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Preparing Teachers for a ChatGPT-Influenced Workforce

Yıl 2025, Cilt: 33 Sayı: 2, 401 - 410, 25.04.2025
https://doi.org/10.24106/kefdergi.1683517

Öz

Purpose: In the 21st century, unique changes have become apparent in our lives. These changes have affected numerous fields, and education is no exception. Innovations in education have progressed rapidly, particularly with the prevalence of Artificial Intelligence (AI) applications. Higher education institutions play a crucial role in these developments due to their capacity to shape professional competencies and raise societal awareness. This study discusses the impact of AI applications on higher education and their significance in teacher training.
Design/Methodology/Approach: The study explores AI applications in higher education, particularly in preparing prospective teachers for AI-driven work environments. A review of literature and current educational practices was conducted to assess the integration of AI into teacher training programs. The study focuses on AI literacy, curriculum adaptation, and professional development.
Findings: AI applications have started to transform higher education by enhancing pedagogical methods, improving access to educational resources, and redefining the skill sets required for future educators. However, there remains a gap in AI literacy among pre-service teachers. Higher education institutions must incorporate AI-based courses and digital competencies to prepare teachers effectively.
Highlights: The study emphasizes the need for AI literacy in teacher training programs to ensure future educators can effectively utilize AI technologies in their profession. Recommendations include updating curricula, integrating AI-driven tools, and fostering interdisciplinary collaboration in higher education.

Kaynakça

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  • Ayyıldız, P., & Yılmaz, A. (2021). ‘Moving the kaleidoscope’ to see the effect of creative personality traits on creative thinking dispositions of preservice teachers: The mediating effect of creative learning environments and teachers’ creativity fostering behavior. Thinking Skills and Creativity, 41, 1-10. https://doi.org/10.1016/j.tsc.2021.100879
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  • Babic, I. D. (2017). Machine learning methods in predicting the student academic motivation. Croatian Operational Research Review, 8(2), 443–461.
  • Becker, S. A., Brown, M., Dahlstrom, E., Davis, A., DePaul, K., Diaz, V., & Pomerantz, J. (2018). NMC Horizon Report: 2018 Higher Education Edition. Educause. https://library.educause.edu/~/media/files/library/2018/8/2018horizonreport.pdf
  • Bhargava, A., Bester, M., & Bolton, L. (2021). Employees’ perceptions of the implementation of robotics, artificial intelligence, and automation (RAIA) on job satisfaction, job security, and employability. Journal of Technology in Behavioral Science, 6(1), 106–113.
  • Borenstein, J., & Howard, A. (2021). Emerging challenges in AI and the need for AI ethics education. AI and Ethics, 1(1), 61-65. https://doi.org/10.1007/s43681-020-00002-7
  • Brouillette, M. (2019). AI added to the curriculum for doctors-to-be. Nature Medicine, 25 (12), 1808–1809.
  • Buckingham Shum, S., Ferguson, R., & Martinez-Maldonado, R. (2019). Human-centred learning analytics. Journal of Learning Analytics, 6(2), 1–9.
  • Buckingham Shum, S., & Deakin Crick, R. (2016). Learning Analytics for 21st century competencies. Journal of Learning Analytics, 3(2), 6–21. https://doi.org/10.18608/jla.2016.32.2
  • Cantú-Ortiz, F. J., Galeano S´anchez, N., Garrido, L., Terashima-Marin, H., & Brena, R. F. (2020). An artificial intelligence educational strategy for the digital transformation. International Journal on Interactive Design and Manufacturing, 14, 1195–1209. https://doi.org/10.1007/s12008-020-00702-8
  • Chatterjee, J., & Dethlefs, N. (2023). This new conversational AI model can be your friend, philosopher, and guide … and even your worst enemy. Pattern, 4(1), 1-11. https://doi.org/10.1016/j.patter.2022.100676
  • Cheddadi, S., & Bouache, M. (2021). Improving equity and access to higher education using artificial intelligence. In The 16th international Conference on computer science & education (ICCSE 2021) (pp. 18–20). August 2021 (Online).
  • Chen, X., Xie, H., & Hwang, G.-J. (2020). A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers & Education: Artificial Intelligence, 1, 1-11. https://doi.org/10.1016/j.caeai.2020.100005
  • Chi, M., VanLehn, K., Litman, D., & Jordan, P. (2011). Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies. User Modeling and User-Adapted Interaction, 21(1), 137–180.
  • Chounta, I.-A., Bardone, E., Raudsep, A., & Pedaste, M. (2022). Exploring teachers’ perceptions of Artificial Intelligence as a tool to support their practice in Estonian K-12 education. International Journal of Artificial Intelligence in Education, 32(3),725–755.
  • Corwin, L. A., Graham, M. J., & Dolan, E. L. (2017). Modeling course-based undergraduate research experiences: An agenda for future research and evaluation. Life Sciences Education, 14, 1-13. https://doi.org/10.1187/cbe.14-10-0167
  • Dai, Y., Chai, C. S., Lin, P. Y., Jong, M. S. Y., Guo, Y., & Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12(16), 6597. https://doi.org/10.3390/su12166597
  • Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
  • Dibble, M. (2023). Schools ban ChatGPT amid fears of artificial intelligence-assisted cheating. VOA News. Available at: https://www.voanews.com/a/schools-ban-chatgpt-amid-fears-of-artificial-intelligence-assisted-cheating/6949800.html
  • Duffy, M. C., & Azevedo, R. (2015). Motivation matters: Interactions between achievement goals and agent scaffolding for selfregulated learning within an intelligent tutoring system. Computers in Human Behavior, 52, 338–348.
  • Felix, C. V. (2020). The role of the teacher and AI in education. In International perspectives on the role of technology in humanizing higher education. Emerald Publishing Limited.
  • Hammer, A. (2023). The rise of the machines? ChatGPT CAN pass US medical licensing exam and the bar. Experts Warn – After the AI Chatbot Received B Grade on Wharton MBA Paper. Daily Mail. Available at: https://www.dailymail.co.uk/news/article-11666429/ChatGPT-pass-United-States-Medical-Licensing-Exam-Bar- Exam.html
  • Holden, O. L., Norris, M. E., & Kuhlmeier, V. A. (2021). Academic integrity in online assessment: A research review. Frontiers in Education, 6, 1-8. https://doi.org/10.3389/feduc.2021.639814
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Öğretmenleri ChatGPT'den Etkilenen İşgücüne Hazırlamak

