Artificial Intelligence Myths: Prevalence Among Turkish University Students and Comparative Analysis of ChatGPT Responses

Yeliz Tunga; Ali Geriş; Berkan Çelik

doi:10.17275/per.25.45.12.3

EN

Artificial Intelligence Myths: Prevalence Among Turkish University Students and Comparative Analysis of ChatGPT Responses

Abstract

Artificial intelligence (AI) has rapidly emerged as a transformative technology across various disciplines. However, its widespread adoption is accompanied by numerous myths, which are fueled by limited public understanding and can significantly shape how individuals perceive and interact with AI, often leading to negative consequences such as misunderstanding, fear, or resistance. Despite the importance of addressing these myths, research on the prevalence of such beliefs remains insufficient, particularly in the Turkish context. This study aims to determine the prevalence of AI myths among Turkish university students, investigate the factors influencing the adoption of these myths, and compare student perceptions with ChatGPT's responses to the same myths. The study analyzed survey data from 288 students (102 males, 35.4%, and 186 females, 64.6%) using an AI-myth survey consisting of 18 items. Both descriptive and inferential analyses were conducted to determine the prevalence of AI myths and investigate how factors such as academic background, gender, AI-related training, and media consumption influence the adoption of these myths. A comparative analysis was also performed between student responses and ChatGPT’s reactions to these myths. Analysis showed that certain AI myths are particularly prevalent among students. Students from technical disciplines demonstrated a greater ability to identify these myths, while prior AI training and media consumption had minimal impact. ChatGPT’s responses highlighted areas where better communication about AI is needed. The findings suggest that improving AI literacy and dispelling myths are essential for preparing students for more informed engagement with AI technologies.

Keywords

artificial intelligence, generative ai, myths, misconceptions

References

Aravind, M. (2025). Debunking myths about Enhanced Recovery After Surgery (ERAS) using Generative Artificial Intelligence. Clinical Nutrition ESPEN, 65, 530-531. https://doi.org/10.1016/j.clnesp.2024.10.104
Atkinson, R. D. (2016). 'It's going to kill us!' and other myths about the future of artificial intelligence. Information Technology & Innovation Foundation. https://ssrn.com/abstract=3066182
Berliner, D. C., & Glass, G. V. (Eds.). (2014). 50 myths and lies that threaten America's public schools: The real crisis in education. Teachers College Press.
Bewersdorff, A., Zhai, X., Roberts, J., & Nerdel, C. (2023). Myths, mis-and preconceptions of artificial intelligence: A review of the literature. Computers and Education: Artificial Intelligence, 4, 100143. https://doi.org/10.1016/j.caeai.2023.100143
Bragazzi, N. L., & Garbarino, S. (2024). Assessing the accuracy of generative conversational artificial intelligence in debunking sleep health myths: Mixed methods comparative study with expert analysis. JMIR Formative Research, 8(1), e55762. https://formative.jmir.org/2024/1/e55762
Cave, S., Coughlan, K., & Dihal, K. (2019). "Scary robots": Examining public responses to AI. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 331-337). https://doi.org/10.1145/3306618.3314232
Chi, M. T. H. (2009). Three types of conceptual change: Belief revision, mental model transformation, and categorical shift. In S. Vosniadou (Ed.), International handbook of research on conceptual change (pp. 61-82). Routledge.
Chowdhary, K.R. (2020). Introducing artificial intelligence. In Fundamentals of artificial intelligence. Springer. https://doi.org/10.1007/978-81-322-3972-7_1
Cook, J., Ecker, U., & Lewandowsky, S. (2015). Misinformation and how to correct it. In R.A. Scott & S.M. Kosslyn (Eds.), Emerging trends in the social and behavioral sciences (pp. 1-17). https://doi.org/10.1002/9781118900772.etrds0222
Davis, L. L. (1992). Instrument review: Getting the most from a panel of experts. Applied Nursing Research, 5(4), 194-197. https://doi.org/10.1016/S0897-1897(05)80008-4

