Araştırma Makalesi
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YAPAY ZEKÂ DESTEKLİ ANINDA GERİ BİLDİRİMİN LİSE ÖĞRENCİLERİNİN BAŞARI MOTİVASYONUNA ETKİSİ

Yıl 2026, Cilt: 16 Sayı: 1, 131 - 158, 13.01.2026
https://doi.org/10.17943/etku.1746065

Öz

Yapay zekâ teknolojilerinin eğitim ortamlarına entegrasyonu, özellikle geri bildirim süreçlerinde önemli bir dönüşüm yaratmakta ve öğrenci motivasyonunun desteklenmesine yönelik yeni olanaklar sunmaktadır. Bu araştırmanın amacı, yapay zekâ destekli anında geri bildirimin lise düzeyinde öğrenim gören öğrencilerin akademik başarı motivasyonuna etkisini incelemektir. Ön test-son test kontrol gruplu deneysel desenin kullanıldığı çalışmada, İstanbul'da öğrenim gören toplam 64 dokuzuncu sınıf öğrencisi rastgele yöntemle deney ve kontrol gruplarına atanmıştır. Deney grubundaki öğrenciler, dört hafta süresince kompozisyon yazma etkinliklerini yapay zekâ tabanlı bir platform (ChatGPT) aracılığıyla tamamlamış ve her etkinlik sonrası anında geri bildirim almıştır. Kontrol grubundaki öğrenciler ise aynı etkinlikleri öğretmenlerinden haftalık gecikmeli yazılı dönüt alarak gerçekleştirmiştir. Öğrencilerin motivasyon düzeyleri, Sarıtepeci (2018) tarafından geliştirilen Başarı Motivasyonu Ölçeği aracılığıyla ön test ve son test şeklinde ölçülmüştür. Bağımsız örneklem t-testi sonuçlarına göre, ön testte gruplar arasında anlamlı bir fark bulunmazken, son testte deney grubu lehine anlamlı bir artış gözlemlenmiştir. Bu bulgu, yapay zekâ destekli anında geri bildirimin öğrencilerin içsel motivasyonunu artırabileceğini ve bu etkinin Öz Belirleme Kuramı (Deci & Ryan, 1985) kapsamında tanımlanan özerklik, yeterlik ve ilişkisellik gibi temel psikolojik ihtiyaçların desteklenmesiyle ilişkili olduğunu göstermektedir. Ayrıca, K-means kümeleme analizi yoluyla elde edilen bulgular, yapay zekâ destekli uygulamanın farklı öğrenci profilleri üzerindeki etkisini daha ayrıntılı biçimde ortaya koymuştur. Deney grubundaki farklılaşmada, öğrencilerin daha önce yapay zekâ araçlarını kullanmış olmalarının belirleyici bir unsur olduğu anlaşılmıştır. Araştırma, yapay zekâ tabanlı dönüt sistemlerinin sadece bilişsel çıktılar değil, duyuşsal kazanımlar üzerinde de etkili olabileceğini deneysel olarak ortaya koyarak literatüre katkı sağlamaktadır.

