Yapay Zekanın Tekinsiz Vadisi: İlkokul Öğrencilerinin Yapay Zekânın İnsan Özellikleri Atfetmesinden Kaynaklanan Korkuları

Öz

Bu nitel çalışma, ilkokul öğrencilerinin yapay zekâ (YZ) korkularını “Tekinsiz Vadi” teorisinin kavramsal merceğinden sistematik olarak incelemektedir. YZ teknolojileri modern eğitim ortamlarına giderek daha fazla entegre oldukça, bunların genç öğrenciler üzerindeki derin psikolojik etkilerini anlamak temel bir önem taşımaktadır. Titiz bir gömülü teori deseni kullanılarak, öğrencilerin altta yatan algılarını derinlemesine keşfetmek amacıyla 10-11 yaş arası sekiz öğrenciyle derinlemesine görüşmeler gerçekleştirilmiştir. Veri analizi, öğrencilerin YZ'ye ilişkin kaygılarının temel olarak üç belirgin temada yoğunlaştığını ortaya koymuştur: her şeyi bilme, kalıcı hafıza ve otonom gelişim. Bulgular, öğrencilerin YZ'yi yalnızca işlevsel bir teknolojik araç olarak değil, aksine kusursuz, insan benzeri bir rakip olarak algıladıklarını göstermektedir. Bu özgül algı; ciddi varoluşsal tehditler, mahremiyetin kalıcı olarak yitirilmesi ve teknoloji üzerindeki insan kontrolünün kaçınılmaz kaybına ilişkin derin kaygıları tetiklemektedir. Sonuç olarak çalışma, çocukların kendilerine özgü bir "Zihnin Tekinsiz Vadisi" deneyimledikleri; yaşadıkları yoğun endişenin YZ'nin fiziksel görünümünden ziyade hiper-gerçekçi bilişsel kapasitelerinden kaynaklandığı sonucuna varmaktadır. Bu karmaşık kaygıları etkili bir şekilde hafifletmek için çalışma, okullarda kapsamlı ve çok boyutlu YZ okuryazarlığı programlarının uygulanmasını güçlü bir şekilde önermektedir. Hedeflenen bu eğitimsel müdahaleler, YZ'nin istatistiksel doğasını gizemden arındırmaya, doğasında var olan sınırlılıkları vurgulamaya ve algoritmik çıktılar yerine insana özgü yetkinlikleri önceliklendiren insan odaklı pedagojik tasarımları teşvik etmeye odaklanmalıdır.

Anahtar Kelimeler

• YZ korkusu
• YZ kaygısı
• antropomorfizm
• insan benzeri özellikler
• tekinsiz vadi

Uncanny Valley of Artificial Intelligence: Primary School Students' Fears of AI Attributing Human Characteristics

Öz

This qualitative study systematically investigates the artificial intelligence (AI) fears of primary school students through the conceptual lens of the “Uncanny Valley” theory. As AI technologies become increasingly integrated into modern educational environments, understanding their profound psychological impact on young learners is fundamentally crucial. Employing a rigorous grounded theory design, in-depth interviews were conducted with eight students aged 10 to 11 to thoroughly explore their underlying perceptions. The data analysis revealed that students' anxieties regarding AI are primarily concentrated within three distinct themes: omniscience, permanent memory, and autonomous evolution. Findings indicate that students perceive AI not merely as a functional technological tool, but rather as an infallible, human-like competitor. This specific perception triggers profound anxieties concerning severe existential threats, the permanent loss of privacy, and an inevitable loss of human control over technology. Consequently, the study concludes that children experience a unique “Uncanny Valley of Mind”; their intense apprehension stems from the hyperrealistic cognitive capabilities of AI rather than its physical appearance. To effectively mitigate these complex anxieties, the study strongly recommends implementing comprehensive, multidimensional AI literacy programmes within schools. These targeted educational interventions must focus on demystifying the statistical nature of AI, highlighting its inherent limitations, and promoting human-centric pedagogical designs that prioritise uniquely human competencies over algorithmic outputs.

Anahtar Kelimeler

• Fear of AI
• AI anxiety
• anthropomorphism
• human-like features
• uncanny valley

Kaynakça

1. Airenti, G. (2018). The development of anthropomorphism in interaction: Intersubjectivity, imagination, and theory of mind. Frontiers in Psychology, 9, 2136. https://doi.org/10.3389/fpsyg.2018.02136
2. Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in Human Behavior, 114, 106548. https://doi.org/10.1016/j.chb.2020.106548
3. Carsten, S. B., & Wright, D. (2018). Ethics and privacy in AI and big data: Implementing responsible research and innovation. IEEE Security & Privacy, 16(3), 26–33. https://doi.org/10.1109/MSP.2018.2701164
4. Cengiz, S., & Peker, A. (2025). Generative artificial intelligence acceptance and artificial intelligence anxiety among university students: The sequential mediating role of attitudes toward artificial intelligence and literacy. Current Psychology. 44(9). 7991-8000.
5. Charmaz, K. (2006). Constructing grounded theory. Sage.
6. Clarke, R. (2019). Why the world wants control over artificial intelligence. Computer Law & Security Review, 35(4), 423–433.
7. D’Amato, A. (1977). Can/should computers replace judges? Georgia Law Review, 11(5), 1277–1302.
8. Deng T, Sun Y, Zhu X, Li N, Huang X, Du Q, Peng L, Peng K, Hu X. Whose values are artificial intelligence models aligning with? How culture shapes people’s normative expectations of artificial intelligence value. WSR. 2026;2(1):29–36. https://doi.org/10.54844/wsr.2025.1106

