KaydedilenlerDerginizi ekleyin

Dergiler / Necatibey Eğitim Fakültesi Elektronik Fen ve Matematik Eğitimi Dergisi / 2026 / Cilt: 20 Sayı: 1

Fen Eğitiminde Yapay Zeka (YZ) Araştırma Eğilimleri: Etik Kaygıların Eleştirel Açıdan İncelenmesi

Artificial Intelligence (AI) Research Trends in Science Education: A Critical Review of Ethical Concerns

Ulku Seher Budak(Boğazici University)0000-0002-4047-9920
Kemal Akoğlu(BOGAZICI UNIVERSITY)0000-0002-5688-1316
Sayfa
1–33
Yayın Tarihi
2026-06-26
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bu inceleme, bilim eğitiminde YZ ile ilgili araştırmaların son yıllarda önemli ölçüde arttığını ortaya koymuştur.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Yapay Zeka (YZ), insanlığı geliştiren ve ortak yararı ilerleten hedeflere ulaşmak için akıllı makinelerin bilim ve mühendisliğidir.
📊
Temel Bulgular & Veri:Bu sistematik literatür incelemesi, Web of Science veritabanında indekslenen 39 ampirik ve hakemli bilim eğitimi çalışmasında, bilim eğitiminde yapay zekanın ilk kez bahsedildiği andan 2024 yılının sonuna kadar nasıl ele alındığını incelemek ve etik kaygıları ele almak amacıyla yapılmıştır.
💡
Sonuç & Katkı:Bu inceleme, bilim eğitiminde YZ ile ilgili araştırmaların son yıllarda önemli ölçüde arttığını ortaya koymuştur.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:2024 Verileri
⚙️Değişkenler:Yapay zeka · fen eğitimi · sistematik derleme

Özet

Yapay Zeka (YZ), insanlığı geliştiren ve ortak yararı ilerleten hedeflere ulaşmak için akıllı makinelerin bilim ve mühendisliğidir. Bu sistematik literatür incelemesi, Web of Science veritabanında indekslenen 39 ampirik ve hakemli bilim eğitimi çalışmasında, bilim eğitiminde yapay zekanın ilk kez bahsedildiği andan 2024 yılının sonuna kadar nasıl ele alındığını incelemek ve etik kaygıları ele almak amacıyla yapılmıştır. Fen eğitiminde YZ araştırmalarındaki eğilimleri belirlemek için içerik analizi kullanılmıştır. Bu inceleme, bilim eğitiminde YZ ile ilgili araştırmaların son yıllarda önemli ölçüde arttığını ortaya koymuştur. Araştırmaların çoğunun ABD ve Kore'de yapıldığı tespit edilmiştir. Bu çalışma, çok az sayıda çalışmanın ChatGPT gibi YZ'yi katılımcı olarak içerdiğini, çoğunun ise insanları katılımcı olarak içerdiğini göstermiştir. Öte yandan, çoğu çalışma K-12 öğrencilerine odaklanırken, en az sayıda çalışma üniversite öğrencileri ve eğitimcilere odaklanmıştır. YZ'nin gelişimi, sosyal ve etik kaygıları da beraberinde getirmektedir. İncelenen makalelerin neredeyse yarısı YZ ile ilgili etik kaygıları ele almamıştır. En sık ele alınan etik kaygılar önyargı, gizlilik ve doğruluktur. Bu çalışma, bilim eğitiminde YZ ile ilgili gelecekteki çalışmalara önemli kanıta dayalı destek sağlayabilir.

