Yapay Zeka Sistemleri ve İnsan İşbirliğinin Psikolojik, Sosyal ve Eğitsel Etkileri

Öz

Yapay zekâ (YZ) sistemlerinin ve YZ destekli robotların işbirliğine dayalı insan çalışma ortamlarına entegrasyonu, çok yönlü psikolojik, sosyal ve eğitsel etkilere yol açmıştır. Bu çalışma, PRISMA 2020 yönergeleri doğrultusunda yürütülen sistematik bir literatür derlemesi sunmakta ve 2019–2025 yılları arasında yayımlanan hakemli çalışmaları sentezlemektedir. İlgili çalışmalar, Scopus, Web of Science ve Google Scholar veri tabanlarında yapılan taramalarla belirlenmiştir. Derlemenin amacı, insan–YZ işbirliğinin ruh sağlığı, duygusal dayanıklılık ve bilişsel dinamikler üzerindeki etkilerini incelemektir. Disiplinlerarası literatüre dayanan analizde; teknostres, otomasyon yorgunluğu, bilişsel aşırı yüklenme, algoritmik kaygı, aşırı güven ve sorumluluk belirsizliği gibi temel olgular ele alınmaktadır. Bulgular, YZ’nin opaklığı, otomasyon yoğunluğu, antropomorfik ve duygusal tasarım özellikleri ile insan merkezli sistem tasarımının, kullanıcıların psikolojik tepkilerini şekillendirmede belirleyici rol oynadığını göstermektedir. İyi tasarlanmış YZ sistemleri bilişsel verimliliği ve iş tatminini artırma potansiyeline sahipken, insan faktörlerini göz ardı eden entegrasyonlar artan kaygı, tükenmişlik ve işten kopma ile ilişkilendirilmektedir. Bu bağlamda çalışma, sürdürülebilir insan–YZ işbirliğini desteklemek amacıyla şeffaflık, açıklanabilirlik ve kullanıcı otonomisini önceleyen insan merkezli bir yaklaşımın benimsenmesinin önemini vurgulamaktadır.

Anahtar Kelimeler

• İnsan-YZ işbirliği
• Teknostres
• Otomasyon yorgunluğu
• Bilişsel yük
• Eğitsel Yapay Zeka

Psychological, Social, and Educational Impacts of Artificial Intelligence Systems and Human Collaboration

Öz

The integration of artificial intelligence (AI) systems and AI-enabled robots into collaborative human work environments has led to innumerable psychological, social, and educational impacts. This study presents a systematic literature review, synthesising peer-reviewed research published between 2019-2025 in accordance with the PRISMA 2020 guidelines. Relevant studies were identified through searches of Scopus, Web of Science, and Google Scholar. The review aims to examine the mental health, emotional resilience, and cognitive dynamics of human–AI collaboration. Drawing on interdisciplinary literature, the analysis addresses key phenomena such as technostress, automation fatigue, cognitive overload, algorithmic anxiety, overreliance, and ambiguity of responsibility. The findings indicate that factors including AI opacity, the intensity of automation, anthropomorphic and affective design features, and human-centred system design play a decisive role in shaping users’ psychological responses. While well-designed AI systems demonstrate the potential to enhance cognitive efficiency and job satisfaction, poorly integrated implementations are associated with increased anxiety, burnout, and disengagement. Overall, the review highlights the importance of adopting a human-centred approach in AI applications, prioritising transparency, explainability, and user autonomy to support sustainable human–AI collaboration.

Anahtar Kelimeler

• Human–AI collaboration
• Technostress
• Automation fatigue
• Cognitive load
• Educational Artificial Intelligence.

