Bilim Eğitiminde Yapay Zekâ Araçlarının Entegrasyonuna İlişkin BİLSEM Öğretmenlerinin Görüşleri

Abstract

Bu nitel çalışma, BİLSEM (Bilim ve Sanat Merkezi) öğretmenlerinin bilim eğitiminde yapay zekâ (YZ) araçlarının kullanımına ilişkin görüşlerini ortaya koymayı amaçlamaktadır. Eğitim ortamlarında YZ teknolojilerinin giderek artan varlığıyla birlikte, bu araçların özel yetenekli öğrencilerle çalışan öğretmenler tarafından nasıl algılandığının ve uygulandığının anlaşılması önem taşımaktadır. Çalışma, Hatay, Ankara ve Yalova’daki BİLSEM kurumlarında görev yapan 20 öğretmenin deneyimlerini incelemek üzere fenomenolojik bir araştırma deseni benimsemiştir. Veriler yarı yapılandırılmış görüşmeler yoluyla toplanmış ve NVivo-11 yazılımı kullanılarak içerik analizi ile çözümlenmiştir. Bulgular, öğretmenlerin YZ araçlarını genel olarak öğrenci katılımını artırmada, bireyselleştirilmiş öğrenmeyi desteklemede ve STEM ile ilgili görevleri kolaylaştırmada yararlı gördüklerini ortaya koymuştur. Bununla birlikte, katılımcılar altyapı yetersizlikleri, eğitim eksikliği ve etik kaygılar gibi çeşitli zorluklara da dikkat çekmiştir. Ayrıca öğretmenler, YZ araçlarının daha etkili entegrasyonuna yönelik yapıcı öneriler sunmuş; mesleki gelişim fırsatlarının artırılması, kurumsal destek sağlanması ve özel yetenekli öğrencilerin eğitimine uyarlanmış YZ temelli kaynakların geliştirilmesi gerektiğini vurgulamışlardır. Bu çalışma, eğitimde yapay zekâ üzerine büyüyen literatüre, bağlama özgü bulgular ve politika yapıcılar, program geliştiriciler ve öğretmen eğitimcileri için pratik çıkarımlar sunarak katkı sağlamaktadır.

Keywords

• Yapay Zekâ
• BİLSEM merkezleri
• Özel yetenekli öğrenciler
• Bilim eğitimi
• Öğretmen algıları

BİLSEM Teachers’ Perspectives on the Integration of Artificial Intelligence Tools in Science Education

Abstract

This qualitative study aims to explore the views of BİLSEM (Science and Art Center) teachers on the use of artificial intelligence (AI) tools in science education (SE). With the increasing presence of AI technologies in educational environments, it is essential to understand how these tools are perceived and implemented by educators working with gifted students. The study adopts a phenomenological research design to investigate the experiences of 20 teachers from BILSEM institutions in Hatay, Ankara, and Yalova. Data were collected through semi-structured interviews and analyzed using content analysis via NVivo-11 software. The findings reveal that teachers generally perceive AI tools as beneficial in enhancing student engagement, facilitating individualized learning, and supporting STEM-related tasks. However, participants also highlighted several challenges, including infrastructure limitations, lack of training, and ethical concerns. Furthermore, teachers provided constructive suggestions for more effective integration of AI tools, emphasizing the need for professional development, institutional support, and the development of AI-based resources tailored for gifted education. This study contributes to the growing body of research on AI in education by offering context-specific insights and practical implications for policymakers, curriculum designers, and teacher educators.