Abstract

Artificial intelligence (AI) is the science and engineering of intelligent machines to achieve goals that improve humanity and advance the common good. This systematic literature review aimed to examine how AI in science education was addressed in 39 empirical and peer-reviewed studies in science education indexed in the Web of Science database from first mention until the end of 2024, and to address ethical concerns. Content analysis was used to identify trends in AI research in science education. This review revealed that research on AI in science education has increased significantly in recent years. USA and Korea were found to be the origin of most of the research. This study demonstrated that very few of the studies involved AI, such as ChatGPT, as a participant, and most of them involved humans as participants. On the other hand, most studies focused on K-12 students, and the fewest focused on university students and educators. The development of AI raises social and ethical concerns. Nearly half of the reviewed articles did not address AI-related ethical concerns. The most common ethical concerns addressed were bias, privacy, and accuracy. This study can provide important evidence-based support for future studies of AI in science education.

🔖

Bu Makaleye Atıf Yap

Budak, U. S., Akoğlu, K. (2026). Fen Eğitiminde Yapay Zeka (YZ) Araştırma Eğilimleri: Etik Kaygıların Eleştirel Açıdan İncelenmesi. Necatibey Eğitim Fakültesi Elektronik Fen ve Matematik Eğitimi Dergisi, Cilt: 20 Sayı: 1, 1–33. https://doi.org/10.17522/balikesirnef.1788147

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (83)