Kaynakça

1. Abdelwanis, M., Alarafati, H. K., Tammam, M. M. S., & Simsekler, M. C. E. (2024). Exploring the risks of automation bias in healthcare artificial intelligence applications: A Bowtie analysis. Journal of Safety Science and Resilience, 5(4), 460-469. https://doi.org/10.1016/j.jnlssr.2024.06.001
2. Arefnezhad, S., Eichberger, A., & Koglbauer, I. V. (2022). Effects of automation and fatigue on drivers from various age groups. Safety, 8(2), 30. https://doi.org/10.3390/safety8020030
3. Becker, D., Rueda, D., Beese, F., Torres, B. S. G., Lafdili, M., Ahrens, K., ... & Wermter, S. (2023, August). The emotional dilemma: influence of a human-like robot on trust and cooperation. In 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) (pp. 1689-1696). IEEE. https://doi.org/10.1109/RO-MAN57019.2023.10309321
4. Bedué, P., & Fritzsche, A. (2022). Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption. Journal of Enterprise Information Management, 35(2), 530-549. https://doi.org/10.1108/JEIM-06-2020-0233
5. Brod, C. (1984). Technostress: The human cost of the computer revolution. Addison-Wesley.
6. Campagna, G., & Rehm, M. (2025). A Systematic Review of Trust Assessments in Human–Robot Interaction. ACM Transactions on Human-Robot Interaction, 14(2), 1-35. https://doi.org/10.1145/3706123
7. Califf, C. B., Sarker, S., & Sarker, S. (2020). The bright and dark sides of technostress: A mixed-methods study involving healthcare IT. MIS Quarterly, 44(2), 809-856. https://doi.org/10.25300/MISQ/2020/14818
8. Castel-Branco, G. D. L. V. (2024). Bridging Technology and Well-Being: Applying Machine Learning to Employee Mental Health: An Investigation Into the Integration, Impact, and Adoption of Intelligent Solutions (Master's thesis, Universidade NOVA de Lisboa (Portugal)).