De Bruyckere, P., Kirschner, P. A., & Hulshof, C. D. (2015). Urban myths about learning and education. Academic Press.
Duymaz, Y., & Şahin, Ş. (2023). From ancient mythology to modern technology: The historical evolution of artificial intelligence. European Journal of Therapeutics, 29(4), 964–965. https://doi.org/10.58600/eurjther1895
Ecker, U. K. H., Swire, B., & Lewandowsky, S. (2014). Correcting misinformation—A challenge for education and cognitive science. In D. N. Rapp & J. L. G. Braasch (Eds.), Processing inaccurate information: Theoretical and applied perspectives from cognitive science and the educational sciences (pp. 13–37). The MIT Press.
Ecker, U. K., Lewandowsky, S., Cook, J., Schmid, P., Fazio, L. K., Brashier, N., ... & Amazeen, M. A. (2022). The psychological drivers of misinformation belief and its resistance to correction. Nature Reviews Psychology, 1(1), 13-29. https://doi.org/10.1038/s44159-021-00006-y
Emmert-Streib, F., Yli-Harja, O., & Dehmer, M. (2020). Artificial intelligence: A clarification of misconceptions, myths and desired Status. Frontiers in Artificial Intelligence, 3(524339). https://doi.org/10.3389/frai.2020.524339
Galanos, V., Bennett, S., Aylett, R., & Hemment, D. (2023). AI myths debunked: Unpacking six common misconceptions. The New Real Magazine, 1(1), 79–83. https://doi.org/10.2218/newreal.9267
Gardner, R. M., & Brown, D. L. (2013). A test of contemporary misconceptions in psychology. Learning and Individual Differences, 24, 211–215. https://doi.org/10.1016/j.lindif.2012.12.008
Giuliano, R. (2020). Echoes of myth and magic in the language of artificial intelligence. AI & Society, 35, 1009 - 1024. https://doi.org/10.1007/s00146-020-00966-4
Giray, L. (2024). Ten myths about artificial intelligence in education. Higher Learning Research Communications, 14(2), 1–12. https://files.eric.ed.gov/fulltext/EJ1454533.pdf
Guerrero, S., Sebastián-Enesco, C., Perez, N., & Enesco, I. (2019). Myths in science: Children trust but do not retain their teacher's information. Journal of Applied Developmental Psychology, 62, 116-121. https://doi.org/10.1016/j.appdev.2019.02.007
Heffernan, T. (2020). The dangers of mystifying artificial intelligence and robotics. Toronto Journal of Theology, 36, 93-95. https://doi.org/10.3138/tjt-2020-0029
Hirsch-Kreinsen, H., & Krokowski, T. (2024). Promises and myths of artificial intelligence. Weizenbaum Journal of the Digital Society, 4(1). https://doi.org/10.34669/WI.WJDS/4.1.6
JASP Team (2024). JASP (Version 0.18.3) [Computer software]. https://jasp-stats.org/download/
Kaplan-Rakowski, R., Grotewold, K., Hartwick, P., & Papin, K. (2023). Generative AI and teachers’ perspectives on its implementation in education. Journal of Interactive Learning Research, 34(2), 313-338. https://www.learntechlib.org/primary/p/222363/
Larson, E. J. (2021). The myth of artificial intelligence: Why computers can't think the way we do. Harvard University Press. https://doi.org/10.4159/9780674259935
Leufer, D. (2020). Why we need to bust some myths about AI. Patterns, 1(7). https://doi.org/10.1016/j.patter.2020.100124
Leufer, D., Steinbrück, A., & Liptakova, Z. (2020). AI myths. https://www.aimyths.org/
Lewandowsky, S., Ecker, U. K., Seifert, C. M., Schwarz, N., & Cook, J. (2012). Misinformation and its correction: Continued influence and successful debiasing. Psychological Science in the Public Interest, 13(3), 106-131. https://doi.org/10.1177/1529100612451018
Lewandowsky, S., Cook, J., Ecker, U. K. H., Albarracín, D., Amazeen, M. A., Kendeou, P., Lombardi, D.,Newman, E. J., Pennycook, G., Porter, E. Rand, D. G., Rapp, D. N., Reifler, J., Roozenbeek, J., Schmid, P.,Seifert, C. M., Sinatra, G. M., Swire-Thompson, B., van der Linden, S., Vraga, E. K., Wood, T. J., & Zaragoza,M. S. (2020). The debunking handbook 2020. https://digitalcommons.unl.edu/scholcom/245/