Keywords

• Artifical Intelligence
• BILSEM centers
• Gifted students
• Science education
• Teacher perceptions

References

1. Abu, N. K., and Dal, E. (2025). Gifted students’ perceptions on science courses in general education classroom. Journal of Baltic Science Education, 24(2), 296–311. https://doi.org/10.33225/jbse/25.24.296
2. Açışlı Çelik, S., and Ergin, I. (2022). Opinions of middle school students on the concept of science and the use of robotic systems. International Journal of Technology in Education, 5(1), 154–170. https://doi.org/10.46328/ijte.232
3. Adıgüzel, T., Kaya, M. H., and Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429. https://doi.org/10.30935/cedtech/13152
4. Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M., Irshad, M., Arraño-Muñoz, M., and Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10(1), 1–14. https://doi.org/10.1057/s41599-023-01787-8
5. Ahmad, S. F., Rahmat, M. K., Mubarik, M. S., Alam, M. M., and Hyder, S. I. (2021). Artificial intelligence and its role in education. Sustainability, 13(22), 12902. https://doi.org/10.3390/su132212902
6. Al Darayseh, A. (2023). Acceptance of artificial intelligence in teaching science: Science teachers' perspective. Computers and Education: Artificial Intelligence, 4, 100132. https://doi.org/10.1016/j.caeai.2023.100132
7. Alarabi, K., Alabidi, S., Tairab, H., Pagani, L. S., AlSadrani, B., and Abu Khurma, O. (2025). AI’s impact on science education: A study of ant and bee mindsets in UAE science classrooms. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1577285
8. Alharbi, S. M., Elfeky, A. I., and Ahmed, E. S. (2022). The effect of e-collaborative learning environment on development of critical thinking and higher order thinking skills. Journal of Positive School Psychology, 6(6), 6848–6854. https://journalppw.com/index.php/jpsp/article/view/8692/5676

9. 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
10. 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, 977–997. https://doi.org/10.1007/s11165-024-10176-3
11. Almasri, F., Hewapathirana, G. I., Alhashem, F., Daniel, C. E., and Lee, N. (2022). The effect of gender composition and pedagogical approach on major and non-major undergraduates biology students’ achievement. Interactive Learning Environments, 1–33. https://doi.org/10.1080/10494820.2022.2066138
12. Alrazaq, A., AlSaad, R., Alhuwail, D., Ahmed, A., Healy, P. M., Latifi, S., Aziz, S., Damseh, R., Alrazak, S. A., and Sheikh, J. (2023). Large language models in medical education: Opportunities, challenges, and future directions. JMIR Medical Education, 9(1), e48291. https://doi.org/10.2196/48291
13. 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
14. Altinay, Z., Altinay, F., Dağlı, G., Shadiev, R., and Othman, A. (2024). Factors influencing AI learning motivation and personalisation among pre-service teachers in higher education. MIER Journal of Educational Studies Trends and Practices, 14 (2), 462-481. https://doi.org/10.52634/mier/2024/v14/i2/2714
15. Alwaqdani, M. (2025). Investigating teachers’ perceptions of artificial intelligence tools in education: Potential and difficulties. Education and Information Technologies, 30, 2737–2755. https://doi.org/10.1007/s10639-024-12903-9
16. Arıcı, F. (2024). Examination of research conducted on the use of artificial intelligence in science education. Sakarya University Journal of Education, 14(3), 539–562. https://doi.org/10.19126/suje.1485114
17. Aslam, S., Alghamdi, A. A., Abid, N., and Kumar, T. (2023). Challenges in implementing STEM education: Insights from novice STEM teachers in developing countries. Sustainability, 15(19), 14455. https://doi.org/10.3390/su151914455
18. Bayram-Jacobs, D., Wieske, G., and Henze, I. (2019). A chemistry lesson for citizenship: Students’ use of different perspectives in decision-making about the use and sale of laughing gas. Education Sciences, 9(2), 1–16. https://doi.org/10.3390/educsci9020100
19. Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö.E., Karadeniz, Ş., ve Demirel, F. (2020). Bilimsel araştırma yöntemleri (28.Baskı). Pegem Akademi.
