Validity challenge in GenAI models: Evaluating the validity of content generated by text-to-image models in the context of social studies education
Okan Yetişensoy 1 *
More Detail
1 Bayburt University, Faculty of Education, Türkiye
* Corresponding Author

Abstract

Generative artificial intelligence (GenAI) models have led to many positive changes in educational settings; however, the validity of the content they produce remains a significant topic of academic discussion. This research aims to determine the validity of content produced by text-to-image models within the context of social studies education. For this purpose, different curricular outcomes from the social studies curriculum implemented in Türkiye were identified, and image content related to them was generated using DALL-E 3. The validity of this content was assessed within a panel consisting of social studies teachers. Quantitative analyses revealed inconsistencies, showing that some images are sufficient in terms of scientific accuracy as well as socio-cultural and geographical relevance, while others are not. Intraclass correlation coefficient analyses demonstrated that there was significantly moderate agreement among the panelists regarding these evaluations, indicating that the assessments are reliable. Qualitative analysis, on the other hand, revealed that panelists evaluated some images positively in terms of validity, noting that the content produced by these models has significant potential to enhance the learning of relevant outcomes of social studies. However, it was indicated that certain images exhibit prominent algorithmic biases, provide misleading or incorrect information, include details that could be characterized as hallucinations, and could potentially negatively impact the learning process. Additionally, it was determined that some images, in an attempt to highlight a particular cultural element, displays representations that are either disconnected from reality or overemphasized, which could be termed “Socio-cultural algorithmic exaggerations”. At this point, it is considered essential for all educators, including social studies teachers, to develop a critical perspective on the content created by GenAI models and to use this content only after thorough evaluation.

Keywords

References

  • Adams, L.C., Busch, F., Truhn, D., Makowski, M.R., Aerts, H.J.W.L., & Bressem, K.K. (2023). What does DALL-E 2 know about radiology? Journal of Medical Internet Research, 25, 43110. https://doi.org/10.2196/43110
  • Ajmera, P., Nischal, N., Ariyaratne, S. Botchu, B., Bhamidipaty, K.D.P., Iyengar, K.P., Ajmera, S.R., Jenko, N. & Botchu, R. (2024). Validity of ChatGPT-generated musculoskeletal images. Skeletal Radiology, 53, 1583-1593. https://doi.org/10.1007/s00256-024-04638-y
  • Alikhani, M., Khalid, B., & Stone, M. (2023). Image-text coherence and its implications for multimodal AI. Frontiers in Artificial Intelligence 6, 1048874. https://doi.org/10.3389/frai.2023.1048874
  • Bahani, M., El Ouaazizi, A., Maalmi, K. (2023). The effectiveness of T5, GPT-2, and BERT on text-to-image generation task. Pattern Recognition Letters, 173, 57-63. https://doi.org/10.1016/j.patrec.2023.08.001
  • Berson, I. R. & Berson, M. J. (2023). The democratization of AI and its transformative potential in social studies education. Social Education, 87(2), 114-118.
  • Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Costello, E., Mason, J., Stracke, C., Romero- Hall, E., Koutropoulos, A., Toquero, C. M., Singh, L., Tlili, A., Lee, K., Nichols, M., Ossiannilsson, E., Brown, M., Irvine, V., Raffaghelli, J. E., Santos-Hermosa, G., Farrell, O., Adam, T., Thong, Y. L., Sani-Bozkurt, S., Sharma, R. C., Hrastinski, S., & Jandrić, P. (2023). Speculative futures on ChatGPT and Generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53-130. https://doi.org/10.5281/zenodo.7636568
  • Bozkurt, A., & Sharma, R. C. (2024). Are we facing an algorithmic renaissance or apocalypse? Generative AI, ChatBots, and emerging human-machine interaction in the educational landscape. Asian Journal of Distance Education, 19(1), 1-12. https://doi.org/10.5281/zenodo.10791959
  • Buzzaccarini, G., Degliuomini, R.S., Borin, M., Fidanza, A., Salmeri, N., Schiraldi, L., Di Summa, P.G., Vercesi, F., Vanni, V.S., Candiani, M., & Pagliardini, L. (2024). The promise and pitfalls of AI-generated anatomical images: Evaluating Midjourney for aesthetic surgery applications. Aesthetic Plastic Surgery, 48, 1874-1883. https://doi.org/10.1007/s00266-023-03826-w
  • Califano, G., & Spence, C. (2024). Assessing the visual appeal of real/AI-generated food images. Food Quality and Preference, 116, 105149. https://doi.org/10.1016/j.foodqual.2024.