Kaynakça

  • Alrashedi, N. (2020). Adaptive learning to enhance students’ understanding in learning technology experience. Technium Social Sciences Journal, 9(1), 32–40. https://doi.org/10.47577/tssj.v9i1.873
  • An, Y., Kim, J., & Park, S. (2023). Exploring motivational outcomes of AI-based feedback in digital classrooms. International Journal of Learning Analytics, 7(1), 44–59.
  • Anjarani, R., Yusuf, A., & Hadi, N. (2024). Intelligent tutoring systems: Enhancing engagement through personalized AI support. Computers & Education: Artificial Intelligence, 5, 100095.
  • Bostan, E., Kaya, A., & Demirtaş, S. (2021). Ortaöğretim öğrencilerinde motivasyonun akademik başarıya etkisi. Eğitimde Kuram ve Uygulama, 17(3), 265–284.
  • Boughida, M., Amine, A., & Slaoui, R. (2024). Psychological impact of AI-based learning environments on student behavior. Education and Information Technologies, 29(1), 115–138.
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2022). Bilimsel araştırma yöntemleri (30. bs.). Pegem Akademi.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  • Corral, D., Martin, L., & Owen, S. (2020). Immediate feedback effects on student learning performance: A meta-analysis. Learning and Instruction, 65, 101266.
  • Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Springer.
  • Dinh, T. T. H., & Pham, Q. L. (2024). Effective feedback strategies in digital learning: Clarity, personalization, and motivation. E-Learning Studies Quarterly, 12(1), 53–72.
  • Dülger, M. (2021). Yapay zekâ tabanlı geri bildirim sistemlerinin eğitsel katkısı: Kuramsal bir bakış. Eğitim Teknolojileri Dergisi, 8(2), 89–104.
  • Eccles, J. S., & Roeser, R. W. (2011). Schools as developmental contexts during adolescence. Journal of Research on Adolescence, 21(1), 225–241. https://doi.org/10.1111/j.1532-7795.2010.00725.x
  • Ellerman, T. (2024). Understanding student motivation in AI-enhanced environments. Contemporary Educational Psychology, 69, 102118.
  • Ellikkal, M., & Rajamohan, A. (2024). Supporting social connection in AI-driven learning platforms. AI and Education Review, 4(1), 88–104.
  • Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2024). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13544
  • Fanshawe, J., Kumar, P., & Dey, R. (2020). The role of formative feedback in improving student self-efficacy. Journal of Learning Development, 9(3), 211–228.
  • Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). McGraw-Hill.
  • Gan, Y., Liu, L., & Nang, Q. (2023). Personalized feedback and academic motivation: Evidence from AI-enabled systems. Educational Technology and Practice, 16(2), 89–105.
  • George, D., & Mallery, P. (2010). SPSS for Windows step by step: A simple guide and reference (10th ed.). Pearson.
  • Gramcheva, M. (2020). Feedback quality and student engagement in digital environments. Innovative Learning Review, 14(1), 66–78.
  • Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
  • Heffernan, N. T., & Heffernan, C. L. (2014). The ASSISTments ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24(4), 470–497. https://doi.org/10.1007/s40593-014-0024-x
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
  • Holstein, K., McLaren, B. M., & Aleven, V. (2019). Student learning benefits of a real-time classroom orchestration tool: A rigorous evaluation. Computers & Education, 140, 103599.
  • Huang, J., & Mizumoto, A. (2024). Enhancing student engagement through interactive AI agents. Journal of Educational Psychology, 116(2), 301–319.
  • Kadonsi, B. (2025). Effects of real-time AI feedback on learning outcomes in adolescents. Youth and Learning Technology Journal, 11(1), 72–90.
  • Karaoğlu, S., & Demir, S. (2020). Yapay zekâ destekli yazma uygulamalarının üniversite öğrencilerinin yazılı anlatım becerilerine etkisi. Yükseköğretim ve Bilim Dergisi, 10(2), 210–224.
  • Karasar, N. (2022). Bilimsel araştırma yöntemi: Kavramlar, ilkeler, teknikler (37. bs.). Nobel Yayıncılık.
  • Katonane Gyonyoru, M., & Katona, B. (2024). AI-powered learning platforms: Potentials and challenges in high school settings. International Journal of Digital Education, 20(1), 101–120.
  • Kaveh, A. (2020). The psychology of learning motivation: Theories and applications. Educational Psychology Today, 10(2), 55–74.
  • Kok, P., Tan, H., & Lee, W. (2024). AI and learner motivation: A theoretical synthesis. Pedagogical Innovations in Digital Learning, 5(1), 33–50.
  • Köse, S., & Arslan, E. (2021). Yapay zekâ destekli çevrim içi öğrenme sistemlerinin lise öğrencilerinin akademik başarı ve motivasyonlarına etkisi. Eğitim Teknolojisi Kuram ve Uygulama, 11(2), 180–197.
  • Lee, H., & Song, M. (2022). Digital distractions and student motivation: Addressing engagement in the age of technology. Journal of Educational Innovation, 23(4), 211–228.
  • Liu, Y., & Gumah, B. (2020). Motivation and feedback in online learning: Learner perspectives. Online Learning and Motivation Journal, 14(3), 96–109.
  • Lo, A., Zhang, B., & Kim, S. (2025). Real-time AI feedback and student revision behavior in writing: Effects on metacognition and perceived competence. Journal of Educational Psychology, 117(3), 456–472.
  • Lu, X., Huang, H., & Johnson, T. (2017). AI-assisted writing tools: Impacts on student writing motivation and performance. Journal of Writing Analytics, 1, 33–52.
  • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
  • Martinez, C., Lopez, R., & Duarte, S. (2024). Timing matters: Immediate vs. delayed feedback effects on secondary students. Learning and Motivation, 78, 101920.
  • Merdiaty, N., & Sulistiasih, E. (2024). Enhancing student motivation through adaptive AI systems. Asian Journal of Educational Technology, 13(2), 44–59.
  • Meyer, J. (2024). Using LLMs to bring evidence-based feedback into the classroom: Cognitive and affective-motivational outcomes. Journal of Educational Technology, 45(2), 123–140. https://doi.org/10.1016/j.edutech.2024.03.005
  • Meyer, J., Jansen, T., Schiller, R., Liebenow, L. W., Steinbach, M., Horbach, A., & Fleckenstein, J. (2024). Using LLMs to bring evidence-based feedback into the classroom: AI-generated feedback increases secondary students’ text revision, motivation, and positive emotions. Computers & Education: Artificial Intelligence, 6, 100199. https://doi.org/10.1016/j.caeai.2023.100199
  • Mohamed, S. (2024). Under the world of AI-generated feedback on writing: Effects on motivation, emotional intelligence, and writing skills in EFL contexts. Language Testing Asia, 2(3). https://doi.org/10.1186/s40468-025-00343-2
  • Narciss, S. (2013). Designing and evaluating tutoring feedback strategies for digital learning environments on the basis of the Interactive Tutoring Feedback Model. Digital Education Review, 23, 7–26.
  • Nijmeijer, K. J., Bruls, M., & Tomic, W. (2023). The effect of feedback timing on student motivation. European Journal of Educational Psychology, 22(1), 109–126.
  • Özdemir, M., & Uslu, Ö. (2022). Yapay zekâ tabanlı öğretim materyalleri ile desteklenen sınıf içi uygulamaların öğrenci tutum ve motivasyonuna etkisi. Eğitimde Kuram ve Uygulama, 18(1), 75–92.
  • Özer, M. (2016). Dijitalleşmenin eğitime etkisi: Gelecek senaryoları ve dönüşüm. Milli Eğitim Dergisi, 45(212), 17–29.
  • Paduraru, C. (2023). Effective feedback practices in digitally mediated education. Educational Practice Review, 7(3), 65–81.
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THE EFFECT OF ARTIFICIAL INTELLIGENCE-SUPPORTED INSTANT FEEDBACK ON THE ACHIEVEMENT MOTIVATION OF HIGH SCHOOL STUDENTS