20. Bulger, M. (2016). Personalized learning: The conversations we’re not having. Data and Society, 22(1), 1–29. https://datasociety.net/wp-content/uploads/2016/09/PersonalizedLearning_primer_2016.pdf
21. Carvalho, D. V., Pereira, E. M., and Cardoso, J. S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics, 8(8), 832. https://doi.org/10.3390/electronics8080832
22. Chiu, T. K. F., and Chai, C. (2020). Sustainable curriculum planning for artificial intelligence education: A self-determination theory perspective. Sustainability, 12(14), 5568. https://doi.org/10.3390/su12145568
23. Chiu, T. K. F., Meng, H., Chai, C.-S., King, I., Wong, S., and Yam, Y. (2022). Creation and evaluation of a pretertiary artificial intelligence (AI) curriculum. IEEE Transactions on Education, 65(1), 30–39. https://doi.org/10.1109/TE.2021.3085878
24. Cooper, G., Thong, L. P., and Tang, K. S. (2024). Transforming science education with virtual reality: An immersive representations model. Educational Media International, 61(3), 229–251. https://doi.org/10.1080/09523987.2024.2389348
25. Cortes, S. T., Lorca, A. S., Pineda, H. A., Tubog, R., and Vilbar, A. (2024). Strengthening science education in basic education through a professional development program on participatory action research for science teachers. Social Sciences & Humanities Open, 10, 101194. https://doi.org/10.1016/j.ssaho.2024.101194
26. Cotton, D. R., Cotton, P. A., and Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
27. Çubukçu, Z., ve Tosuntaş, Ş. B. (2018). Üstün yetenekli/zekâlı öğrencilerin eğitiminde teknolojinin yeri. Elektronik Eğitim Bilimleri Dergisi, 7(13), 45–57. https://dergipark.org.tr/en/pub/ejedus/article/417836
28. Davey, L. (1990). The application of case study evaluations. Practical Assessment, Research & Evaluation, 2(9). https://doi.org/10.7275/02g8-bb93
29. De Angelis, L., Baglivo, F., Arzilli, G., Privitera, G. P., Ferragina, P., Tozzi, A. E., and Rizzo, C. (2023). ChatGPT and the rise of large language models: The new AI-driven infodemic threat in public health. Frontiers in Public Health, 11, 1166120. https://doi.org/10.3389/fpubh.2023.1166120
30. Dempere, J., Modugu, K. P., Hesham, A., and Ramasamy, L. (2023). The impact of ChatGPT on higher education. Frontiers in Education, 8, 1206936. https://doi.org/10.3389/feduc.2023.1206936
31. Denny, P., Prather, J., Becker, B. A., Finnie-Ansley, J., Hellas, A., Leinonen, J., ... and Sarsa, S. (2024). Computing education in the era of generative AI. Communications of the ACM, 67(2), 56-67. https://doi.org/10.1145/3624720
32. Dergaa, I., Chamari, K., Zmijewski, P., and Saad, H. B. (2023). From human writing to artificial intelligence generated text: Examining the prospects and potential threats of ChatGPT in academic writing. Biology of Sport, 40(2), 615–622. https://doi.org/10.5114/biolsport.2023.125623
33. Dimitriadou, E., and Lanitis, A. (2023). A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms. Smart Learning Environments, 10(1), 12. https://doi.org/10.1186/s40561-023-00231-3
34. Efe, R., and Aslan Efe, H. (2025). Science teachers’ perceptions of the artificial intelligence in science education: Challenges, readiness, benefits, and impact on student learning. Journal of Baltic Science Education, 24(4), 655–669. https://doi.org/10.33225/jbse/25.24.655
35. El Fathi, T., Saad, A., Larhzil, H., Lamri, D., and Al Ibrahmi, E. M. (2025). Integrating generative AI into STEM education: Enhancing conceptual understanding, addressing misconceptions, and assessing student acceptance. Disciplinary and Interdisciplinary Science Education Research, 7(1), 6. https://doi.org/10.1186/s43031-025-00125-z
36. Elmas, R., Adiguzel-Ulutas, M., and 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
37. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., and Vayena, E. (2018). AI4People-An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
38. Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., and Pearson, A. T. (2023). Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers. npj Digital Medicine, 6, Article 75. https://doi.org/10.1038/s41746-023-00819-6