  1. Adeyele, V. O., & Ramnarain, U. (2024). Exploring the integration of ChatGPT in inquiry-based learning: Teacher perspectives. International Journal of Technology in Education, 7(2), 200-217. https://doi.org/10.46328/ijte.638
  2. Aksit, O. (2025). Teaching science with AI: How preservice teachers perceive the role of AI tools in science education. In Kadry S. and Balasubramaniam S. (Eds.), Artificial intelligence in education: Creating an equitable, creative, and effective learning environment. IntechOpen. https://doi.org/10.5772/intechopen.1012592
  3. AlKanaan, H. M. N. (2022). Awareness regarding the implication of artificial intelligence in science education among pre-service science teachers. International Journal of Instruction, 15(3), 895-912. https://doi.org/10.29333/iji.2022.15348a
  4. Almasri, F. (2024). Exploring the impact of artificial intelligence in teaching and learning of science: A systematic review of empirical research. Research in Science Education, 54(5), 977-997. https://doi.org/10.1007/s11165-024-10176-3
  5. Anderson, M. & Anderson, S. L. (2006). "Guest Editors' Introduction: Machine Ethics, IEEE Intelligent Systems, 21 (4), 10-11. https://doi.org/10.1109/MIS.2006.70
  6. Anderson, M., & Anderson, S. L. (2007). Machine ethics: Creating an ethical intelligent agent. AI magazine, 28(4), 15-26. https://doi.org/10.1609/aimag.v28i4.20
  7. Antonenko, P., & Abramowitz, B. (2023). In-service teachers’ (mis)conceptions of artificial intelligence in K-12 science education. Journal of Research on Technology in Education, 55(1), 64-78. https://doi.org/10.1080/15391523.2022.2119450
  8. Alshorman, S. M. (2024). The readiness to use AI in teaching science: Science teachers’ perspective. Journal of Baltic Science Education, 23(3), 432-448. https://doi.org/10.33225/jbse/24.23.432
  9. Baker, R. S., & Hawn, A. (2022). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32(4), 1052–1092. https://doi.org/10.1007/s40593-021-00285-9
  10. Behrens, K. A., Marbach-Ad, G., & Kocher, T. D. (2025). AI in the genetics classroom: A useful tool but not a replacement for creative writing. Journal of Science Education and Technology, 34, 621-635. https://doi.org/10.1007/s10956-024-10160-6
  11. Blanchard, K., & Peale, N. V. (2011). The power of ethical management. Random House.
  12. Birkle, C., Pendlebury, D. A., Schnell, J., & Adams, J. (2020). Web of Science as a data source for research on scientific and scholarly activity. Quantitative Science Studies, 1(1), 363-376. https://doi.org/10.1162/qss_a_00018
  13. Budak, U.S., & Yerdelen Damar, S. (2025). Research trends and emerging areas of interest from 2004 to 2024 in science education. Journal of Turkish Science Education, 22(4), 625-654. http://doi.org/10.36681/tused.2025.032
  14. Casal-o, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K-12: A systematic literature review. International Journal of STEM Education, 10, Article 29. https://doi.org/10.1186/s40594-023-00418-7
  15. Chang, J., Park, J., & Park, J. (2023). Using an artificial intelligence chatbot in scientific inquiry: Focusing on a guided-inquiry activity using inquiry bot. Asia-Pacific Science Education, 9(1), 44-74. https://doi.org/10.1163/23641177-bja10062
  16. Chen, J., & See, K. C. (2020). Artificial intelligence for COVID-19: Rapid review. Journal of Medical Internet Research, 22(10), Article e21476. https://doi.org/10.2196/21476
  17. Chen, P. Y., & Liu, Y. C. (2024). Impact of AI robot image recognition technology on improving students' conceptual understanding of cell division and science learning motivation. Journal of Baltic Science Education, 23(2), 208-220. https://doi.org/10.33225/jbse/24.23.208
  18. Cheung, K. K. C., Pun, J. K. H & Li, W. (2024). Students’ holistic reading of socio-scientific texts on climate change in a ChatGPT scenario. Research in Science Education, 54, 957-976. https://doi.org/10.1007/s11165-024-10177-2
  19. Cheung, K. K. C., Pun, J., Kenneth-Li, W., & Mai, J. (2025). Exploring students’ multimodal representations of ideas about epistemic reading of scientific texts in generative AI tools. Journal of Science Education and Technology, 34, 284-297. https://doi.org/10.1007/s10956-024-10182-0
  20. Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32, 444-452. https://doi.org/10.1007/s10956-023-10039-y
  21. Cooper, G., & Tang, K. S. (2024). Pixels and pedagogy: Examining science education imagery by generative artificial intelligence. Journal of Science Education and Technology, 33, 556-568. https://doi.org/10.1007/s10956-024-10104-0