Yıl 2025, Cilt: 33 Sayı: 2, 401 - 410, 25.04.2025
https://doi.org/10.24106/kefdergi.1683517

Öz

Çalışmanın Amacı: 21. yüzyılda yaşamımızda önemli değişimler meydana gelmiştir. Bu değişimler birçok alanı etkilediği gibi, eğitim alanında da önemli dönüşümler yaşanmıştır. Yapay zekâ uygulamalarının yaygınlaşmasıyla birlikte, eğitimde inovasyon hız kazanmıştır. Yükseköğretim kurumları, profesyonel becerileri şekillendirme ve toplumsal farkındalık oluşturma konularında kritik bir rol üstlenmektedir. Bu çalışma, yükseköğretimde yapay zekâ uygulamalarının etkisini ve öğretmen eğitimi açısından önemini ele almaktadır.
Materyal ve Yöntem: Çalışmada, yükseköğretimde yapay zekâ uygulamalarının öğretmen yetiştirme sürecine entegrasyonu incelenmiştir. Literatür taraması ve mevcut eğitim uygulamalarının analizi yapılarak, öğretmen adaylarını yapay zekâ destekli bir iş gücüne hazırlamak için gerekli stratejiler değerlendirilmiştir. Yapay zekâ okuryazarlığı, müfredat adaptasyonu ve mesleki gelişim odak noktaları olarak ele alınmıştır.
Bulgular: Yapay zekâ uygulamaları, yükseköğretimde pedagojik yöntemleri geliştirerek, eğitim kaynaklarına erişimi artırarak ve öğretmenlerin sahip olması gereken becerileri yeniden tanımlayarak önemli bir dönüşüm sağlamaktadır. Ancak, öğretmen adayları arasında yapay zekâ okuryazarlığı konusunda hala eksiklikler bulunmaktadır. Yükseköğretim kurumlarının, öğretmenleri daha etkin bir şekilde hazırlayabilmesi için yapay zekâ temelli dersleri ve dijital yeterlilikleri müfredata dahil etmesi gerekmektedir.
Önemli Vurgular: Çalışma, öğretmen eğitimi programlarında yapay zekâ okuryazarlığının gerekliliğini vurgulamaktadır. Geleceğin eğitimcilerinin yapay zekâ teknolojilerini etkin bir şekilde kullanabilmesi için müfredatın güncellenmesi, yapay zekâ destekli araçların entegrasyonu ve disiplinler arası iş birliğinin teşvik edilmesi önerilmektedir.