Yıl 2026, Cilt: 16 Sayı: 1, 131 - 158, 13.01.2026
https://doi.org/10.17943/etku.1746065

Öz

The integration of artificial intelligence (AI) technologies into educational environments has led to a transformation in how feedback is delivered and how student motivation is supported. This study aims to examine the effect of AI-supported instant feedback on the academic achievement motivation of high school students. Within the framework of the pretest-posttest control group experimental design, a total of 64 ninth-grade students were randomly assigned to experimental and control groups. While the experimental group received real-time feedback through an AI-based platform (ChatGPT) during weekly composition writing tasks, the control group received traditional delayed written feedback from their teacher. Data were collected using the Academic Achievement Motivation Scale developed by Sarıtepeci (2018), and both pre- and post-tests were administered to assess motivational change. The results of independent samples t-tests indicated no significant difference between the groups in the pretest phase. However, post-test results revealed a statistically significant increase in the motivation levels of the experimental group compared to the control group. These findings suggest that instant and personalized feedback provided by AI can significantly enhance students’ intrinsic motivation by supporting key psychological needs such as autonomy, competence, and relatedness, as defined in Self-Determination Theory (Deci & Ryan, 1985). Furthermore, the findings obtained through K-means clustering analysis have revealed the impact of the AI-supported application on different student profiles in greater detail. In the differentiation of the experimental group, it was understood that the students' previous experience with artificial intelligence tools was a decisive factor. The study contributes to the literature by experimentally validating the motivational impact of AI-based feedback systems and highlights the potential of such tools to enrich students' learning experiences beyond cognitive outcomes.

