PIAGET’NİN BİLİŞSEL GELİŞİM TEORİSİ PERSPEKTİFİNDEN YAPAY ZEKÂ SİSTEMLERİNİN DEĞERLENDİRİLMESİ: KAVRAMSAL BİR İNCELEME

Öz

Makinelerin mantıksal akıl yürütme, öğrenme ve problem çözme gibi insan zekâsı gerektiren görevleri yapabilme yeteneği olan yapay zekânın (YZ) insan hayatındaki rolü artmaktadır. Psikoloji çalışmalarında YZ’nin algılama, öğrenme ve karar verme gibi insanın bilişsel becerilerini simüle etme kapasitesi araştırılmaktadır. YZ’nin bu becerileri gerçekleştirebilmesi için kuramların temel oluşturabileceği düşünülmektedir. Bu makale, Piaget’nin Bilişsel Gelişim Teorisi perspektifinden YZ sistemlerini değerlendiren kavramsal bir incelemedir. YZ teknolojileri ve insanın bilişsel gelişim evreleri arasındaki benzerliklerin ve farklılıkların incelenmesi amaçlanmış, dört evre kapsamında değerlendirilmiştir. Duyu-motor evrede bebeğin duyu organları ve uzuvlarıyla dünyayı tanıma yeteneği, YZ’nin sensör verilerine; işlem öncesi evredeki dil öğrenmenin, doğal dil işleme sistemlerine benzediği düşünülmektedir. Somut işlemsel evredeki nesnelerle sınırlı olan mantıksal çıkarımlar, kural tabanlı sistemlerle; soyut işlemsel evredeki hipotetik düşünme, derin öğrenme algoritmalarındaki soyut temsillerle örtüşmektedir. Ancak YZ’nin bilişsel süreçleri taklit edebilmesindeki bu benzerliklerin sınırlı olduğu görülmüştür. Sonuçta YZ’nin insanı simüle etmesinde başta Piaget’nin teorisi olmak üzere psikoloji kuramlarının rehber olabileceği düşünülmektedir.

Anahtar Kelimeler

• yapay zekâ
• Piaget
• Bilişsel Gelişim Teorisi
• benzerlikler
• farklılıklar

Etik Beyan

Derleme, araştırma ve yayın etiği kurallarına uygun olarak hazırlanmıştır. Yapılan bu çalışma, etik kurul izni gerektirmemektedir.

An Evaluation of Artificial Intelligence Systems from the Perspective of Piaget’s Cognitive Development Theory: A Conceptual Review

Öz

Artificial intelligence (AI), defined as machines’ ability to perform tasks requiring human intelligence such as reasoning, learning, and problem-solving, plays an increasingly prominent role in human life. Psychological research examines AI’s capacity to simulate cognitive abilities including perception, learning, and decision-making. This article conceptually evaluates AI systems through Piaget’s Theory of Cognitive Development, examining parallels across four developmental stages. The sensorimotor stage, where infants explore through sensory and motor functions, parallels AI’s sensor data processing; the preoperational stage’s language acquisition resembles natural language processing systems; concrete operational reasoning relates to rule-based systems; and formal operational thought corresponds to deep learning’s abstract representations. However, analysis reveals these similarities remain limited in AI’s cognitive replication capacity. The study concludes that psychological theories, particularly Piaget’s framework, can guide AI development toward more sophisticated human cognition simulation.

Anahtar Kelimeler

• artificial intelligence
• Piaget
• Cognitive Development Theory
• similarities
• differences

Etik Beyan

The review has been prepared in accordance with research and publication ethics. This study does not require ethics committee approval.

Kaynakça

1. Abdollahi, H., Mahoor, M. H., Zandie, R., Siewierski, J., Qualls, H. (2023). Artificial emotional intelligence in socially assistive robots for older adults: A pilot study. IEEE Transactions on Affective Computing, 14(3), 2020-2032.
2. Abrar, F., Mehmood, S., Kandhro, A. N. (2025). Cognitive development and AI: A longitudinal study of children and adults navigating problem-solving with AI tools. The Critical Review of Social Sciences Studies, 3(1), 1888-1904.
3. American Psychological Association Dictionary. (2025, Nisan 11). Definition of cognitive development. https://dictionary.apa.org/cognitive-development
4. Asante, J. & Hanson, R. (2018). Exploring Ghanaian children conservation of number. Journal of Information Technologies and Lifelong Learning, 1(2), 28-35.
5. Aydın, B. (2013). Çocuk ve ergen psikolojisi. Nobel Akademik Yayıncılık.
6. Babakr, Z., Mohamedamin, P., Kakamad, K. (2019). Piaget’s cognitive developmental theory: Critical review. Education Quarterly Reviews, 2(3), 517-524.
7. Bálint, A., Jenák, M., Rab, V. (2025). Three scenarios of development: A predictive psychobiography of ChatGPT. (Makale taslağı / preprint).
8. Barak, T. & Loewenstein, Y. (2024). Untrained neural networks can demonstrate memorization-independent abstract reasoning. Scientific Reports, 14(1), 1-12.