9. Castelvecchi, D. (2016). Can we open the black box of AI? Nature News, 538(7623), 20.
10. Chang, P. C., Zhang, W., Cai, Q., & Guo, H. (2024). Does AI-driven technostress promote or hinder employees’ artificial intelligence adoption intention? A moderated mediation model of affective reactions and technical self-efficacy. Psychology Research and Behavior Management, 413-427. https://doi.org/10.2147/PRBM.S441444
11. Cheong, B. C. (2024). Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making. Frontiers in Human Dynamics, 6, 1421273. https://doi.org/10.3389/fhumd.2024.1421273
12. Chu, L., Chen, H. W., Cheng, P. Y., Ho, P., Weng, I. T., Yang, P. L., ... & Yeh, S. L. (2019). Identifying features that enhance older adults’ acceptance of robots: a mixed methods study. Gerontology, 65(4), 441-450. https://doi.org/10.1159/000494881
13. Chuang, Y. T., Chiang, H. L., & Lin, A. P. (2025). Insights from the Job Demands–Resources Model: AI's dual impact on employees’ work and life well-being. International Journal of Information Management, 83, 102887. https://doi.org/10.1016/j.ijinfomgt.2025.102887
14. Corrao, F., Nardelli, A., Renoux, J., & Recchiuto, C. T. (2025). EmoACT: a Framework to Embed Emotions into Artificial Agents Based on Affect Control Theory. arXiv preprint arXiv:2504.12125. https://doi.org/10.48550/arXiv.2504.12125
15. Daud, N. M. (2025). From innovation to stress: analyzing hybrid technology adoption and its role in technostress among students. International Journal of Educational Technology in Higher Education, 22(1), 31. https://doi.org/10.1186/s41239-025-00529-x
16. Dong, X., Wang, Z., & Han, S. (2025). Mitigating Learning Burnout Caused by Generative Artificial Intelligence Misuse in Higher Education: A Case Study in Programming Language Teaching. Informatics, 12(2), 51. https://doi.org/10.3390/informatics12020051
17. Duan, H., & Zhao, W. (2024). The effects of educational artificial intelligence-powered applications on teachers’ perceived autonomy, professional development for online teaching, and digital burnout. International Review of Research in Open and Distributed Learning, 25(3), 57-76. https://doi.org/10.19173/irrodl.v25i3.7659
18. Er, F. (2026). Digitalization and Technostress: Psychosocial Effects and Artificial Intelligence Supported Intervention Opportunities. Psikiyatride Güncel Yaklaşımlar, 18(2), 616-627.
19. Ergen, M. (2019). What is artificial intelligence? Technical considerations and future perception. Anatolian J. Cardiol, 22(2), 5-7.
20. Frenkenberg, A., & Hochman, G. (2025). It’s Scary to Use It, It’s Scary to Refuse It: The Psychological Dimensions of AI Adoption—Anxiety, Motives, and Dependency. Systems, 13(2), 82. https://doi.org/10.3390/systems13020082
21. Fügener, A., Grahl, J., Gupta, A., & Ketter, W. (2022). Cognitive challenges in human–artificial intelligence collaboration: Investigating the path toward productive delegation. Information Systems Research, 33(2), 678-696. https://doi.org/10.1287/isre.2021.1079
22. Gandhi, T. K., Classen, D., Sinsky, C. A., Rhew, D. C., Vande Garde, N., Roberts, A., & Federico, F. (2023). How can artificial intelligence decrease cognitive and work burden for front line practitioners? JAMIA Open, 6(3), ooad079. https://doi.org/10.1093/jamiaopen/ooad079
23. Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006
24. Haddaway, N. R., Collins, A. M., Coughlin, D., & Kirk, S. (2015). The role of Google Scholar in evidence reviews and its applicability to grey literature searching. PloS One, 10(9), e0138237. https://doi.org/10.1371/journal.pone.0138237
25. Hauptman, A. I., Schelble, B. G., Duan, W., Flathmann, C., & McNeese, N. J. (2024). Understanding the influence of AI autonomy on AI explainability levels in human-AI teams using a mixed methods approach. Cognition, Technology & Work, 26(3), 435-455. https://doi.org/10.1007/s10111-024-00765-7
26. Holbrook, C., Holman, D., Clingo, J., & Wagner, A. R. (2024). Overtrust in AI recommendations about whether or not to kill: Evidence from two human-robot interaction studies. Scientific Reports, 14(1), 19751. https://doi.org/10.1038/s41598-024-69771-z
27. Hor, T. S. C., Fong, L., Wynne, K., & Verhoeven, B. (2025). Easing the cognitive load of general practitioners: AI design principles for future-ready healthcare. Technovation, 142, 103208. https://doi.org/10.1016/j.technovation.2025.103208
28. Issa, H., Jaber, J., & Lakkis, H. (2024). Navigating AI unpredictability: Exploring technostress in AI-powered healthcare systems. Technological Forecasting and Social Change, 202, 123311. https://doi.org/10.1016/j.techfore.2024.123311
29. Kim, B. J., & Lee, J. (2024). The mental health implications of artificial intelligence adoption: the crucial role of self-efficacy. Humanities and Social Sciences Communications, 11(1), 1-15. https://doi.org/10.1057/s41599-024-04018-w
30. Kong, H., Yuan, Y., Baruch, Y., Bu, N., Jiang, X., & Wang, K. (2021). Influences of artificial intelligence (AI) awareness on career competency and job burnout. International Journal of Contemporary Hospitality Management, 33(2), 717-734. https://doi.org/10.1108/IJCHM-07-2020-0789
31. Kumar, S., Datta, S., Singh, V., Datta, D., Singh, S. K., & Sharma, R. (2024). Applications, challenges, and future directions of human-in-the-loop learning. IEEE Access, 12, 75735-75760. https://doi.org/10.1109/ACCESS.2024.3401547
32. Kupfer, C., Prassl, R., Fleiß, J., Malin, C., Thalmann, S., & Kubicek, B. (2023). Check the box! How to deal with automation bias in AI-based personnel selection. Frontiers in Psychology, 14, 1118723. https://doi.org/10.3389/fpsyg.2023.1118723