39. García-López, C., Tabuenca-Cuevas, M., and Navarro-Soria, I. (2025). A systematic review of the use of AI in EFL and EL classrooms for gifted students. Trends in Higher Education, 4(3), Article 33. https://doi.org/10.3390/higheredu4030033
40. García-Perales, R., and Almeida, L. S. (2019). An enrichment program for students with high intellectual ability: Positive effects on school adaptation. Comunicar, 27(60), 39–48. https://doi.org/10.3916/C60-2019-04
41. Gocen, A., and Aydemir, F. (2021). Artificial intelligence in education and schools. Research on Education and Media, 12(1), 13–21. https://doi.org/10.2478/rem-2020-0003
42. Gözüküçük, Y., and Bağçeci, B. (2025). Evaluation of BİLSEM teachers’ use of digital tools according to teachers’ opinions. Pedagogical Perspective, 4(2), 437–454. https://doi.org/10.29329/pedper.2025.137
43. Grinnell, F. (2021). Scientific inquiry, pluralism and complementarity. Scientific Inquiry Pluralism and Complementarity. https://doi.org/10.29329/pedper.2025.137
44. Grinschgl, S., and Neubauer, A. C. (2022). Supporting cognition with modern technology: Distributed cognition today and in an AI-enhanced future. Frontiers in Artificial Intelligence, 5, 908261. https://doi.org/10.3389/frai.2022.908261
45. Gsenger, R., and Strle, T. (2021). Trust, automation bias and aversion: Algorithmic decision-making in the context of credit scoring. Interdisciplinary Description of Complex Systems: INDECS, 19(4), 542–560. http://dx.doi.org/10.7906/indecs.19.4.7
46. Güneş, A. (2018). Türkiye’de Bilim Sanat Merkezleri. Avrasya Sosyal ve Ekonomi Araştırmaları Dergisi (ASEAD), 5(6), 185–193. https://izlik.org/JA59JF43BD
47. Guo, S., Shi, L., and Zhai, X. (2025). Developing and validating an instrument for teachers’ acceptance of artificial intelligence in education. Education and Information Technologies, 30(10), 13439–13461. https://doi.org/10.1007/s10639-025-13338-6
48. Hébert, C., Jenson, J., and Terzopoulos, T. (2021). “Access to technology is the major challenge”: Teacher perspectives on barriers to DGBL in K–12 classrooms. E-Learning and Digital Media, 18(3), 307–324. https://doi.org/10.1177/2042753021995315
49. Iskender, A. (2023). Holy or unholy? Interview with OpenAI’s ChatGPT. European Journal of Tourism Research, 34, Article 3414. https://doi.org/10.54055/ejtr.v34i.3169
50. Kamalov, F., Santandreu Calonge, D., and Gurrib, I. (2023). New era of artificial intelligence in education: Towards a sustainable multifaceted revolution. Sustainability, 15(16), 12451. https://doi.org/10.3390/su151612451
51. Kang, J., and Tolppanen, S. (2024). Exploring the role of science education as a catalyst for students’ willingness to take climate action. International Journal of Science Education, 2497–2515. https://doi.org/10.1080/09500693.2024.2393461
52. Kannan, P., and Zapata-Rivera, D. (2022). Facilitating the use of data from multiple sources for formative learning in the context of digital assessments: Informing the design and development of learning analytic dashboards. Frontiers in Education, 7, 913594. https://doi.org/10.3389/feduc.2022.913594
53. Karabey, B., Çakmak, A., and Günay, Y. (2026). An examination of mathematics teachers’ views on differentiated and individualized education programs for gifted students developed with artificial intelligence. Necatibey Faculty of Education Electronic Journal of Science and Mathematics Education, 20(1), 357–383. https://doi.org/10.17522/balikesirnef.1821050
54. Karaköse, T., Demirkol, M., Aslan, N., Köse, H., and Yirci, R. (2023). A conversation with ChatGPT about the impact of the COVID-19 pandemic on education: Comparative review based on human–AI collaboration. Educational Process: International Journal, 12(3), Article 1. https://doi.org/10.22521/edupij.2023.123.1
55. Karan, B., and Angadi, G. R. (2023). 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
56. Kelley, M., and Wenzel, T. (2025). Advancing artificial intelligence literacy in teacher education through professional partnership inquiry. Education Sciences, 15(6), 659. https://doi.org/10.3390/educsci15060659