Kaynakça

  • Alrashedi, N. (2020). Adaptive learning to enhance students’ understanding in learning technology experience. Technium Social Sciences Journal, 9(1), 32–40. https://doi.org/10.47577/tssj.v9i1.873
  • An, Y., Kim, J., & Park, S. (2023). Exploring motivational outcomes of AI-based feedback in digital classrooms. International Journal of Learning Analytics, 7(1), 44–59.
  • Anjarani, R., Yusuf, A., & Hadi, N. (2024). Intelligent tutoring systems: Enhancing engagement through personalized AI support. Computers & Education: Artificial Intelligence, 5, 100095.
  • Bostan, E., Kaya, A., & Demirtaş, S. (2021). Ortaöğretim öğrencilerinde motivasyonun akademik başarıya etkisi. Eğitimde Kuram ve Uygulama, 17(3), 265–284.
  • Boughida, M., Amine, A., & Slaoui, R. (2024). Psychological impact of AI-based learning environments on student behavior. Education and Information Technologies, 29(1), 115–138.
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2022). Bilimsel araştırma yöntemleri (30. bs.). Pegem Akademi.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  • Corral, D., Martin, L., & Owen, S. (2020). Immediate feedback effects on student learning performance: A meta-analysis. Learning and Instruction, 65, 101266.
  • Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Springer.
  • Dinh, T. T. H., & Pham, Q. L. (2024). Effective feedback strategies in digital learning: Clarity, personalization, and motivation. E-Learning Studies Quarterly, 12(1), 53–72.
  • Dülger, M. (2021). Yapay zekâ tabanlı geri bildirim sistemlerinin eğitsel katkısı: Kuramsal bir bakış. Eğitim Teknolojileri Dergisi, 8(2), 89–104.
  • Eccles, J. S., & Roeser, R. W. (2011). Schools as developmental contexts during adolescence. Journal of Research on Adolescence, 21(1), 225–241. https://doi.org/10.1111/j.1532-7795.2010.00725.x
  • Ellerman, T. (2024). Understanding student motivation in AI-enhanced environments. Contemporary Educational Psychology, 69, 102118.
  • Ellikkal, M., & Rajamohan, A. (2024). Supporting social connection in AI-driven learning platforms. AI and Education Review, 4(1), 88–104.
  • Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2024). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13544
  • Fanshawe, J., Kumar, P., & Dey, R. (2020). The role of formative feedback in improving student self-efficacy. Journal of Learning Development, 9(3), 211–228.
  • Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). McGraw-Hill.
  • Gan, Y., Liu, L., & Nang, Q. (2023). Personalized feedback and academic motivation: Evidence from AI-enabled systems. Educational Technology and Practice, 16(2), 89–105.
  • George, D., & Mallery, P. (2010). SPSS for Windows step by step: A simple guide and reference (10th ed.). Pearson.
  • Gramcheva, M. (2020). Feedback quality and student engagement in digital environments. Innovative Learning Review, 14(1), 66–78.
  • Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
  • Heffernan, N. T., & Heffernan, C. L. (2014). The ASSISTments ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24(4), 470–497. https://doi.org/10.1007/s40593-014-0024-x
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
  • Holstein, K., McLaren, B. M., & Aleven, V. (2019). Student learning benefits of a real-time classroom orchestration tool: A rigorous evaluation. Computers & Education, 140, 103599.
  • Huang, J., & Mizumoto, A. (2024). Enhancing student engagement through interactive AI agents. Journal of Educational Psychology, 116(2), 301–319.
  • Kadonsi, B. (2025). Effects of real-time AI feedback on learning outcomes in adolescents. Youth and Learning Technology Journal, 11(1), 72–90.
  • Karaoğlu, S., & Demir, S. (2020). Yapay zekâ destekli yazma uygulamalarının üniversite öğrencilerinin yazılı anlatım becerilerine etkisi. Yükseköğretim ve Bilim Dergisi, 10(2), 210–224.