  • Chan, C.K.Y., & Hu, W. (2023). Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(43), 1-18. https://doi.org/10.1186/s41239-023-00411-8
  • Chang, C.H., & Kidman, G. (2023). The rise of generative artificial intelligence (AI) language models- challenges and opportunities for geographical and environmental education. International Research in Geographical and Environmental Education, 32(2), 85-89. https://doi.org/10.1080/10382046.2023.2194036
  • Chauncey, S.C., & McKenna, H.P. (2023). A framework and exemplars for ethical and responsible use of AI Chatbot technology to support teaching and learning. Computers and Education: Artificial Intelligence, 5, 100182. https://doi.org/10.1016/j.caeai.2023.100182
  • Chelli, M., Descamps, J., Lavoué, V., Trojani, C., Azar, M., Deckert, M., Raynier, J.L., Clowez, G., Boileau, P., & Ruetsch Chelli, C., (2024). Hallucination rates and reference accuracy of ChatGPT and Bard for systematic reviews: Comparative analysis. Journal of Medical Internet Research, 22(26), e53164. https://doi.org/10.2196/53164
  • Chen, M., Liu, Y., Yi, J., Xu, C., Lai, Q., Wang, H., Ho, T., Xu, Q. (2024). Evaluating text-to-image generative models: An empirical study on human image synthesis (Publication no: 2403.05125). Arxiv. https://arxiv.org/pdf/2403.05125
  • Chen, Y., Zhang, X., & Hu, L. (2024). A progressive prompt-based image-generative AI approach to promoting students’ achievement and perceptions in learning ancient Chinese poetry. Educational Technology & Society, 27(2), 284-305. https://doi.org/10.30191/ETS.202404_27(2).TP01
  • Cheong, M., Abedin, E., Ferreira, M., Reimann, R., Chalson, S., Robinson, P., Byrne, J., Ruppanner, L., Alfano, M., & Klein, C. (2024). Investigating gender and racial biases in DALL-E mini images. ACM Journal on Responsible Computing, 1(2), 1-20. https://doi.org/10.1145/3649883
  • Chiang, Y. V., Chang, M., & Chen, N.S. (2024). Can generative AI help realize the shift from an outcome-oriented to a process-outcome-balanced educational practice? Educational Technology & Society, 27(2), 347-385. https://doi.org/10.30191/ETS.202404_27(2).TP04
  • Chiu, T.K.F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, 100197. https://doi.org/10.1016/j.caeai.2023.100197
  • Creswell, J.H., & Creswell, J.D. (2018). Research design: Qualitative, quantitative and mixed methods approaches. Sage.