57. Kotsis, K. T. (2024). Correcting students’ misconceptions in physics using experiments designed by ChatGPT. European Journal of Contemporary Education and E-Learning, 2(2), 83–100. https://doi.org/10.59324/ejceel.2024.2(2).07
58. Krullaars, Z. H., Januardani, A., Zhou, L., and Jonkers, E. (2023). Exploring initial interactions: High school students and generative AI chatbots for relationship development. In Mensch und Computer 2023 – Workshopband (pp. 415–418). Gesellschaft für Informatik. https://doi.org/10.18420/muc2023-mci-src-415
59. Kshetri, N. (2024). The future of education: generative artificial intelligence’s collaborative role with teachers. IT Professional, 25(6), 8-12. https://doi.org/10.1109/MITP.2023.3333070
60. Kuş, Z., and Mert, H. (2023). Does science education contribute to citizenship education in Turkey? Journal of International and Comparative Education, 12(2), 69–86. https://doi.org/10.14425/jice.2023.12.2.0913
61. Lee, Y. J., Davis, R. O., and Ryu, J. (2024). Korean in-service teachers’ perceptions of implementing artificial intelligence (AI) education for teaching in schools and their AI teacher training programs. International Journal of Information and Education Technology, 14(2), 214–219. https://doi.org/10.18178/ijiet.2024.14.2.2042
62. Lee, P., Bubeck, S., and Petro, J. (2023). Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. New England Journal of Medicine, 388(13), 1233–1239. https://doi.org/10.1056/NEJMsr2214184
63. Li, P. H., Kinshuk, and Huang, Y. M. (2026). Adaptive auto-prompting in inquiry-based learning: Enhancing curiosity and learning engagement to improve higher-order thinking. In W.-S. Wang, C.-F. Lai, Y.-M. Huang, F. E. Sandnes, & T. A. Sandtrø (Eds.), Innovative technologies and learning: 8th International Conference, ICITL 2025, proceedings (pp. 121–130). Springer. https://doi.org/10.1007/978-3-031-98185-2_14
64. Lin, X. F., Chen, L., Chan, K. K., Peng, S., Chen, X., Xie, S., ... and Hu, Q. (2022). Teachers’ perceptions of teaching sustainable artificial intelligence: A design frame perspective. Sustainability, 14(13), 7811. https://doi.org/10.3390/su14137811
65. Mahmoud, A. (2020). Artificial intelligence applications: An introduction to education development in the light of coronavirus pandemic COVID-19 challenges. International Journal of Research in Educational Sciences, 3(4), 171–224. http://iafh.net/index.php/IJRES/article/view/240
66. Mavroudi, A., Giannakos, M., and Krogstie, J. (2018). Supporting adaptive learning pathways through the use of learning analytics: Developments, challenges and future opportunities. Interactive Learning Environments, 26(2), 206–220. https://doi.org/10.1080/10494820.2017.1292531
67. Mazı, A., and Yıldırım, İ. O. (2025). Primary school teachers’ opinions on the use of artificial intelligence in educational practices. Social Sciences & Humanities Open, 11, 101576. https://doi.org/10.1016/j.ssaho.2025.101576
68. Mbalaka, B. (2023). Epistemically violent biases in artificial intelligence design: the case of DALLE-E 2 and Starry AI. Digital Transformation and Society, 2(4), 376-402. https://doi.org/10.1108/DTS-01-2023-0003
69. Miles, M. B., and Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Sage. MoNE (2019). Millî Eğitim Bakanlığı Bilim ve Sanat Merkezleri Yönergesi. Millî Eğitim Bakanlığı Özel Eğitim ve Rehberlik Hizmetleri Genel Müdürlüğü. https://orgm.meb.gov.tr/meb_iys_dosyalar/2025_11/11113603_milliegitimbakanligibilimvesanatmerkezleriyonergesi.pdf
70. Monteiro, F. F., Souza, P. V. S., da Silva, M. C., Maia, J. R., da Silva, W. F., and Girardi, D. (2024). ChatGPT in Brazilian K–12 science education. Frontiers in Education, 9, Article 1321547. https://doi.org/10.3389/feduc.2024.1321547 Moreno-Guerrero, A. J., López-Belmonte, J., Marín-Marín, J. A., and Soler-Costa, R. (2020). Scientific development of educational artificial intelligence in Web of Science. Future Internet, 12, 124. https://doi.org/10.3390/fi12080124
71. Neuman, L. W. (2017). Social research methods: Qualitative and quantitative approaches (Özge, S., Trans.). Yayın Odası.
72. Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., and Nguyen, B.-P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w
73. Nyaaba, M., and Zhai, X. (2024). Generative AI professional development needs for teacher educators. Journal of AI, 8(1), 1–13. https://doi.org/10.61969/jai.1385915
74. Önal, N., İlhan, A. G., and Önal, N. T. (2026). How ready are gifted education teachers for AI integration? Evidence from BILSEM in Türkiye. Education and Information Technologies, 31, 3007–3033. https://doi.org/10.1007/s10639-026-13932-2
75. Owan, V. J., Abang, K. B., Idika, D. O., Etta, E. O., and Bassey, B. A. (2023). Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia Journal of Mathematics, Science and Technology Education, 19(8), em2307. https://doi.org/10.29333/ejmste/13428
76. Paek, S., and Kim, N. (2021). Analysis of worldwide research trends on the impact of artificial intelligence in education. Sustainability, 13(14), 7941. https://doi.org/10.3390/su13147941
77. Patton, M. Q. (2002). Qualitative research and evaluation methods (3rd ed.). Sage Publications.
78. Pei, X., and Zheng, T. (2021). Comparative study on development of science education worldwide: Advance in ideas, themes and practice. Bulletin of Chinese Academy of Sciences (Chinese Version), 36(7), 771–778. https://doi.org/10.16418/j.issn.1000-3045.20210517001
79. Perla, L., Agrati, L. S., and Beri, A. (2025). Post-teaching and professional learning: An investigation on teachers’ attitudes towards AI. Professional Development in Education, 51(3), 466–477. https://doi.org/10.1080/19415257.2025.2465970
80. Perrotta, C., and Selwyn, N. (2020). Deep learning goes to school: Toward a relational understanding of AI in education. Learning, Media and Technology, 45(3), 251–269. https://doi.org/10.1080/17439884.2020.1686017
81. Sanusi, I. T., Olaleye, S. A., Agbo, F. J., and Chiu, T. K. (2022). The role of learners’ competencies in artificial intelligence education. Computers and Education: Artificial Intelligence, 3, 100098. https://doi.org/10.1016/j.caeai.2022.100098
82. Sarı, U., Pektaş, H. M., Şen, Ö. F., and Çelik, H. (2022). Algorithmic thinking development through physical computing activities with Arduino in STEM education. Education and Information Technologies, 27, 6669–6689. https://doi.org/10.1007/s10639-022-10893-0
83. Su, J., Zhong, Y., and Ng, D. T. K. (2022). A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers and Education: Artificial Intelligence, 3, Article 100065. https://doi.org/10.1016/j.caeai.2022.100065
84. Su, K. D. (2022). Implementation of innovative artificial intelligence cognitions with problem-based learning guided tasks to enhance students’ performance in science. Journal of Baltic Science Education, 21(2), 245–257. https://doi.org/10.33225/jbse/22.21.245
85. Sullivan, M., Kelly, A., and McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning & Teaching, 6(1), 1–10. https://jalt.open-publishing.org/index.php/jalt/article/view/731
86. Sumida, M. (2017). Science education for gifted learners. In K. S. Taber & B. Akpan (Eds.), Science education: An international course companion (pp. 479–491). Sense Publishers. https://doi.org/10.1007/978-94-6300-749-8_35
87. Tallvid, M. (2016). Understanding teachers’ reluctance to the pedagogical use of ICT in the 1:1 classroom. Education and Information Technologies, 21(3), 503–519. https://doi.org/10.1007/s10639-014-9335-7
88. Tanık Önal, N., and Büyük, U. (2021). Science education for gifted students: Opinions of students, parents, and teachers. European Journal of Educational Sciences, 8(1), 15–32. https://doi.org/10.19044/ejes.v8no1a15
