journal of Information Systems Engineering & Management, 10, 21s, 300-308, (2025). https://doi.org/10.52783/jisem.v10i21s.3327
29. [29] Zawacki-Richter, O., Marín, V. I., Bond, M. ve Gouverneur, F., Systematic review of research on artificial intelligence applications in higher education - where are the educators?, International Journal of Educational Technology in Higher Education, 16, 1, 39, (2019). https://doi.org/10.1186/s41239-019-0171-0
30. [30] Tarisayi, K., Strategic leadership for responsible artificial intelligence adoption in higher education, CTE Workshop Proceedings, 11, 4-14, (2024). https://doi.org/10.55056/cte.616
31. [31] Spivakovsky, O., Omelchuk, S., Kobets, V., Valko, N. ve Malchykova, D., Institutional policies on artificial intelligence in university learning, teaching and research, Information Technologies and Learning Tools, 97, 5, 181-202, (2023). https://doi.org/10.33407/itlt.v97i5.5395
32. [32] Michaeli, T., Romeike, R. ve Seegerer, S., What students can learn about artificial intelligence: Recommendations for K-12 computing education, Proceedings, ISSEP 2023, 196-208, (2023). https://doi.org/10.1007/978-3-031-43393-1_19
33. [33] Lindgren, H. ve Heintz, F., The WASP-ED AI curriculum: A holistic curriculum for artificial intelligence, Proceedings, INTED 2023, 6496-6502, (2023). https://doi.org/10.21125/inted.2023.1727
34. [34] Katajavuori, N., Salminen, O., Vuorensola, K., Huhtala, H., Vuorela, P. ve Hirvonen, J., Competence-based pharmacy education at the University of Helsinki, Pharmacy, 5, 2, 29, (2017). https://doi.org/10.3390/pharmacy5020029
35. [35] Thanh, B., Vo, D., Nhat, M., Pham, T., Thai, H. ve Son, H., Race with the machines: Assessing the capability of generative AI in solving authentic assessments, Australasian Journal of Educational Technology, 39, 5, 59-81, (2023). https://doi.org/10.14742/ajet.8902
36. [36] Franklin, D., Denny, P., Gonzalez-Maldonado, D. ve Tran, M., Generative AI in computer science education, Cambridge University Press, (2025). https://doi.org/10.1017/9781009581738
37. [37] Ncube, P., Redefining student assessment in AI-infused learning environments: A systematic review of challenges and strategies for academic integrity, AI and Ethics, 6, 1, (2025). https://doi.org/10.1007/s43681-025-00871-w
38. [38] Cutillas, A., Balili, E., Rellin, E., Remo, M., Quindala, E. ve Bacalso, F., Analyzing alternative academic assessments in universities with cheat-proofing behaviors and instructor-formulated mechanics in an AI-dependent setting, Environment and Social Psychology, 10, 5, (2025). https://doi.org/10.59429/esp.v10i5.3265
39. [39] Nikolopoulou, K., Assessment redefined: Educational assessment meets AI - ChatGPT challenges, Culminating Undergraduate Papers in Education Research, 8, 1, 17-30, (2025). https://doi.org/10.46303/cuper.2025.2
40. [40] Jodi, K., Zulfadli, M., Azizan, A., Husain, F. ve Haridi, N., Ethical framework for artificial intelligence in professional practices of higher education lecturers: A systematic review, International Journal of Education Psychology and Counseling, 10, 57, 194-212, (2025). https://doi.org/10.35631/ijepc.1057013
41. [41] Ren, X. ve Wu, M., Examining teaching competencies and challenges while integrating artificial intelligence in higher education, TechTrends, 69, 3, 519-538, (2025). https://doi.org/10.1007/s11528-025-01055-3
42. [42] Băiceanu, C., Artificial intelligence literacy in teacher education: Towards a pedagogical framework for responsible integration, Proceedings, Education, Psychology and Vocational Learning Conference, 200-218, (2025). https://doi.org/10.56177/epvl.ch21.2025.en
43. [43] Ahmed, J., Barriers to AI adoption in education: Insights from teacher’s perspectives, International Journal of Agriculture & Sustainable Development, 411-421, (2025). https://doi.org/10.33411/ijist/202571411421
44. [44] Kallunki, V., Kinnunen, P., Pyörälä, E., Haarala-Muhonen, A., Katajavuori, N. ve Myyry, L., Navigating the evolving landscape of teaching and learning: University faculty and staff perceptions of the artificial intelligence-altered terrain, Education Sciences, 14, 7, 727, (2024). https://doi.org/10.3390/educsci14070727
45. [45] Hill, R., AI as fad or AI as lasting? Priorities for college faculty instructional development for generative artificial intelligence, Irish Journal of Technology Enhanced Learning, 7, 2, 136-145, (2023). https://doi.org/10.22554/ijtel.v7i2.154
46. [46] Conceição, S. ve Samuel, A., Strategic integration of artificial intelligence in faculty development grounded in adult learning principles, New Directions for Adult and Continuing Education, 2025, 187, 64-71, (2025). https://doi.org/10.1002/ace.70003
47. [47] Alnasib, B., Factors affecting faculty members’ readiness to integrate artificial intelligence into their teaching practices: A study from the Saudi higher education context, International Journal of Learning Teaching and Educational Research, 22, 8, 465-491, (2023). https://doi.org/10.26803/ijlter.22.8.24