  • Dehghani, H., & Mashhadi, A. (2024). A. Exploring Iranian english as a foreign language teachers’ acceptance of ChatGPT in english language teaching: Extending the technology acceptance model. Education and Information Technologies, 29, 19813-19834. https://doi.org/10.1007/s10639-024-12660-9
  • Dictionary Cambridge (2024). Validity. Author. https://dictionary.cambridge.org
  • Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Wright, R., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., & Buhalis, D., Carter, L., & Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
  • Elmas, R., Adiguzel-Ulutas, M. & Yılmaz, M. (2024). Examining ChatGPT’s validity as a source for scientific inquiry and its misconceptions regarding cell energy metabolism. Education and Information Technologies, 29, 25427-25456. https://doi.org/10.1007/s10639-024-12749-1
  • Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107-115. https://doi.org/10.1111/j.1365-2648.2007.04569.x
  • Fareed, M.W., Bou, Nassif, A., & Nofal, E. (2024). Exploring the potentials of artificial intelligence image generators for educating the history of architecture. Heritage, 7, 1727-1753. https://doi.org/10.3390/heritage7030081
  • Friedrich, F., Brack, M., Struppek, L., Hintersdorf, D., Schramowski, P., Luccioni, S., & Kersting, K. (2024). Auditing and instructing text-to-image generation models on fairness. AI and Ethics, 5, 2103-2123. https://doi.org/10.1007/s43681-024-00531-5
  • Ghosh, S. Venkit, P.N., Gautam, S., Wilson, S., & Caliskan, A. (2024). Do generative AI models output harm while representing non-western cultures: Evidence from a community-centered approach (Publication no: 2407.14779). Arxiv. https://arxiv.org/abs/2407.14779
  • Ghosh, S., & Caliskan, A. (2023). Person’ == Light-skinned, western man, and sexualization of women of color: Stereotypes in Stable Diffusion. In H. Bouamor, J. Pino, & K. Bali (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 6971-6985). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.465
  • Gill, S.S., Xu, M., Patros, P., Wu, H., Kaur, R., Kaur, K., Fuller, S., Singh, M., Arora, P., Parlikad, A.K., Stankovski, V., Abraham, A., Ghosh, S.K., Lutfiyya, H., Kanhere, S.S., Bahsoon, R., Rana, O., Dustdar, S, Sakellariou, R., Uhlig, S., & Buyya, R. (2024). Transformative effects of ChatGPT on modern education: Emerging era of AI chatbots. Internet of Things and Cyber-Physical Systems, 4, 19-23. https://doi.org/10.1016/j.iotcps.2023.06.002
  • Hsu, Y.C., & Ching, Y.H.(2023). Generative artificial intelligence in education, part one: The dynamic frontier. TechTrends, 67, 603-607. https://doi.org/10.1007/s11528-023-00863-9
  • Hwang, G.J., & Chen, N.S. (2023). Editorial position paper: Exploring the potential of generative artificial intelligence in education: Applications, challenges, and future research directions. Educational Technology & Society, 26(2), 1-18. https://doi.org/10.30191/ETS.202304_26(2).0014
  • Jiang, Y., Xie, L., Lin, G., & Mo, F. (2024). Widen the debate: What is the academic community’s perception on ChatGPT?. Education and Information Technologies, 29, 20181-20200. https://doi.org/10.1007/s10639-024-12677-0
  • Johnson, D., Goodman, R., Patrinely, J., Stone, C., Zimmerman, E., Donald, R., Chang, S., Berkowitz, S., Finn, A., Jahangir, E., Scoville, E., Reese, T., Friedman, D., Bastarache, J., Van der Heijden, Y., Wright, J., Carter, N., Alexander, M., Choe, J., Chastain, C., Zic, J., Horst, S., Turker, I., Agarwal, R., Osmundson, E., Idrees, K., Kieman, C., Padmanabhan, C., Bailey, C., Schlegel, C., Chambless, L., Gibson, M., Osterman, T., & Wheless, L. (2023). Assessing the accuracy and reliability of AI-generated medical responses: an evaluation of the Chat-GPT model (Publication no: PMC10002821). PubMed Central. https://doi.org/10.21203/rs.3.rs-2566942/v1
  • Karaduman, H., & Yetişensoy, O. (2023). Sosyal bilgiler ve yapay zekâ: Eğitimde ideal bir sentez mi? [Social studies and artificial intelligence: An ideal synthesis in education?]. In A. F. Ersoy & H. Karaduman (Eds.), Sosyal bilgilerde güncel okumalar 3 [Contemporary readings in social studies 3] (pp. 249–277). Eğiten Kitap.