9. Bender, E. M. & Koller, A. (2020, July). Climbing towards NLU: On meaning, form, and understanding in the age of data. Proceedings of the 58th annual meeting of the association for computational linguistics içinde (5185-5198. ss.).
10. Berberoğlugil, B. M. (2023). Yönetimde yapay zekâ. Scientific Journal of Innovation and Social Sciences Research, 3(2), 81-96.
11. Binz, M. & Schulz, E. (2023). Using cognitive psychology to understand GPT-3. Nature Human Behaviour, 7(9), 1493-1505.
12. Blasch, E., Pham, T., Chong, C. Y., Koch, W., Leung, H., Braines, D., Abdelzaher, T. (2021). Machine learning/artificial intelligence for sensor data fusion-opportunities and challenges. IEEE Aerospace and Electronic Systems Magazine, 36(7), 80-93.
13. Blockeel, H., Devos, L., Frénay, B., Nanfack, G., Nijssen. (2023). Decision trees: From efficient prediction to responsible AI. Frontiers in Artificial Intelligence, (6).
14. Borgelt, C. & Kruse, R. (2006). Artificial Intelligence Methodologies. A. Munack (Ed.), Handbook of Agricultural Engineering Volume VI Information Technology içinde (153-168. ss.), American Society of Agricultural Engineers.
15. Brunke, L., Greeff, M., Hall, A. W., Yuan, Z., Zhou, Panerati, J., Schoellig, A. P. (2022). Safe learning in robotics: From learning-based control to safe reinforcement learning. Annual Review of Control, Robotics, and Autonomous Systems, 5(1), 411-444.
16. Buntine, W. (2020). Learning classification trees. D. J. Hand (Ed.), Artificial Intelligence Frontiers in Statistics içinde (182-201. ss.), Chapman and Hall/CRC.
17. Calais, G. J. (2008). Overlapping Waves Theory to gauge learning in a balanced reading ınstruction framework. Focus on Colleges, Unıversities, and Schools, 2(1), 1-10.
18. Cangelosi, A. & Schlesinger, M. (2015). Developmental robotics: From babies to robots. MIT Press.
19. Cardona, M. A., Rodríguez, R. J., Ishmael, K. (Ed.). (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. U.S. Department of Education, Office of Educational Technology.
20. Case, R. (1992). Neo-Piagetian theories of child development. R. J. Sternberg, C. A. Berg (Ed.), Intellectual Development içinde (ss. 161-196). Cambridge University Press.
21. Charbuty, B. & Abdulazeez, A. (2021). Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(01), 20-28.
22. Chen, M. (2023, Aralık 6). What is AI model training & why is it important?. https://www.oracle.com/uk/artificial-intelligence/ai-model-training/
23. Chowdhary, K. R. (2020). Natural language processing. K. R. Chowdhary (Ed.), Fundamentals of Artificial Intelligence içinde (603-649. ss.), Springer.
24. Clark, A. (2008). Supersizing the mind: Embodiment, action, and cognitive extension. Oxford University Press.
25. Dasen, P. R. (2022). Culture and cognitive development. Journal of Cross-Cultural Psychology, 53(7), 789-816.
26. Davidson, G., Orhan, A. E., Lake, B. M. (2024). Spatial relation categorization in infants and deep neural networks. Cognition, (245), 105690.
27. de la Barrera, U., Mónaco, E., Postigo-Zegarra, Gil-Gómez, J. A., Montoya-Castilla, I. (2021). EmoTIC: Impact of a game-based social-emotional programme on adolescents. Plos one, 16(4).
28. Demirkol, Z. (2022). Herkes için yapay zekâ. Genç Destek.
29. Dhanasekar, V., Preethi, Y., Vishali, S., Ir, P. J. (2021). A chatbot to promote students mental health through emotion recognition. 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) içinde (1412-1416. ss.), IEEE.
30. Dinçer, İ., Arcaklıoğlu, E., Ezan, M. A. (2022). Enerjide yapay zekânın rolü raporu. Türkiye Bilimler Akademisi.
31. Efe, A. (2021). Yapay zekâ odaklı siber risk ve güvenlik yönetimi. Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi, 5(2), 144-165.