Lilienfeld, S. O., Lynn, S. J., Ruscio, J., & Beyerstein, B. L. (2010). 50 great myths of popular psychology: Shattering widespread misconceptions about human behavior. Wiley Blackwell.
Loomba, S., De Figueiredo, A., Piatek, S. J., De Graaf, K., & Larson, H. J. (2021). Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nature Human Behaviour, 5(3), 337-348. https://doi.org/10.1038/s41562-021-01056-1
Natale, S., & Ballatore, A. (2020). Imagining the thinking machine: Technological myths and the rise of artificial intelligence. Convergence: The International Journal of Research into New Media Technologies, 26(1), 3-18. https://doi.org/10.1177/1354856517715164
Omisakin, O. A., Ulrich-Schad, J. D., Hunt, A., Givens, J. E., & Beacham, M. (2023). Belief in vaccine myths and vaccine uptake in Utah during the COVID-19 pandemic. Preventive Medicine Reports, 36, 102390. https://doi.org/10.1016/j.pmedr.2023.102390
Organisation for Economic Co-operation and Development (OECD) (2002). Understanding the brain towards a new learning science. OECD Publishing, Paris. https://doi.org/10.1787/9789264174986-en.
O’Regan, G. (2016). History of artificial intelligence. In Introduction to the history of computing. Springer. https://link.springer.com/chapter/10.1007/978-3-319-33138-6_19
Pasquinelli, E. (2012). Neuromyths: Why do they exist and persist? Mind, Brain, and Education, 6(2), 89–96. https://doi.org/10.1111/j.1751-228X.2012.01141.x
Pesapane, F., Tantrige, P., Patella, F., Biondetti, P., Nicosia, L., Ianniello, A., Rossi, U., Carrafiello, G., & Ierardi, A. (2020). Myths and facts about artificial intelligence: Why machine- and deep-learning will not replace interventional radiologists. Medical Oncology, 37, 1-9. https://doi.org/10.1007/s12032-020-01368-8
Polit, D. F., Beck, C. T., & Owen, S. V. (2007). Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Research in Nursing & Health, 30(4), 459-467. https://doi.org/10.1002/nur.20199
Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. Science Education, 66(2), 211–227. https://doi.org/10.1002/sce.3730660207
Rogers, J., & Cheung, A. (2020). Pre-service teacher education may perpetuate myths about teaching and learning. Journal of Education for Teaching, 46(3), 417-420. https://doi.org/10.1080/02607476.2020.1766835
The Council of Higher Education (HEC). (2024). Yapay zeka, dijitalleşme ve büyük veri alanlarında yeni programlar [New programs in the fields of artificial intelligence, digitalization, and big data]. https://www.yok.gov.tr/Sayfalar/Haberler/2024/yapay-zeka-dijitallesme-buyuk-veri-yeni-programlar.aspx
Torrijos-Muelas, M., González-Víllora, S. ve Bodoque-Osma, A. R. (2021). The persistence of neuromyths in the educational settings: A systematic review. Frontiers in Psychology, 11, 591923. https://doi.org/10.3389/fpsyg.2020.591923
Tunga, Y., & Çağıltay, K. (2023). Myths or facts: Prevalence and predictors of neuromyths among Turkish teachers. Education & Science, 48(216), 229-246. https://doi.org./10.15390/EB.2023.12089
Ullah, I., Khan, K. S., Tahir, M. J., Ahmed, A., & Harapan, H. (2021). Myths and conspiracy theories on vaccines and COVID-19: Potential effect on global vaccine refusals. Vacunas, 22(2), 93-97. https://doi.org/10.1016/j.vacun.2021.01.001
Velander, J., Taiye, M. A., Otero, N., & Milrad, M. (2024). Artificial Intelligence in K-12 Education: Eliciting and reflecting on Swedish teachers’ understanding of AI and its implications for teaching & learning. Education and Information Technologies, 29, 4085–4105. https://doi.org/10.1007/s10639-023-11990-4
Vosniadou, S. (2013). Conceptual change in learning and instruction: The framework theory approach. In S. Vosniadou (Ed.), International handbook of research on conceptual change (2nd ed.) (pp. 11-30). Routledge.