  • Kee, T., Kuys, B., & King, R. (2024). Generative artificial intelligence to enhance architecture education to develop digital literacy and holistic competency. Jarina, 3(1), 24-41. https://doi.org/10.24002/jarina.v3i1.8347
  • Kim, J., & Lee, Y. (2024). Accuracy evaluation of tree images created using generative artificial intelligence. Journal of Digital Landscape Architecture, 9, 1029-1037. https://doi.org/10.14627/537752098
  • Kim, J., Yu, S., Detrick, R., & Li, N. (2024). Exploring students’ perspectives on Generative AI-assisted academic writing. Education and Information Technologies, 30, 1265-1300. https://doi.org/10.1007/s10639-024-12878-7
  • Kıyak, Y.S., & Emekli, E. (2024). ChatGPT prompts for generating multiple-choice questions in medical education and evidence on their validity: A literature review. Postgraduate Medical Journal, 2024, qgae065. https://doi.org/10.1093/postmj/qgae065
  • Knoth, N., Tolzin, A., Janson, A., & Leimeister, J.M. (2024). AI literacy and its implications for prompt engineering strategies. Computer and Education: Artificial Intelligence, 6, 100225. https://doi.org/10.1016/j.caeai.2024.100225
  • Koga, S., & Du, W. (2024). ChatGPT’s limited accuracy in generating anatomical images for medical education. Skeletal Radiology, 53, 1595-1596. https://doi.org/10.1007/s00256-024-04655-x
  • Koo, T.K., & Li, M.Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 15(2), 155-63. https://doi.org/10.1016/j.jcm.2016.02.012
  • Kruse, C., Schneider, J., & Seeber, I. (2023). Validity claims in children-AI discourse: Experiment with ChatGPT. In O. Poquet, A. Ortega-Arranz, O. Viberg, I. Chounta, B. McLaren, & J. Jovanovic (Eds.), Proceedings of the 16th International Conference on Computer Supported Education - Volume 1 (pp. 289-296). SciTePress. https://doi.org/10.5220/0012552300003693
  • Kuşçu, O., Pamuk, A.E., Sütay Süslü N., & Hosal, S. (2023). Is ChatGPT accurate and reliable in answering questions regarding head and neck cancer? Frontiers in Oncology, 13, 1256459. https://doi.org/10.3389/fonc.2023.1256459
  • Lee, H. (2023). The rise of ChatGPT: Exploring its potential in medical education. Anatomical Sciences Education, 17(5), 926-931. https://doi.org/10.1002/ase.2270
  • Lee, U., Han, A., Lee, J., Lee, E., Kim, J., Kim, H., & Lim, C. (2023). Prompt Aloud!: Incorporating image‐GenAI into STEAM class with learning analytics using prompt data. Education and Information Technologies, 29, 9575-9605. https://doi.org/10.1007/s10639-023-12150-4
  • Leivada, E., Murphy, E., & Marcus, G. (2023). DALL⋅E 2 fails to reliably capture common syntactic processes. Social Sciences & Humanities Open, 8, 100648. https://doi.org/10.1016/j.ssaho.2023.100648
  • Liao, J., Chen, X., Fu, Q., Du, L., He, X., Wang, X., Han, S., & Zhang, D. (2024). Text-to-image generation for abstract concepts. Proceedings of the AAAI Conference on Artificial Intelligence, 38(4), 3360-3368. https://doi.org/10.1609/aaai.v38i4.28122
  • Lim, B., Seth, I., Kah, S., Sofiadellis, F., Ross, R.J., Rozen, W.M., & Cuomo, R. (2023). Using generative artificial intelligence tools in cosmetic surgery: A study on rhinoplasty, facelifts, and blepharoplasty procedures. Journal of Clinical Medicine, 12, 6524. https://doi.org/10.3390/jcm12206524
  • Lim, Y., Choi, H., & Shim, H. (2025). evaluating image hallucination in text-to-image generation with question-answering. Proceedings of the AAAI Conference on Artificial Intelligence, 39(25), 26290-26298. https://doi.org/10.1609/aaai.v39i25.34827
  • Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.