  22. Deveci Topal, A., Dilek Eren, C., & Kolburan Geçer, A. (2021). Chatbot application in a 5th grade science course. Education and Information Technologies, 26, 6241-6265. https://doi.org/10.1007/s10639-021-10627-8
  23. Dignum, V. (2021). The role and challenges of education for responsible AI. London Review of Education, 19(1), 1–11. https://doi.org/10.14324/LRE.19.1.01
  24. Erduran, S. (2023). AI is transforming how science is done. Science education must reflect this change. Science, 382(6677), Article eadm9788. https://www.science.org/doi/10.1126/science.adm9788
  25. Garofalo, S. G., & Farenga, S. J. (2025). Science teacher perceptions of the state of knowledge and education at the advent of generative artificial intelligence popularity. Science & Education, 34, 893-912. https://doi.org/10.1007/s11191-024-00534-y
  26. Gay, L. R., Mills, G. E., & Airasian, P. W. (2012). Educational research: Competencies for analysis and applications (10th ed). Pearson.
  27. Göktepe Körpeoğlu, S., & Göktepe Yıldız, S. (2024). Using artificial intelligence to predict students’ STEM attitudes: An adaptive neural-network-based fuzzy logic model. International Journal of Science Education, 46(10), 1001-1026. https://doi.org/10.1080/09500693.2023.2269291
  28. Green, J., & Thorogood, N. (2004). Qualitative methods for health research. Sage.
  29. Heeg, D. M., & Avraamidou, L. (2023). The use of Artificial intelligence in school science: a systematic literature review. Educational Media International, 60(2), 125-150. https://doi.org/10.1080/09523987.2023.2264990
  30. Heilinger, J. C. (2022). The ethics of AI ethics. A constructive critique. Philosophy & Technology, 35, Article 61. https://doi.org/10.1007/s13347-022-00557-9
  31. HLEG. (2019). High-level expert group on Artificial Intelligence. Ethics guidelines for trustworthy AI. Brussels: European Commission. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai
  32. Huang, X., & Qiao, C. (2024). Enhancing computational thinking skills through artificial intelligence education at a STEAM high school. Science & Education, 33, 383-403. https://doi.org/10.1007/s11191-022-00392-6
  33. Huang, H. W., Teng, D. C. E., & Tiangco, J. A. N. Z. (2025). The impact of AI chatbot-supported guided discovery learning on pre-service teachers’ learning performance and motivation. Journal of Science Education and Technology, 34, 1286-1300. https://doi.org/10.1007/s10956-024-10179-9
  34. Huang, C., Zhang, Z., Mao, B., & Yao, X. (2022). An overview of artificial intelligence ethics. IEEE Transactions on Artificial Intelligence, 4(4), 799-819. https://doi.org/10.1109/TAI.2022.3194503
  35. Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, Article 100001. https://doi.org/10.1016/j.caeai.2020.100001
  36. Jang, H., & Choi, H. (2025). A double-edged sword: Physics educators’ perspectives on utilizing ChatGPT and its future in classrooms. Journal of Science Education and Technology, 34(2), 267-283. https://doi.org/10.1007/s10956-024-10173-1
  37. Jho, H., & Ha, M. (2024). Towards effective argumentation: Design and implementation of a generative AI-based evaluation and feedback system. Journal of Baltic Science Education, 23(2), 280-291. https://doi.org/10.33225/jbse/24.23.280
  38. Jia, F., Sun, D., & Looi, C. K. (2024). Artificial intelligence in science education (2013-2023): Research trends in ten years. Journal of Science Education and Technology, 33, 94-117. https://doi.org/10.1007/s10956-023-10077-6
  39. Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023). New era of artificial intelligence in education: Towards a sustainable multifaceted revolution. Sustainability, 15(16), 1-27. https://doi.org/10.3390/su151612451
  40. Karahan, E. (2023). Using video-elicitation focus group interviews to explore pre-service science teachers’ views and reasoning on artificial intelligence. International Journal of Science Education, 45(15), 1283-1302. https://doi.org/10.1080/09500693.2023.2200887
  41. Karan, B., & Angadi, G. R. (2024). Potential risks of artificial intelligence integration into school education: A systematic review. Bulletin of Science, Technology & Society, 43(3–4), 67–85. https://doi.org/10.1177/02704676231224705
  42. Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI chatbots in education: Systematic literature review. International Journal of Educational Technology in Higher Education, 20, Article 56. https://doi.org/10.1186/s41239-023-00426-1