  • Karasar, N. (2022). Bilimsel araştırma yöntemi: Kavramlar, ilkeler, teknikler (37. bs.). Nobel Yayıncılık.
  • Katonane Gyonyoru, M., & Katona, B. (2024). AI-powered learning platforms: Potentials and challenges in high school settings. International Journal of Digital Education, 20(1), 101–120.
  • Kaveh, A. (2020). The psychology of learning motivation: Theories and applications. Educational Psychology Today, 10(2), 55–74.
  • Kok, P., Tan, H., & Lee, W. (2024). AI and learner motivation: A theoretical synthesis. Pedagogical Innovations in Digital Learning, 5(1), 33–50.
  • Köse, S., & Arslan, E. (2021). Yapay zekâ destekli çevrim içi öğrenme sistemlerinin lise öğrencilerinin akademik başarı ve motivasyonlarına etkisi. Eğitim Teknolojisi Kuram ve Uygulama, 11(2), 180–197.
  • Lee, H., & Song, M. (2022). Digital distractions and student motivation: Addressing engagement in the age of technology. Journal of Educational Innovation, 23(4), 211–228.
  • Liu, Y., & Gumah, B. (2020). Motivation and feedback in online learning: Learner perspectives. Online Learning and Motivation Journal, 14(3), 96–109.
  • Lo, A., Zhang, B., & Kim, S. (2025). Real-time AI feedback and student revision behavior in writing: Effects on metacognition and perceived competence. Journal of Educational Psychology, 117(3), 456–472.
  • Lu, X., Huang, H., & Johnson, T. (2017). AI-assisted writing tools: Impacts on student writing motivation and performance. Journal of Writing Analytics, 1, 33–52.
  • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
  • Martinez, C., Lopez, R., & Duarte, S. (2024). Timing matters: Immediate vs. delayed feedback effects on secondary students. Learning and Motivation, 78, 101920.
  • Merdiaty, N., & Sulistiasih, E. (2024). Enhancing student motivation through adaptive AI systems. Asian Journal of Educational Technology, 13(2), 44–59.
  • Meyer, J. (2024). Using LLMs to bring evidence-based feedback into the classroom: Cognitive and affective-motivational outcomes. Journal of Educational Technology, 45(2), 123–140. https://doi.org/10.1016/j.edutech.2024.03.005
  • Meyer, J., Jansen, T., Schiller, R., Liebenow, L. W., Steinbach, M., Horbach, A., & Fleckenstein, J. (2024). Using LLMs to bring evidence-based feedback into the classroom: AI-generated feedback increases secondary students’ text revision, motivation, and positive emotions. Computers & Education: Artificial Intelligence, 6, 100199. https://doi.org/10.1016/j.caeai.2023.100199
  • Mohamed, S. (2024). Under the world of AI-generated feedback on writing: Effects on motivation, emotional intelligence, and writing skills in EFL contexts. Language Testing Asia, 2(3). https://doi.org/10.1186/s40468-025-00343-2
  • Narciss, S. (2013). Designing and evaluating tutoring feedback strategies for digital learning environments on the basis of the Interactive Tutoring Feedback Model. Digital Education Review, 23, 7–26.
  • Nijmeijer, K. J., Bruls, M., & Tomic, W. (2023). The effect of feedback timing on student motivation. European Journal of Educational Psychology, 22(1), 109–126.
  • Özdemir, M., & Uslu, Ö. (2022). Yapay zekâ tabanlı öğretim materyalleri ile desteklenen sınıf içi uygulamaların öğrenci tutum ve motivasyonuna etkisi. Eğitimde Kuram ve Uygulama, 18(1), 75–92.
  • Özer, M. (2016). Dijitalleşmenin eğitime etkisi: Gelecek senaryoları ve dönüşüm. Milli Eğitim Dergisi, 45(212), 17–29.
  • Paduraru, C. (2023). Effective feedback practices in digitally mediated education. Educational Practice Review, 7(3), 65–81.
  • Pitychoutis, P. (2024). Exploring AI feedback mechanisms in secondary education: Student reactions and outcomes. Technology in Learning Journal, 15(1), 44–62.
  • Poláková, P., & Ivenz, P. (2024). The impact of ChatGPT feedback on the development of EFL students’ writing skills. Cogent Education, 11(1), 2410101. https://doi.org/10.1080/2331186X.2024.2410101