  • Mack, K.A., Qadri, R., Denton, R., Kane, S.K., & Bennett, C.L. (2024). “They only care to show us the wheelchair”: Disability representation in text-to-image AI models. In F. Mueller, P. Kyburz, J. Williamson, C. Sas, M. Wilson, P. Dugas, & I. Shklovski (Eds.), Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ‘24) (pp. 1-23). Association for Computing Machinery. https://doi.org/10.1145/3613904.3642166
  • Mackey, B.P., Garabet, R., Maule, L., Tadesse, A., Cross, J., & Weingarten, M. (2024). Evaluating ChatGPT-4 in medical education: an assessment of subject exam performance reveals limitations in clinical curriculum support for students. Discovering Artificial Intelligence, 4(38), 1-5. https://doi.org/10.1007/s44163-024-00135-2
  • Magomere, J., Ishida, S., Afonja, T., Salama, A., Kochin, D., Yuehgoh, F., Hamzaoui, I., Sefala, R., Alaagib, A., Dalal, S., Marchegiani, B., Semenova, E., Crais, L., & Hall, S. M. (2025). The world wide recipe: A community-centred framework for fine-grained data collection and regional bias operationalisation. In J. W. Vaughan, S. Mohamed, S. Fazelpour, & T. B. Gillis (Eds.), Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT ‘25) (pp. 246-282). Association for Computing Machinery. https://doi.org/10.1145/3715275.3732019
  • Ministry of National Education [MoNE]. (2018). Sosyal bilgiler öğretim programı: İlkokul ve ortaokul 4,5,6, ve 7. sınıflar [Social studies curriculum: Elementary and middle school 4th, 5th, 6th and 7th grades]. Author.
  • Montenegro Rueda, M., Fernández-Cerero, J., Fernández-Batanero, J.M., & López-Meneses, E. (2023). Impact of the implementation of ChatGPT in education: A systematic review. Computers, 12, 153. https://doi.org/10.3390/computers12080153
  • Morton, J.L. (2024). On inscription and bias: data, actor network theory, and the social problems of text-to-image AI models. AI Ethics, 5, 775-790. https://doi.org/10.1007/s43681-024-00431-8
  • Nasution, N.E.A. (2023). Using artificial intelligence to create biology multiple choice questions for higher education. Agricultural and Environmental Education, 2(1), em002. https://doi.org/10.29333/agrenvedu/13071
  • Nielsen, J.P.S., Buchwald, C.V., & Grønhøj, C. (2023). Validity of the large language model ChatGPT (GPT4) as a patient information source in otolaryngology by a variety of doctors in a tertiary otorhinolaryngology department. Acta OtoLaryngologica, 143(9), 779-782, https://doi.org/10.1080/00016489.2023.2254809
  • Nguyen, H., Nguyen, V., López-Fierro, S., Ludovise, S., & Santagata, R. (2024). Simulating climate change discussion with large language models: Considerations for science communication at scale. In D. Joyner (Ed.), Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S ‘24) (pp. 28–38). Association for Computing Machinery. https://doi.org/10.1145/3657604.3662033
  • Ogunleye, B., Zakariyyah, K.I., Ajao, O., Olayinka, O., & Sharma, H. (2024). A systematic review of generative AI for teaching and learning practice. Education Sciences, 14, 636. https://doi.org/10.3390/educsci14060636
  • OpenAI. (2023). DALL·E 3 [Large language model]. https://openai.com/index/dall-e-3/
  • Pack, A., Barrett, A., & Escalante, J. (2024). Large language models and automated essay scoring of English language learner writing: Insights into validity and reliability. Computers and Education: Artificial Intelligence, 6, 100234. https://doi.org/10.1016/j.caeai.2024.100234