32. Eral, H. (2024). Eğitimde yapay zekâ uygulamaları uluslararası forumu raporu. Türkiye Cumhuriyeti Milli Eğitim Bakanlığı Yenilik ve Eğitim Teknolojileri Genel Müdürlüğü.
33. Erickson, B. J. (2021). Basic artificial intelligence techniques: Machine learning and deep learning. Radiologic Clinics, 59(6), 933-940.
34. Eroğlu, M. (2023). Çocukluk döneminde bilişsel gelişim: Piaget ve Vygotsky’nin bilişsel gelişim kuramlarının incelenmesi ve karşılaştırılması. Eğitim ve Yeni Yaklaşımlar Dergisi, 6(1), 69-77.
35. Eymann, V. (2024). Divergent and convergent thinking. (Yayımlanmamış doktora tezi). Kaiserslautern-Landau: Rheinland-Pfälzische Technische Universität.
36. Fan, J., Fang, L., Wu, J., Guo, Y., Dai, Q. (2020). From Brain science to artificial intelligence. Engineering, 6(3), 248-252.
37. Figlio, D. N., Freese, J., Karbownik, K., Roth, J. (2017). Socioeconomic status and genetic influences on cognitive development. Proceedings of the National Academy of Sciences, 114(51), 13441-13446.
38. Filippini, C., Spadolini, E., Cardone, D. Bianchi, D., Preziuso, M., Sciarreta, C., del Cimmuto, V., Lisciani, D., Merla, A. (2021). Facilitating the child-robot interaction by endowing the robot with the capability of understanding the child engagement: The case of Mio Amico Robot. International Journal of Social Robotics, (13), 677-689.
39. Fischer, K. W. (1980). A theory of cognitive development: The control and construction of hierarchies of skills. Psychological Review, 87(6), 477-531.
40. Fuad, M. T. H., Fime, A. A., Sikder, D., Iftee, M. A. R., Rabbi, J., Al-Rakhami, M. S., Gumaei, A., Sen, O., Fuad, M., Islam, M. N. (2021). Recent advances in deep learning techniques for face recognition. IEEE Access, (9), 99112-99142.
41. Gander, M. J. & Gardiner, H. W. (2007). Çocuk ve ergen gelişimi. (Çev: A. Dönmez, H. N. Çelen, B. Onur), İmge Kitabevi.
42. Garcez, A. D. A., Gori, M., Lamb, L. C., Serafini, L., Spranger, M., Tran, S. N. (2019). Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning. arXiv preprint arXiv:1905.06088.
43. Georgeon, O. L. (2017). Little AI: Playing a constructivist robot. SoftwareX, (6), 161-164.
44. Goertzel, B. (2006). The hidden pattern: A patternist philosophy of mind. BrownWalker Press.
45. Grice, H. P. (1975). Logic and conversation. P. Cole, J. L. Morgan (Ed.), Syntax and semantics içinde (41-58. ss.). Academic Press.
46. Güney, M. (2020). 48-60 aylık çocuklarda bilişsel işlevler ile sembolik oyun becerilerinin incelenmesi. (Yayımlanmamış yüksek lisans tezi). Ankara: Ankara Üniversitesi Sağlık Bilimleri Enstitüsü.
47. Haddad, A., Doherty, R., Purtilo, R. (2019). Chapter 11 - Respectful interaction: Working with newborns, infants, and children in the early years. Health Professional and Patient Interaction içinde (167-218). Elseiver.
48. Halford, G. S. (1993). Children’s understanding: The development of mental models. Lawrence Erlbaum Associates, Inc.
49. Hassabis, D., Kumaran, D., Summerfield, C., Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258.
50. Işık, U., Ölçekçi, H., Koz, K. A. (2022). Yapay zekâ ve algoritma ekseninde gazeteciliğin geleceği ve toplum için anlamı. Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi, 10(2), 1248-1275.
51. Jakhar, D. & Kaur, I. (2019). Artificial intelligence, machine learning and deep learning: Definitions and differences. Clinical and Experimental Dermatology, 45(1), 131-132.
52. Janiesch, C., Zschech, P., Heinrich, K. (2021). Machine learning and deep learning. Electron Markets, (31), 685-695.