  • Qi, L., Zhang, Y., & Min, H. (2024). Emotional and cognitive dimensions of digital feedback. Educational Psychology Research, 19(2), 98–116.
  • Razzaq, R., Ostrow, K., & Heffernan, N. (2020). Effect of immediate feedback on math achievement at the high school level. In Artificial Intelligence in Education (Vol. 12164, pp. 263–267). Springer. https://doi.org/10.1007/978-3-030-52240-7_48
  • Rohana, A., Mahmud, R., & Daud, A. (2024). Role of feedback in improving learners’ engagement. Malaysian Online Journal of Educational Technology, 12(1), 33–49.
  • Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3
  • Saini, N., Kaur, J., & Sharma, R. (2025). AI-driven feedback systems and teacher perspectives. Journal of Educational Computing Research, 63(1), 101–122.
  • Sarıtepeci, M. (2018). Akademik başarının yordayıcısı olarak başarı motivasyonu ölçeği: Geçerlik ve güvenirlik çalışması. Eğitim ve Bilim, 43(193), 109–125. https://doi.org/10.15390/EB.2018.7432
  • Schei, V., Aarstad, J., & Pedersen, T. (2024). Chatbot effectiveness in educational feedback: An experimental study. Scandinavian Journal of Educational Research, 68(1), 47–68.
  • Sharafi, A. (2024). Motivasyonun eğitimdeki rolü: Yeni nesil öğrenme yaklaşımları. Eğitimde Dönüşüm Dergisi, 12(1), 22–39.
  • Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189. https://doi.org/10.3102/0034654307313795
  • Siyu, L. (2025). Motivation in learning: An integrative review of emerging trends. Global Education Studies, 18(2), 74–93.
  • Sung, J., Guillain, A., & Schneider, M. (2025). Student perceptions of social presence in AI-based feedback tools. Educational AI Review, 9(1), 99–113.
  • VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369
  • Wang, X. (2025). Generative artificial intelligence in pedagogical practices: A systematic review of empirical studies (2022–2024). Cogent Education, 12(1), 2485499. https://doi.org/10.1080/2331186X.2025.2485499
  • Whalen, B., Carter, L., & Singh, R. (2023). Feedback loops in digital learning platforms. Learning Technology Horizons, 7(2), 122–139.
  • Wu, Z., Han, L., & Meng, T. (2024). Digital motivation: Exploring the link between screen use and learning goals. Frontiers in Psychology, 15, 1103219.
  • Xiong, Y. (2024). Intelligent feedback systems and student engagement in secondary schools. AI and Education Today, 8(1), 66–81.
  • Yakhnich, L., Himi, H., & Michael, L. (2021). Adolescent identity formation and the role of learning motivation. Journal of Adolescent Development, 34(2), 201–220.
  • Yıldız, M., & Karaman, P. (2019). Çevrim içi yazma platformlarının lise öğrencilerinin yazma becerilerine ve motivasyonuna etkisi. Eğitimde Nitel Araştırmalar Dergisi, 7(4), 1057–1080.
  • Yudelson, M., Koedinger, K. R., & Gordon, G. J. (2013). Individualized Bayesian knowledge tracing models. In Artificial Intelligence in Education (pp. 171–180). Springer. https://doi.org/10.1007/978-3-642-33112-5_18
  • Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). 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), 1–27. https://doi.org/10.1186/s41239-019-0171-0
  • Zhan, Y., & Yan, Z. (2025). Students’ engagement with ChatGPT feedback: Feedback literacy in generative AI environments. Assessment & Evaluation in Higher Education. https://doi.org/10.1080/02602938.2025.2471821
Toplam 70 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Eğitim Teknolojisi ve Bilgi İşlem
Bölüm Araştırma Makalesi
Yazarlar

Cansu Şahin Kölemen 0000-0003-2376-7899

Ersin Şahin 0000-0003-4466-7483

Gönderilme Tarihi 18 Temmuz 2025
Kabul Tarihi 14 Ekim 2025
Yayımlanma Tarihi 13 Ocak 2026
Yayımlandığı Sayı Yıl 2026 Cilt: 16 Sayı: 1

Kaynak Göster

APA Şahin Kölemen, C., & Şahin, E. (2026). YAPAY ZEKÂ DESTEKLİ ANINDA GERİ BİLDİRİMİN LİSE ÖĞRENCİLERİNİN BAŞARI MOTİVASYONUNA ETKİSİ. Eğitim Teknolojisi Kuram ve Uygulama, 16(1), 131-158. https://doi.org/10.17943/etku.1746065