33. Lițan, D. E. (2025). The Impact of Technostress Generated by Artificial Intelligence on the Quality of Life: The Mediating Role of Positive and Negative Affect. Behavioral Sciences, 15(4), 552. https://doi.org/10.3390/bs15040552
34. Liu, X., Zhang, L., Lin, Z., & Guan, X. (2025). Robots make me feel more like a human! Investigating how employee-robot engagement reduces workplace depersonalization. Tourism Management, 109, 105149. https://doi.org/10.1016/j.tourman.2025.105149
35. Lu, W. (2024). Inevitable challenges of autonomy: ethical concerns in personalized algorithmic decision-making. Humanities and Social Sciences Communications, 11(1), 1-9. https://doi.org/10.1057/s41599-024-03864-y
36. Luxton, D. D. (2014). Artificial intelligence in psychological practice: Current and future applications and implications. Professional Psychology: Research and Practice, 45(5), 332. https://psycnet.apa.org/doi/10.1037/a0034559
37. Macnamara, B. N., Berber, I., Çavuşoğlu, M. C., Krupinski, E. A., Nallapareddy, N., Nelson, N. E., ... & Ray, S. (2024). Does using artificial intelligence assistance accelerate skill decay and hinder skill development without performers’ awareness? Cognitive Research: Principles and Implications, 9(1), 46. https://doi.org/10.1186/s41235-024-00572-8
38. Mahdavi, N., Tapak, L., Darvishi, E., Doosti-Irani, A., & Shafiee Motlagh, M. (2024). Unravelling the interplay between mental workload, occupational fatigue, physiological responses and cognitive performance in Office workers. Scientific Reports, 14(1), 17866. https://doi.org/10.1038/s41598-024-68889-4
39. Marmolejo-Ramos, F., Marrone, R., Korolkiewicz, M., Gabriel, F., Siemens, G., Joksimovic, S., ... & Tejada, J. (2025). Factors influencing trust in algorithmic decision-making: an indirect scenario-based experiment. Frontiers in Artificial Intelligence, 7, 1465605. https://doi.org/10.3389/frai.2024.1465605
40. Middleton, S. E., Letouzé, E., Hossaini, A., & Chapman, A. (2022). Trust, regulation, and human-in-the-loop AI: within the European region. Communications of the ACM, 65(4), 64-68. https://doi.org/10.1145/3511597
41. Neubauer, C. E., Matthews, G., & De Los Santos, E. P. (2023). Fatigue and secondary media impacts in the automated vehicle: a multidimensional state perspective. Safety, 9(1), 11. https://doi.org/10.3390/safety9010011
42. Onnasch, L., & Hildebrandt, C. L. (2021). Impact of anthropomorphic robot design on trust and attention in industrial human-robot interaction. ACM Transactions on Human-Robot Interaction (THRI), 11(1), 1-24. https://doi.org/10.1145/3472224
43. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372. https://doi.org/10.1136/bmj.n71
44. Pflügner, K. (2022). Technostress management at the workplace: A systematic literature review.
45. Sankaran, S., Zhang, C., Aarts, H., & Markopoulos, P. (2021). Exploring peoples' perception of autonomy and reactance in everyday ai interactions. Frontiers in Psychology, 12, 713074. https://doi.org/10.3389/fpsyg.2021.713074
46. Santoni de Sio, F., & Mecacci, G. (2021). Four responsibility gaps with artificial intelligence: Why they matter and how to address them. Philosophy & Technology, 34(4), 1057-1084. https://doi.org/10.1007/s13347-021-00450-x
47. Schreiter, T., Morillo-Mendez, L., Chadalavada, R. T., Rudenko, A., Billing, E. A., & Lilienthal, A. J. (2022). The effect of anthropomorphism on trust in an industrial human-robot interaction. arXiv preprint arXiv:2208.14637. https://doi.org/10.48550/arXiv.2208.14637
48. Shekar, S., Pataranutaporn, P., Sarabu, C., Cecchi, G. A., & Maes, P. (2025). People Overtrust AI-Generated Medical Advice despite Low Accuracy. NEJM AI, 2(6), AIoa2300015. https://doi.org/10.1056/AIoa2300015
49. Su, B., Jung, S., Lu, L., Wang, H., Qing, L., & Xu, X. (2024). Exploring the impact of human-robot interaction on workers' mental stress in collaborative assembly tasks. Applied Ergonomics, 116, 104224. https://doi.org/10.1016/j.apergo.2024.104224
50. Sun, F., Li, L., Meng, S., Teng, X., Payne, T. R., & Craig, P. (2025). Integrating emotional intelligence, memory architecture, and gestures to achieve empathetic humanoid robot interaction in an educational setting. Frontiers in Robotics and AI, 12, 1635419. https://doi.org/10.3389/frobt.2025.1635419
51. Ullrich, D., Butz, A., & Diefenbach, S. (2021). The development of overtrust: An empirical simulation and psychological analysis in the context of human–robot interaction. Frontiers in Robotics and AI, 8, 554578. https://doi.org/10.3389/frobt.2021.554578
52. Vignola, E. F., Baron, S., Abreu Plasencia, E., Hussein, M., & Cohen, N. (2023). Workers’ health under algorithmic management: emerging findings and urgent research questions. International Journal of Environmental Research and Public Health, 20(2), 1239. https://doi.org/10.3390/ijerph20021239
53. Wu, S., Liu, Y., Ruan, M., Chen, S., & Xie, X. Y. (2025). Human-generative AI collaboration enhances task performance but undermines human’s intrinsic motivation. Scientific Reports, 15(1), 15105. https://doi.org/10.1038/s41598-025-98385-2
54. Xia, M. (2023). Co-working with AI is a double-sword in technostress? An integrative review of human-AI collaboratio from a holistic process of technostress. In SHS Web of Conferences (Vol. 155, p. 03022). EDP Sciences. https://doi.org/10.1051/shsconf/202315503022
55. 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
56. Zheng, J., & Zhang, T. (2025). Association between AI awareness and emotional exhaustion: the serial mediation of job insecurity and work interference with family. Behavioral Sciences, 15(4), 401. https://doi.org/10.3390/bs15040401