Details

Primary Language

English

Subjects

Higher Education Studies (Other), Specialist Studies in Education (Other)

Journal Section

Research Article

Authors

Yeliz Tunga ^*
0000-0002-4046-4198
Türkiye

Ali Geriş
0000-0003-2136-5490
Türkiye

Berkan Çelik
0000-0002-7068-8918
Türkiye

Early Pub Date

May 7, 2025

Publication Date

May 1, 2025

Submission Date

November 19, 2024

Acceptance Date

March 3, 2025

Published in Issue

Year 2025 Volume: 12 Number: 3

DOI

https://doi.org/10.17275/per.25.45.12.3

IZ

https://izlik.org/JA27XZ65DW

APA

Tunga, Y., Geriş, A., & Çelik, B. (2025). Artificial Intelligence Myths: Prevalence Among Turkish University Students and Comparative Analysis of ChatGPT Responses. Participatory Educational Research, 12(3), 292-311. https://doi.org/10.17275/per.25.45.12.3

AMA

1.Tunga Y, Geriş A, Çelik B. Artificial Intelligence Myths: Prevalence Among Turkish University Students and Comparative Analysis of ChatGPT Responses. PER. 2025;12(3):292-311. doi:10.17275/per.25.45.12.3

Chicago

Tunga, Yeliz, Ali Geriş, and Berkan Çelik. 2025. “Artificial Intelligence Myths: Prevalence Among Turkish University Students and Comparative Analysis of ChatGPT Responses”. Participatory Educational Research 12 (3): 292-311. https://doi.org/10.17275/per.25.45.12.3.

EndNote

Tunga Y, Geriş A, Çelik B (May 1, 2025) Artificial Intelligence Myths: Prevalence Among Turkish University Students and Comparative Analysis of ChatGPT Responses. Participatory Educational Research 12 3 292–311.

IEEE

[1]Y. Tunga, A. Geriş, and B. Çelik, “Artificial Intelligence Myths: Prevalence Among Turkish University Students and Comparative Analysis of ChatGPT Responses”, PER, vol. 12, no. 3, pp. 292–311, May 2025, doi: 10.17275/per.25.45.12.3.

ISNAD

Tunga, Yeliz - Geriş, Ali - Çelik, Berkan. “Artificial Intelligence Myths: Prevalence Among Turkish University Students and Comparative Analysis of ChatGPT Responses”. Participatory Educational Research 12/3 (May 1, 2025): 292-311. https://doi.org/10.17275/per.25.45.12.3.

JAMA

1.Tunga Y, Geriş A, Çelik B. Artificial Intelligence Myths: Prevalence Among Turkish University Students and Comparative Analysis of ChatGPT Responses. PER. 2025;12:292–311.

MLA

Tunga, Yeliz, et al. “Artificial Intelligence Myths: Prevalence Among Turkish University Students and Comparative Analysis of ChatGPT Responses”. Participatory Educational Research, vol. 12, no. 3, May 2025, pp. 292-11, doi:10.17275/per.25.45.12.3.

Vancouver

1.Yeliz Tunga, Ali Geriş, Berkan Çelik. Artificial Intelligence Myths: Prevalence Among Turkish University Students and Comparative Analysis of ChatGPT Responses. PER. 2025 May 1;12(3):292-311. doi:10.17275/per.25.45.12.3