Kaynakça

  • ABC News. (2023). Queensland to join NSW in banning access to ChatGPT in state schools. ABC News. Available at: https://www.abc.net.au/news/2023-01-23/queensland-to-join-nsw-in-banning-access-to/101884288
  • Ahmad, T. (2019). Scenario based approach to re-imagining future of higher education which prepares students for the future of work. Higher Education, Skills and Work-based Learning, 10(1), 217–238.
  • Ayyıldız, P., & Yılmaz, A. (2021). ‘Moving the kaleidoscope’ to see the effect of creative personality traits on creative thinking dispositions of preservice teachers: The mediating effect of creative learning environments and teachers’ creativity fostering behavior. Thinking Skills and Creativity, 41, 1-10. https://doi.org/10.1016/j.tsc.2021.100879
  • Ayyıldız, P., & Yılmaz, A. (2023). Effective school management: Leadership capacity of the school principal. D. Outhwaite & C.A. Simon (Edts.). In Leadership and Management for Education Studies: Introducing Key Concepts of Theory and Practice (pp.46-58). London and New York: Routledge.
  • Babic, I. D. (2017). Machine learning methods in predicting the student academic motivation. Croatian Operational Research Review, 8(2), 443–461.
  • Becker, S. A., Brown, M., Dahlstrom, E., Davis, A., DePaul, K., Diaz, V., & Pomerantz, J. (2018). NMC Horizon Report: 2018 Higher Education Edition. Educause. https://library.educause.edu/~/media/files/library/2018/8/2018horizonreport.pdf
  • Bhargava, A., Bester, M., & Bolton, L. (2021). Employees’ perceptions of the implementation of robotics, artificial intelligence, and automation (RAIA) on job satisfaction, job security, and employability. Journal of Technology in Behavioral Science, 6(1), 106–113.
  • Borenstein, J., & Howard, A. (2021). Emerging challenges in AI and the need for AI ethics education. AI and Ethics, 1(1), 61-65. https://doi.org/10.1007/s43681-020-00002-7
  • Brouillette, M. (2019). AI added to the curriculum for doctors-to-be. Nature Medicine, 25 (12), 1808–1809.
  • Buckingham Shum, S., Ferguson, R., & Martinez-Maldonado, R. (2019). Human-centred learning analytics. Journal of Learning Analytics, 6(2), 1–9.
  • Buckingham Shum, S., & Deakin Crick, R. (2016). Learning Analytics for 21st century competencies. Journal of Learning Analytics, 3(2), 6–21. https://doi.org/10.18608/jla.2016.32.2
  • Cantú-Ortiz, F. J., Galeano S´anchez, N., Garrido, L., Terashima-Marin, H., & Brena, R. F. (2020). An artificial intelligence educational strategy for the digital transformation. International Journal on Interactive Design and Manufacturing, 14, 1195–1209. https://doi.org/10.1007/s12008-020-00702-8
  • Chatterjee, J., & Dethlefs, N. (2023). This new conversational AI model can be your friend, philosopher, and guide … and even your worst enemy. Pattern, 4(1), 1-11. https://doi.org/10.1016/j.patter.2022.100676
  • Cheddadi, S., & Bouache, M. (2021). Improving equity and access to higher education using artificial intelligence. In The 16th international Conference on computer science & education (ICCSE 2021) (pp. 18–20). August 2021 (Online).
  • Chen, X., Xie, H., & Hwang, G.-J. (2020). A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers & Education: Artificial Intelligence, 1, 1-11. https://doi.org/10.1016/j.caeai.2020.100005
  • Chi, M., VanLehn, K., Litman, D., & Jordan, P. (2011). Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies. User Modeling and User-Adapted Interaction, 21(1), 137–180.
  • Chounta, I.-A., Bardone, E., Raudsep, A., & Pedaste, M. (2022). Exploring teachers’ perceptions of Artificial Intelligence as a tool to support their practice in Estonian K-12 education. International Journal of Artificial Intelligence in Education, 32(3),725–755.