  • Peled, T., Sela, H.Y., Weiss, A., Grisaru-Granovsky, S., Agrawal, S., & Rottenstreich, M. (2024). Evaluating the validity of ChatGPT responses on common obstetric issues: Potential clinical applications and implications. International Journal of Gynecology & Obstetrics, 166(3), 1127-1133. https://doi.org/10.1002/ijgo.15501
  • Sallam, M. (2023). ChatGPT utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare, 11, 887. https://doi.org/10.3390/healthcare11060887
  • Sanusi, I. T., Oyelere, S. S., & Omidiora, J. O. (2022). Exploring teachers’ preconceptions of teaching machine learning in high school: A preliminary insight from Africa. Computers and Education Open, 3, 100072. https://doi.org/10.1016/j.caeo.2021.100072
  • Seth, I., Lim, B., Cevik, J., Sofiadellis, F., Ross, R.J., Cuomo, R., & Rozen, W.M. (2024). Utilizing GPT-4 and generative artificial intelligence platforms for surgical education: An experimental study on skin ulcers. European Journal of Plastic Surgery, 47, 19. https://doi.org/10.1007/s00238-024-02162-9
  • Suppadungsuk, S., Thongprayoon, C., Krisanapan, P., Tangpanithandee, S., Garcia Valencia, O., Miao, J., Mekraksakit, P., Kashani, K., & Cheungpasitporn, W. (2023). Examining the validity of Chatgpt in identifying relevant nephrology literature: Findings and implications. Journal of Clinical Medicine, 12, 5550. https://doi.org/10.3390/jcm12175550
  • Temsah, M.H., Alhuzaimi, A.N., Almansour, M. Aljamaan, F., Alhasan, K., Batarfi, M.A., Altamimi, I., Alharbi, A., Alsuhaibani, A.A., Alwakeel, L., Alzahrani, A.A., Alsulaim, K.B., Jamal, A., Khayat, A., Alghamdi, M.H., Halwani, R., Khan, M.K., Al-Eyadhy, A., & Nazer, R. (2024). Art or Artifact: Evaluating the accuracy, appeal, and educational value of AI-generated imagery in DALL·E 3 for illustrating congenital heart diseases. Journal of Medical Systems, 48, 54. https://doi.org/10.1007/s10916-024-02072-0
  • Vartiainen, H., & Tedre, M. (2023). Using artificial intelligence in craft education: Crafting with text to-image generative models. Digital Creativity, 34, 1-21. https://doi.org/10.1080/14626268.2023.2174557
  • Vartiainen, H., Kahila, J., Tedre, M., López-Pernas, S., & Pope, N. (2024). Enhancing children’s understanding of algorithmic biases in and with text-to-image generative AI. New Media & Society. Advance online publication. https://doi.org/10.1177/14614448241252820
  • Vartiainen, H., Tedre, M., & Jormanainen, I. (2023). Co-creating digital art with GenAI in K-9 education: Socio- material insights. International Journal of Education Through Art, 19(3), 405-423. https://doi.org/10.1386/eta_00143_1
  • Wang, J., Liu, X. G., Di, Z., Liu, Y., & Wang, X. E. (2023). T2IAT: Measuring valence and stereotypical biases in text-to-image generation (Publication no: 2306.00905). Arxiv. https://arxiv.org/abs/2306.00905
  • Wang, N., Wang, X., & Su, Y.S. (2024). Critical analysis of the technological affordances, challenges and future directions of Generative AI in education: A systematic review, Asia Pacific Journal of Education, 44(1), 139-155. https://doi.org/10.1080/02188791.2024.2305156
  • Wei, Q., Yao, Z., Cui, Y., Wei, B., Jin, Z., & Xu, X. (2024). Evaluation of ChatGPT-generated medical responses: A systematic review and meta-analysis. Journal of Biomedical Informatics, 151, 104620. https://doi.org/10.1016/j.jbi.2024.104620