  43. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174. https://www.jstor.org/stable/2529310
  44. Lee, G. G., Mun, S., Shin, M. K., & Zhai, X. (2024). Collaborative learning with artificial intelligence speakers. Science & Education, 34, 847–875. https://doi.org/10.1007/s11191-024-00526-y
  45. Lee, G. G., & Zhai, X. (2024). Using ChatGPT for science learning: A study on pre-service teachers' lesson planning. IEEE Transactions on Learning Technologies., 17, 643-1660. https://doi.org/10.1109/TLT.2024.3401457
  46. Lee, J., An, T., Chu, H. E., Hong, H. G., & Martin, S. N. (2023). Improving science conceptual understanding and attitudes in elementary science classes through the development and application of a rule-based AI chatbot. Asia-Pacific Science Education, 9(2), 365-412. https://doi.org/10.1163/23641177-bja10070
  47. Lee, J., Lee, G. G., & Hong, H. G. (2023). Automated assessment of student hand drawings in free-response items on the particulate nature of matter. Journal of Science Education and Technology, 32, 549-566. https://doi.org/10.1007/s10956-023-10042-3
  48. Lee, J., Suh, T., Roy, D., & Baucus, M. (2019). Emerging technology and business model innovation: The case of artificial intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), Article 44. https://doi.org/10.3390/joitmc5030044
  49. Li, Z., & Ironsi, C. S. (2025). The efficacy of generative artificial intelligence in developing science education preservice teachers’ writing skills: An experimental approach. Journal of Science Education and Technology, 34, 1260-1271. https://doi.org/10.1007/s10956-024-10148-2
  50. Limna, P., Jakwatanatham, S., Siripipattanakul, S., Kaewpuang, P., & Sriboonruang, P. (2022). A review of artificial intelligence (AI) in education during the digital era. Advance Knowledge for Executives, 1(1), 1-9. https://ssrn.com/abstract=4160798
  51. Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: Go beyond artificial intelligence. Mobile Networks and Applications, 23, 368–375. https://doi.org/10.1007/s11036-017-0932-8
  52. Martin, P. P., Kranz, D., Wulff, P., & Graulich, N. (2024). Exploring new depths: Applying machine learning for the analysis of student argumentation in chemistry. Journal of Research in Science Teaching, 61(8), 1757-1792. https://doi.org/10.1002/tea.21903
  53. McCarthy, J. (2007). From here to human-level AI. Artificial Intelligence, 171(18), 1174–1182. https://doi.org/10.1016/j.artint.2007.10.009
  54. Mittal, U., Sai, S., Chamola, V. & Sangwan, D. (2024). A comprehensive review on generative AI for education, IEEE Access, 12, 142733-142759. https://doi.org/10.1109/ACCESS.2024.3468368
  55. Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The Prisma Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine, 151(4), 264– 269. https://10.7326/0003-4819-151-4-200908180-00135
  56. Moher, D., Liberati, A., Tetzlaff, J. & Altman, D. G. (2010). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. International Journal of Surgery, 8(5), 336-341. https://doi.org/10.1136/bmj.b2535
  57. Monteiro, F. F., Souza, P. V. S., da Silva, M. C., Maia, J. R., da Silva, W. F., & Girardi, D. (2024). ChatGPT in Brazilian K-12 science education. Frontiers in Education, 9, Article 1321547. https://doi.org/10.3389/feduc.2024.1321547
  58. Ng, D. T. K., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. W. (2023). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies, 28, 8445-8501. https://doi.org/10.1007/s10639-022-11491-w
  59. Ng, D. T. K., Tan, C. W., & Leung, J. K. L. (2024). Empowering student self-regulated learning and science education through ChatGPT: A pioneering pilot study. British Journal of Educational Technology, 55, 1328-1353.https://doi.org/10.1111/bjet.13454
  60. Nguyen, H., & Hayward, J. (2025). Applying generative artificial intelligence to critiquing science assessments. Journal of Science Education and Technology, 34, 199-214. https://doi.org/10.1007/s10956-024-10177-x
  61. Nja, C. O., Idiege, K. J., Uwe, U. E., Meremikwu, A. N., Ekon, E. E., Erim, C. M., & Umalili, B. (2023). Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers. Smart Learning Environments, 10, Article 42. https://doi.org/10.1186/s40561-023-00261-x
  62. Oh, P. S., & Lee, G. G. (2025). Confronting imminent challenges in humane epistemic agency in science education: An interview with ChatGPT. Science & Education, 34, 779-805. https://doi.org/10.1007/s11191-024-00515-1