  • Corwin, L. A., Graham, M. J., & Dolan, E. L. (2017). Modeling course-based undergraduate research experiences: An agenda for future research and evaluation. Life Sciences Education, 14, 1-13. https://doi.org/10.1187/cbe.14-10-0167
  • Dai, Y., Chai, C. S., Lin, P. Y., Jong, M. S. Y., Guo, Y., & Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12(16), 6597. https://doi.org/10.3390/su12166597
  • Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
  • Dibble, M. (2023). Schools ban ChatGPT amid fears of artificial intelligence-assisted cheating. VOA News. Available at: https://www.voanews.com/a/schools-ban-chatgpt-amid-fears-of-artificial-intelligence-assisted-cheating/6949800.html
  • Duffy, M. C., & Azevedo, R. (2015). Motivation matters: Interactions between achievement goals and agent scaffolding for selfregulated learning within an intelligent tutoring system. Computers in Human Behavior, 52, 338–348.
  • Felix, C. V. (2020). The role of the teacher and AI in education. In International perspectives on the role of technology in humanizing higher education. Emerald Publishing Limited.
  • Hammer, A. (2023). The rise of the machines? ChatGPT CAN pass US medical licensing exam and the bar. Experts Warn – After the AI Chatbot Received B Grade on Wharton MBA Paper. Daily Mail. Available at: https://www.dailymail.co.uk/news/article-11666429/ChatGPT-pass-United-States-Medical-Licensing-Exam-Bar- Exam.html
  • Holden, O. L., Norris, M. E., & Kuhlmeier, V. A. (2021). Academic integrity in online assessment: A research review. Frontiers in Education, 6, 1-8. https://doi.org/10.3389/feduc.2021.639814
  • Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2021). Ethics of AI in edu¬cation: Towards a community-wide framework. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-021-00239-1
  • Hu, K. (2023). ChatGPT sets record for fastest-growing user base. Reuters. Available at: https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/
  • Hu, Y., Li, W., Wright, D., Aydin, O., Wilson, D., Maher, O., & Raad, M. (2019). Artificial intelligence approaches. In J. P. Wilson (Ed.). The Geographic Information Science & Technology Body of Knowledge. https://doi.org/10.22224/gistbok/2019.3.4
  • Jaeger, C. (2023). AI tool banned in Victorian state schools. The Age. Available at: https://www.theage.com.au/national/victoria/ai-tool-banned-in-victorian-schools-as-implications-examined-20230201-p5ch8h.html
  • Jalal, S., Parker, W., Ferguson, D., & Nicolaou, S. (2021). Exploring the role of artificial intelligence in an emergency and trauma radiology department. Canadian Association of Radiologists Journal, 72(1), 167-174. https://doi.org/10.1177/0846537120918338
  • Jansen, S., & Martin, B. (2015). The Streisand effect and censorship backfire. International Journal of Communication, 9, 656–671.
  • Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or not, AI comes—an interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63 (1), 5–20.
  • Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016). Artificial intelligence and computer science in education: From kindergarten to university. IEEE Frontiers in Education Conference (FIE), 1–9. https://doi.org/10.1109/FIE.2016. 7757570
  • Karaca, O., Çalışkan, S. A., & Demir, K. (2021). Medical artificial intelligence readiness scale for medical students (MAIRS-MS)–development, validity and reliability study. BMC Medical Education, 21(1), 1–9.
  • Koehler, M. J., Mishra, P., & Cain, W. (2013). What is technological pedagogical content knowledge (TPACK)? Journal of Education, 193(3), 13–19.