53. Johri, P., Khatri, K., Al-Taani, A. T., Sabharwal, M., Suvanov, Kumar, A. (2021). Natural language processing: History, evolution, application, and future work. D. Virmani, A. Abraham, O. Castillo (Ed.), Proceedings of 3rd International Conference on Computing Informatics and Networks: ICCIN 2020 içinde (365-375. ss.), Springer Singapore.
54. Karakaş, S. (2017). Prof. Dr. Sirel Karakaş psikoloji sözlüğü: Bilgisayar programı ve veritabanı - www.psikolojisozlugu.com (sürüm: 5.2.0 / 2022)
55. Khurana, D., Koli, A., Khatter, K., Singh, S. (2023). Natural language processing: State of the art, current trends and challenges. Multimedia Tools and Applications, 82(3), 3713-3744.
56. Kol, S. (2011). Erken çocuklukta bilişsel gelişim ve dil gelişimi. Sakarya Üniversitesi Eğitim Fakültesi Dergisi, (21), 1-21.
57. Krichen, M. (2023). Convolutional neural networks: A survey. Computers, 12(8), 151.
58. Lai, T., Xie, C., Ruan, M., Wang, Z., Lu, H., Fu, S. (2023a). Influence of artificial intelligence in education on adolescents’ social adaptability: The mediatory role of social support. Plos one, 18(3).
59. Lai, T., Zeng, X., Xu, B., Xie, C., Liu, Y., Wang, Z., Lu, H., Fu, S. (2023b). The application of artificial intelligence technology in education influences Chinese adolescent’s emotional perception. Current Psychology, 43(6), 5309-5317.
60. Lake, B. M., Ullman, T. D., Tenenbaum, J. B., Gershman, J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, (40).
61. Lamkin-Kennard, K. A. & Popovic, M. B. (2018). Sensors: Natural and synthetic sensors. M. B. Popovic (Ed.), Biomechatronics içinde (81-107. ss.), Academic Press.
62. Lee, D. & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(271).
63. Leonelli, S. & Williamson, H. F. (2023). Artificial intelligence in plant and agricultural research. A. Choudhary, G. Fox, T. Hey (Ed.), Artificial Intelligence for Science: A Deep Learning Revolution içinde (319-333. ss.), World Scientific Publishing Company.
64. Liu, F., Chen, D., Wang, F., Li, Z., Xu, F. (2023). Deep learning based single sample face recognition: A survey. Artificial Intelligence Review, 56(3), 2723-2748.
65. Logan, D. E., Breazeal, C., Goodwin, M. S., Jeong, S., O’Connell, B., Smith-Freedman, D., Heathers, J., Weinstock, P. (2019). Social robots for hospitalized children. Pediatrics, 144(1), e20181511.
66. López, G., Quesada, L., Guerrero, L. A. (2018). Alexa vs. Siri vs. Cortana vs. Google assistant: A comparison of speech-based natural user interfaces. I. L. Nunes (Ed.), Advances in Human Factors and Systems Interaction: Proceedings of the AHFE 2017 International Conference on Human Factors and Systems Interaction içinde (241-250. ss.), Springer International Publishing.
67. López-Ortega, M., García-Ramírez, F., Morales, A. (2023). A multi-agent system model to advance artificial general intelligence based on Piaget’s theory of cognitive development. (Makale dosyası; yayımlanmış veya preprint formatı).
68. Marcus, G. (2020). The next decade in AI: four steps towards robust artificial intelligence. arXiv preprint arXiv:2002.06177.
69. Marino, F., Chilà, P., Sfrazzetto, S. T., Carrozza, C., Crimi, I., Failla, C., Busa, M., Bernava, G., Tartarisco, G., Vagni, D., Ruta, L., Pioggia, G. (2020). Outcomes of a robot-assisted social-emotional understanding intervention for young children with autism spectrum disorders. Journal of Autism and Developmental Disorders, (50), 1973-1987.
70. Masri, N., Sultan, Y. A., Akkila, A. N., Almasri, A., Ahmed, A., Mahmoud, A. Y., Zaqout, I., Abu-Naser, S. S. (2019). Survey of rule-based systems. International Journal of Academic Information Systems Research (IJAISR), 3(7), 1-23.
71. Meltzoff, A. N., Kuhl, P. K., Movellan, J., Sejnowski, T. J. (2009). Foundations for a new science of learning. Science, 325(5938), 284-288.