89. Terzi, R. (2020). An adaptation of artificial intelligence anxiety scale into Turkish: Reliability and validity study. International Online Journal of Education and Teaching (IOJET), 7(4), 1501–1515. https://iojet.org/index.php/IOJET/article/view/1031
90. Törün, F., and Mentiş Köksoy, A. (2023). Perspectives of Science and Art Center (BİLSEM) teachers and students on distance education: The example of İzmir province. Journal of Qualitative Research in Education, (35). https://doi.org/10.14689/enad.35.1743
91. Tran, T. M., Bakajic, M., and Pullman, M. (2025). Teacher’s pet or rebel? Practitioners’ perspectives on the impacts of ChatGPT on course design. Higher Education, 90, 777–800. https://doi.org/10.1007/s10734-024-01350-7
92. Triplett, W. J. (2023). Artificial intelligence in STEM education. Cybersecurity and Innovative Technology Journal, 1(1), 23–29. https://doi.org/10.53889/citj.v1i1.296
93. Ülker, S., and Kocakülah, M. S. (2023). An analysis of the learning outcomes related to recognizing individual abilities in the BİLSEM science course curriculum based on the revised Bloom’s taxonomy. Dokuz Eylul University Journal of Buca Faculty of Education, (56), 929–948. https://doi.org/10.53444/deubefd.12est66
94. Valenzuela, J. M. (2025). Developing teachers’ AI literacy through professional development. In G. Morris & J. Ye (Eds.), Innovative approaches to staff development in transnational higher education (pp. 215–244). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-9102-0.ch009
95. Van Tassel-Baska, J., and Stambaugh, T. (2006). Comprehensive curriculum for gifted learners. Pearson Education.
96. Wahyono, I. D., Fadlika, I., Asfani, K., Putranto, H., Hammad, J., and Sunarti. (2019). New adaptive intelligence method for personalized adaptive laboratories. In 2019 International Conference on Electrical, Electronics and Information Engineering (ICEEIE) (pp. 196–200). IEEE. https://doi.org/10.1109/ICEEIE47180.2019.8981477
97. Yıldırım, A., ve Şimşek, H. (2018). Sosyal bilimlerde nitel araştırma yöntemleri (11. Baskı). Seçkin Yayıncılık.
98. Zawacki-Richter, O., Marín, V. I., Bond, M., and Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
99. Zhai, X. (2025). Transforming teachers’ roles and agencies in the era of generative AI: Perceptions, acceptance, knowledge, and practices. Journal of Science Education and Technology, 34, 1323–1333. https://doi.org/10.1007/s10956-024-10174-0
100. Zhai, X., Haudek, K. C., and Ma, W. (2023). Assessing argumentation using machine learning and cognitive diagnostic modeling. Research in Science Education, 53(2), 405–424. https://doi.org/10.1007/s11165-022-10062-w
101. Zhai, X.,and Krajcik, J. (2024). Artificial intelligence-based STEM education. In X. Zhai & J. Krajcik (Eds.), Uses of artificial intelligence in STEM education (pp. 3–14). Oxford University Press. https://doi.org/10.1093/oso/9780198882077.003.0001
102. Zhang, K., and Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025
103. Zhao, L., Wu, X., and Luo, H. (2022). Developing AI literacy for primary and middle school teachers in China: Based on a structural equation modeling analysis. Sustainability, 14(21), 14549. https://doi.org/10.3390/su142114549
104. Zulyusri, Z., Elfira, I., Lufri, L., and Santosa, T. A. (2023). Literature study: Utilization of the PjBL model in science education to improve creativity and critical thinking skills. Jurnal Penelitian Pendidikan IPA, 9(1), 133–143. https://jppipa.unram.ac.id/index.php/jppipa/article/view/2555/2003