9. Doi, S. (2006). Technological development of driving support systems based on human behavioral characteristics. IATSS Research, 30(2), 20–21.
10. Fast, E., & Horvitz, E. (2017). Long-term trends in the public perception of artificial intelligence. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1), 963–969. https://doi.org/10.1609/aaai.v31i1.10635
11. Freud, S. (1919). Das unheimliche. In Gesammelte Werke (Vol. 12, pp. 229–268).
12. Freund, Y., & Schapire, R. E. (1999). Large margin classification using the perceptron algorithm. Machine Learning, 37(3), 277–296. https://doi.org/10.1023/A:1007662407062
13. Future of Life Institute. (2015). Autonomous weapons: An open letter from AI & robotics researchers. https://futureoflife.org/open-letter-autonomous-weapons/
14. Geller, T. (2008). Overcoming the uncanny valley. IEEE Computer Graphics and Applications, 28(4), 11–17.
15. Granter, S. R., Beck, A. H., & Papke, D. J. (2017). Deep learning, and the future of the human microscopist. Archives of Pathology & Laboratory Medicine, 141(5), 619–621.
16. Gubrium, J. F., & Holstein, J. A. (2012). The SAGE handbook of interview research: The complexity of the craft. Sage.
17. Hofstede, G. (2011). Dimensionalizing cultures: The Hofstede model in context. Online Readings in Psychology and Culture, 2(1), 8. https://doi.org/10.9707/2307-0919.1014
18. Johnson, D. G., & Verdicchio, M. (2017). AI anxiety. Journal of the Association for Information Science and Technology, 68(9), 2267–2270.
19. Kuriscak, E., Marsalek, P., Stroffek, J., & Tóth, P. G. (2015). Biological context of Hebb learning in artificial neural networks: A review. Neurocomputing, 152, 27–35.
20. Lee, J. Y. (2020). Understanding the role of cultural context and user interaction in artificial intelligence based systems. Journal of Global Information Technology Management, 23(3), 167–171. https://doi.org/10.1080/1097198X.2020.1794131
21. Lindsay, R. K., Buchanan, B. G., Feigenbaum, E. A., & Lederberg, J. (1993). DENDRAL: A case study of the first expert system for scientific hypothesis formation. Artificial Intelligence, 61(2), 209–261.
22. Marcoulides, G. A. (1989). Measuring computer anxiety: The Computer Anxiety Scale. Educational and Psychological Measurement, 49(3), 733–739.
23. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1995). A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine, 27(4), 12–14.
24. McCloud, S. (1993). Understanding comics: The invisible art. Kitchen Sink Press.
25. McKinsey Global Institute. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. https://www.mckinsey.com/global-themes/future-of-work/jobs-lost-jobs-gained-workforce-transitions
26. Mohri, M., Afshin, R., & Ameet, T. (2018). Foundations of machine learning. MIT Press.
27. Mori, M. (1970). The uncanny valley. Energy, 7(4), 33–35.
28. Muehlhauser, N., & Bostrom, L. (2014). Why we need friendly AI. Thinking, 13(36), 41–47.
29. Müller, V. C., & Bostrom, N. (2014). Future progress in artificial intelligence. AI Matters, 1(1), 9–11.
30. Newell, A., Shaw, J. C., & Simon, H. A. (1959). Report on a general problem-solving program. IFIP Congress, 256, 64.
31. Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041.
32. Oke, S. A. (2008). A literature review on artificial intelligence. International Journal of Information Management Sciences, 19(4), 535–570.
33. Piaget, J. (1929). The child's conception of the world. Harcourt, Brace and Company.
34. Piaget, J. (1972). Intellectual evolution from adolescence to adulthood. Human Development, 15(1), 1–12. https://doi.org/10.1159/000271225
35. Powell, A. L. (2013). Computer anxiety: Comparison of research from the 1990s and 2000s. Computers in Human Behavior, 29(6), 2337–2381.
36. Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., & Bonnefon, J. F. (2019). Machine behaviour. Nature, 568(7753), 477–486. https://doi.org/10.1038/s41586-019-1138-y
37. Rosen, L. D., Sears, D. C., & Weil, M. M. (1987). Computerphobia. Behavior Research Methods, Instruments, & Computers, 19(2), 167–179.
38. Russell, S., & Norvig, P. (2010). Artificial intelligence: A modern approach (3rd ed.). Prentice Hall.
39. Samuel, A. L. (2000). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 44(1–2), 206–226.
40. Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146(83):102551. https://doi.org/10.1016/j.ijhcs.2020.102551
41. Tuomi, I. (2018). The impact of artificial intelligence on learning, teaching, and education. Joint Research Centre. https://doi.org/10.2760/12297
42. Turkle, S. (2005). The second self: Computers and the human spirit (20th anniversary ed.). MIT Press.
43. Unterrainer, J. M., Kaller, C. P., Halsband, U., & Rahm, B. (2006). Planning abilities and chess: A comparison of chess and non-chess players on the Tower of London task. British Journal of Psychology, 97(Pt 3), 299–311.
44. Véliz, C. (2020). Privacy is power: Why and how you should take back control of your data. Bantam Press.
45. Waltz, D. L. (2006). Evolution, sociobiology, and the future of artificial intelligence. IEEE Intelligent Systems, 21(3), 66–69.
46. Wang, B., Rau, P. L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behaviour and Information Technology, 42(9), 1324–1337.
47. Zhang, L., & Zhang, B. A. (1999). Geometrical representation of McCulloch-Pitts neural model and its applications. IEEE Transactions on Neural Networks, 10(4), 925–929.
48. Zhou, J. (2024). The impact of AI use on college students' academic self-efficacy: Exploring the mediating role of anxiety. Lecture Notes in Education Psychology and Public Media. https://doi.org/10.54254/2753-7048/2024.17925
49. Zhou, Z. (2016). Machine learning. Tsinghua University Press.