72. Miller, P. H. (1993). Theories of developmental psychology. Freeman.
73. Mollon, J., Knowles, E. E., Mathias, S. R., Gur, R., Peralta, J. M., Weiner, D. J., Robinson, E. B., Gur, R. E., Blangero, J., Almasy, L., Glahn, D. C. (2021). Genetic influence on cognitive development between childhood and adulthood. Molecular Psychiatry, 26(2), 656-665.
74. Morandín-Ahuerma, F. (2022). What is artificial intelligence?. International Journal of Research Publication and Reviews, 3(12), 1947-1951.
75. Namlı, Ş. (2023). Pragmatik dil becerilerinin değerlendirilmesi. Çocuk ve Gelişim Dergisi, 6(11), 67-92.
76. Namlısesli, D., Baş, H. N., Bostancı, H., Coşkun, B., Erol Barkana, D., Tarakçı, D. (2024). The effect of use of social robot NAO on children’s motivation and emotional states in special education. 21st International Conference on Ubiquitous Robots.
77. Nishimoto, R. & Tani, J. (2009). Development of hierarchical structures for actions and motor imagery: A constructivist view from synthetic neuro-robotics study. Psychological Research, 73(4), 545-558.
78. O’Gieblyn, M. (2023). Tanrı, insan, hayvan, makine. (Çev: F. Sarıalioğlu), Altın Kitaplar.
79. Oudeyer, P. Y. & Kaplan, F. (2007). What is intrinsic motivation? A typology of computational approaches. Frontiers in Neurorobotics, 1(6).
80. Pascual-Leone, J. (1969). A mathematical model for the transition rule in Piaget’s developmental stages. Acta Psychologica, (32), 301-345.
81. Perone, S. & Simmering, V. R. (2017). Applications of dynamic systems theory to cognition and development: New frontiers. Advances in Child Development and Behavior, (52), 43-80.
82. Piaget, J. (1971). The theory of stages in cognitive development. D. R. Green, M. P. Ford, G. B. Flamer (Ed.), Measurement and Piaget içinde (1-11. ss.), McGraw-Hill.
83. Pinto-Bernal, M., J. Sierra, S. D., Munera, M., Casas, D., Villa-Moreno, A., Frizera-Neto, A., Stoelen, M. F., Belpaeme, T., Cifuentes, C. A. (2023). Do different robot appearances change emotion recognition in children with ASD? Frontiers in Neurorobotics, (17).
84. Rossi, S., Santini, S. J., Di Genova, D., Maggi, G., Verrotti, A., Farello, G., Romualdi, R., Alisi, A., Tozzi, A. E., Balsano, C. (2022). Using the social robot NAO for emotional support to children at a pediatric emergency department: Randomized clinical trial. Journal of Medical Internet Research, 24(1).
85. Sağıroğlu, Ş. (2024). Türkiye’de yapay zekâ çalışmaları ve değerlendirmeler. Yeni Türkiye: Yapay Zekâ Özel Sayısı, 30(138), 33-37.
86. Salas‐Pilco, S. Z. (2020). The impact of AI and robotics on physical, social‐emotional and intellectual learning outcomes: An integrated analytical framework. British Journal of Educational Technology, 51(5), 1808-1825.
87. Santrock, J. W. (2023). Yaşam boyu gelişim. G. Yüksel (Ed.), Nobel Akademik Yayıncılık.
88. Sarkar, A., Jain, N., Imran, A., Rajagopal, S., Chowdhury, A. (2024). Descartes’ “Cogito Ergo Sum”: The revolution and critique of rationalism. Library Progress Internation, 44(3), 22782-22788.
89. Schultz, D. P. & Schultz, S. E. (2008). A history of modern psychology. Thomson Learning Academic Resource Center.
90. Sevinç, G. (2019). A Review on the Neo-Piagetian theory of cognitive development. Ankara University Journal of Faculty of Educational Sciences, 52(2), 611-631.
91. Seyrek, M., Yıldız, S., Emeksiz, H., Şahin, A., Türkmen, M. T. (2024). Öğretmenlerin eğitimde yapay zekâ kullanımına yönelik algıları. International Journal of Social and Humanities Sciences Research (JSHSR), 11(106), 845-856.
92. Shaheen, M. Y. (2021). Applications of artificial intelligence (AI) in healthcare: A review. Science Open Preprints.