  • Wittmann, J. (2023). Science fact vs science fiction: A ChatGPT immunological review experiment gone awry. Immunology Letters, 256-257, 42-47. https://doi.org/10.1016/j.imlet.2023.04.002
  • Xu, J., Liu, X., Wu, Y., Tong, Y., Li, Q., Ding, M., Tang, J., & Dong, Y. (2024). ImageReward: Learning and evaluating human preferences for text-to-image generation (Publication no: 2304.05977). Arxiv. https://arxiv.org/pdf/2304.05977
  • Yang, S., & Chang, M.C. (2024). The assessment of the validity, safety, and utility of ChatGPT for patients with herniated lumbar disc A preliminary study. Medicine Open, 103(23), 1-4. https://doi.org/10.1097/MD.0000000000038445
  • Yeh, H.C. (2024). Revolutionizing language learning: Integrating generative AI for enhanced language proficiency. Educational Technology & Society, 27(3), 335-353. https://doi.org/%2010.30191/ETS.202407_27(3).TP01
  • Yeşilyurt, S., Dündar, R., Demir, R. Z. & Yeşilyurt, A. G. (2025). Teaching social studies with generative artificial ıntelligence tools: advantages and disadvantages. Journal of Interdisciplinary Educational Research, 9(20), 1-16. https://doi.org/10.57135/jier.1594253
  • Yetişensoy, O. (2025). Beyond algorithms: Unveiling the intersection of AI ethics, citizenship education, and social studies. In J. Zajda & A. Rapoport (Eds.), Discourses of globalisation and citizenship education. globalisation, comparative education and policy research (pp. 151-166). Springer. https://doi.org/10.1007/978-3-031-86145-1_9
  • Yetişensoy, O., & Karaduman, H. (2024). The effect of AI-powered chatbots in social studies education. Education and Information Technologies, 29, 17035-17069. https://doi.org/10.1007/s10639-024-12485-6
  • Yu, H. (2024). The application and challenges of ChatGPT in educational transformation: New demands for teachers’ roles. Heliyon, 10(2), e24289. https://doi.org/10.1016/j.heliyon.2024.e24289
  • Zack T, Lehman E, Suzgun M., Rodriguez, J.A., & Celi, L.A. (2024). Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: A model evaluation study. The Lancet Digital Health, 6, e12-22. https://doi.org/10.1016/S2589-7500(23)00225-X
  • Zalzal, H.G., Cheng, J., & Shah, R.K. (2023). Evaluating the current ability of ChatGPT to assist in professional potolaryngology education. Oto Open, 7(4), e94. https://doi.org/10.1002/oto2.94
  • Zarei M., Zarei, M., Hamzehzadeh, S., Oliyaei S.S.B., & Hosseini, M. (2024). ChatGPT, a friend or a foe in medical education: A review of strengths, challenges, and opportunities. Shiraz E-Medical Journal, 25(6), e145840. https://doi.org/10.5812/semj-145840
  • Zhang, E.Y., Cheok, A.D., Pan, Z., Cai, J., & Yan, Y. (2023). From Turing to transformers: A comprehensive review and tutorial on the evolution and applications of generative transformer models. Sci, 5, 46. https://doi.org/10.3390/sci5040046
  • Zhang, L., Liao, X., Yang, Z., Gao, B., Wang, C., Yang, Q., & Li, D. (2024). Partiality and misconception: Investigating cultural representativeness in text-to-image models. In F. Mueller, P. Kyburz, J. Williamson, C. Sas, M. Wilson, P. Dugas, & I. Shklovski (Eds.), Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ‘24) (pp. 1-25) Association for Computing Machinery. https://doi.org/10.1145/3613904.3642877

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.