  63. Okulu, H. Z., & Muslu, N. (2024). Designing a course for pre-service science teachers using ChatGPT: What ChatGPT brings to the table. Interactive Learning Environments, 32(10), 7450-7467. https://doi.org/10.1080/10494820.2024.2322462
  64. Ramnarain, U., Ogegbo, A. A., Penn, M., Ojetunde, S., & Mdlalose, N. (2025). Pre-Service science teachers’ intention to use generative artificial intelligence in inquiry-based teaching. Journal of Science Education and Technology, 34, 1272-1285. https://doi.org/10.1007/s10956-024-10159-z
  65. Resnik, D. B. (2011). What is ethics in research and why is it important. National Institute of Environmental Health Sciences, 1(10), 49–70. https://www.niehs.nih.gov/research/resources/bioethics/whatis
  66. Sabourin Laflamme, A., & Bruneault, F. (2022). How to teach AI ethics? A pragmatist approach to ethical competency. Pédagogie Collégiale, 36(1), 61–67. https://eduq.info/xmlui/handle/11515/38675
  67. Siau, K., & Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 31(2), 47–53. https://ink.library.smu.edu.sg/sis_research/9371
  68. Siau, K., & Wang, W. (2020). Artificial intelligence (AI) ethics: Ethics of AI and ethical AI. Journal of Database Management, 31(2), 74-87. https://doi.org/10.4018/JDM.2020040105
  69. Singer, A. E. (2010). Integrating ethics and strategy: A pragmatic approach. Journal of Business Ethics, 92, 479-491. https://doi.org/10.1007/s10551-009-0176-z
  70. Singh, V. K., Singh, P., Karmakar, M., Leta, J., & Mayr, P. (2021). The journal coverage of Web of Science, Scopus and dimensions: A comparative analysis. Scientometrics, 126, 5113-5142. https://doi.org/10.1007/s11192-021-03948-5
  71. Stockman, C., & Nottingham, E. (2022). Surveillance capitalism in schools: What’s the problem?. Digital Culture & Education, 14(1), 1–15. https://www.digitalcultureandeducation.com/s/Stockman_Nottingham_2022-mw5h.pdf
  72. Taani, O., & Alabidi, S. (2025). ChatGPT in education: Benefits and challenges of ChatGPT for mathematics and science teaching practices. International Journal of Mathematical Education in Science and Technology, 56(9), 1748-1777. https://doi.org/10.1080/0020739X.2024.2357341
  73. Uyanik Aktulun, O., Kasapoglu, K., & Aydogdu, B. (2024). Comparing Turkish pre-service STEM and non-STEM teachers’ attitudes and anxiety toward artificial intelligence. Journal of Baltic Science Education, 23(5), 950-963. https://doi.org/10.33225/jbse/24.23.950
  74. Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & Health Sciences, 15, 398–405. https://doi.org/10.1111/nhs.12048
  75. Wei, X., Wang, L., Lee, L. K., & Liu, R. (2024). Multiple generative AI pedagogical agents in augmented reality environments: A study on implementing the 5E model in science education. Journal of Educational Computing Research, 63(2), 336–371. https://doi.org/10.1177/07356331241305519
  76. Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: A systematic review from 2011 to 2021. International Journal of STEM Education, 9, Article 59. https://doi.org/10.1186/s40594-022-00377-5
  77. Yampolskiy, R. V. (2019). Predicting future AI failures from historic examples. Foresight, 21(1), 138-152. https://doi.org/10.1108/FS-04-2018-0034
  78. Yusuf, A., Pervin, N., Román‐González, M., & Noor, N. M. (2024). Generative AI in education and research: A systematic mapping review. Review of Education, 12, Article e3489. https://doi.org/10.1002/rev3.3489
  79. Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 2021, Article 8812542. https://doi.org/10.1155/2021/8812542
  80. Zhai, X., Haudek, K. C., & Ma, W. (2023). Assessing argumentation using machine learning and cognitive diagnostic modeling. Research in Science Education, 53, 405-424. https://doi.org/10.1007/s11165-022-10062-w
  81. Zhai, X., He, P., & Krajcik, J. (2022). Applying machine learning to automatically assess scientific models. Journal of Research in Science Teaching, 59(10), 1765-1794. https://doi.org/10.1002/tea.21773
  82. Zhai, X., Nyaaba, M., & Ma, W. (2025). Can generative AI and ChatGPT outperform humans on cognitive-demanding problem-solving tasks in science?. Science & Education, 34, 649-670. https://doi.org/10.1007/s11191-024-00496-1
  83. Zhang, W. X., Lin, J. J., & Hsu, Y. S. (2025). AI‐assisted assessment of inquiry skills in socioscientific issue contexts. Journal of Computer Assisted Learning, 41(1), Article e13102. https://doi.org/10.1111/jcal.13102