93. Shapiro, L. (2019). Embodied cognition. Routledge.
94. Sheldon, R. (2025, Nisan 11). What is a sensor?. https://www.techtarget.com/whatis/definition/sensor
95. Siegler, R. S. (1996). Emerging minds: The process of change in children’s thinking. Oxford University Press.
96. Singh, B., Kumar, R., Singh, V. P. (2022). Reinforcement learning in robotic applications: A comprehensive survey. Artificial Intelligence Review, 55(2), 945-990.
97. Smith, E. E., Nolen-Hoeksema, S., Fredrickson, B. L., Loftus, G. R. (2003). Atkinson ve Hilgard psikolojiye giriş. (Çev: Ö. Öncül, D. Ferhatoğlu), Arkadaş Yayınevi.
98. Sönmez, O. (2019). Ulusal güvenlikte yapay zekâ kullanımı: ABD ve Çin örnekleri. (Yayımlanmamış yüksek lisans tezi). İstanbul: Bahçeşehir Üniversitesi Sosyal Bilimler Enstitüsü.
99. Sternberg, R. J. (2011). Individual differences in cognitive development. U. Goswami (Ed.), The Wiley-Blackwell Handbook of Childhood Cognitive Development içinde (749-774. ss.), Wiley Blackwell.
100. Stryker, C. & Holdsworth, J. (2024, Ağustos 11). What is NLP (natural language processing)?. https://www.ibm.com/think/topics/natural-language-processing
101. Şen Atiker, E. (2024). Görsel iletişim tarsarımında üretken yapay zekâ ve makine öğrenmesinin rolünü keşfetmek. Uluslararası İnsan ve Sanat Araştırmaları Dergisi, 9(4), 321-332.
102. Thelen, E. & Smith, L. B. (1994). A dynamic systems approach to the development of cognition and action. MIT Press.
103. Tok, Y. & Sağlam, M. (2023). Erken çocukluk dönemi problem çözme becerilerine yönelik yapılan eğitimsel uygulamaların etkililiği: Bir meta-analiz çalışması. Millî Eğitim Dergisi, 52(237), 9-32.
104. Tsantekidis, A., Passalis, N., Tefas, A. (2022). Recurrent neural networks. A. Iosifidis, A. Tefas (Ed.), Deep Learning for Robot Perception and Cognition içinde (101-115. ss.), Academic Press.
105. Tucker-Drob, E. M., Briley, D. A., Harden, K. P. (2013). Genetic and environmental influences on cognition across development and context. Current Directions in Psychological Science, 22(5).
106. Üner Kaya, A. (2024). Descartes’ın bilinçsiz makineleri: Hayvanlar. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (55), 70-80.
107. Ünveren Kapanadze, D. (2019). Vygostky’nin sosyo-kültürel ve bilişsel gelişim teorisi bağlamında Türkçe öğretiminin değerlendirilmesi. Süleyman Demirel Üniversitesi Fen-Edebiyat Fakültesi Sosyal Bilimler Dergisi, (47), 181-195. Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E. (2018). Deep learning for computer vision: A brief review. Computational Intelligence and Neuroscience, (1).
108. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
109. Wang, M. & Deng, W. (2021). Deep face recognition: A survey. Neurocomputing, (429), 215-244.
110. Watson, R. A. (2007). Cogito, ergo sum: The life of René Descartes. David R. Godine Publisher.
111. Xie, C., Ruan, M., Lin, P., Wang, Z., Lai, T., Xie, Y., Fu, S, Lu, H. (2022). Influence of artificial intelligence in education on adolescents’ social adaptability: A machine learning study. International Journal of Environmental Research and Public Health, 19(7890).
112. Yengin, D. & Bayrak, T. (2024). Yeni medya çalışmaları ve Yapay Zekâ-I. İKSAD Publishing House.
113. Yüksel, N., Börklü, H. R., Sezer, H. K., Canyurt, O. E. (2023). Review of artificial intelligence applications in engineering design perspective. Engineering Applications of Artificial Intelligence, 118(105697).
114. Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., Parmar, M. (2024). A review of convolutional neural networks in computer vision. Artificial Intelligence Review, 57(99).