  • Kong, S. C., Ogata, H., Shih, J. L., & Biswas, G. (2021). The role of Artificial Intelligence in STEM education, in: Proceedings of 29th International Conference on Computers in Education 7 Conference, Asia-Pacific Society for Computers in Education, Taoyuan City, pp. 774–776.
  • Larson, L. C., & Miller, T. N. (2011). 21st century skills: Prepare students for the future. Kappa Delta Pi Record, 47(3), 121–123.
  • Leander, K. M., & Burriss, S. K. (2020). Critical literacy for a posthuman world: When people read, and become, with machines. British Journal of Educational Technology, 51(4), 1262–1276.
  • Lim, W. M., Chin, M. W. C., Ee, Y. S., Fung, C. Y., Giang, C. S., Heng, K. S., … Weissmann, M. A. (2022). What is at stake in a war? A prospective evaluation of the Ukraine and Russia conflict for business and society. Global Business and Organizational Excellence, 41(6), 23–36. https://doi.org/10.1002/joe.22162
  • Lim, W. M., Günasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 1-13. https://doi.org/10.1016/j.ijme.2023.100790
  • Long, D., & Megerko, B. (2020). What is AI literacy? Competencies and design considerations. In CHI ’20: Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1–16). https://doi.org/10.1145/3313831.3376727
  • Luckin, R., Cukurova, M., Kent, C., & du Boulay, B. (2022). Empowering educators to be AI-ready. Computers & Education: Artificial Intelligence, 3, 1-11. https://doi.org/10.1016/j.caeai.2022.100076
  • Lukpat, A. (2023). ChatGPT banned in New York City public schools over concerns about cheating, learning development. The Wall Street Journal. Available at: https://www.wsj.com/articles/chatgpt-banned-in-new-york-city-public-schools-over-concerns-about-cheating-learning-development-11673024059
  • Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S., Tondeur, J., De Laat, M., Buckingham, S., Dragan Gaˇsevi´c, S., & Siemens, G. (2022). Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI? Computers & Education: Artificial Intelligence, 3, 1-16. https://doi.org/10.1016/j.caeai.2022.100056
  • National Security Commission on Artificial Intelligence (NSCAI). (2021). Final report. Retrieved from https://www.nscai.gov/2021-final-report/ (Accessed 21 June 2023).
  • Nature. (2023). Tools such as ChatGPT threaten transparent science; here are our ground rules for their use. Nature, 613, 612. https://doi.org/10.1038/d41586-023- 00191-1
  • Nazaretsky, T., Cukurova, M., Ariely, M., & Alexandron, G. (2021). Confirmation bias and trust: Human factors that influence teachers’ attitudes towards AI-based educational technology. In , Vol. 3042. CEUR workshop proceedings.
  • Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers & Education: Artificial Intelligence, 2, 1-11. https://doi.org/10.1016/j.caeai.2021.100041
  • O’Connor, S., & ChatGPT. (2023). Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice, 66, 1-14. https://doi.org/10.1016/j.nepr.2022.103537
  • OpenAI. (2023). OpenAI. Available at: https://openai.com/.
  • Pavlik, J. V. (2023). Collaborating with ChatGPT: Considering the implications of generative artificial intelligence for journalism and media education. Journalism and Mass Communication Educator. https://doi.org/10.1177/10776958221149577
  • Pechenkina, K. (2023). Artificial intelligence for good? Challenges and possibilities of AI in higher education from a data justice perspective. In L. Czerniewicz, & C. Cronin (Eds.), Higher Education for good: Teaching and learning futures (#HE4Good). Cambridge, UK: Open Book Publishers.
  • PEGA. (2022). 101 artificial intelligence statistics. Retrieved from https://techjury. net/blog/aistatistics/#gref. (Accessed 15 June 2023).
  • Robinson, S. C. (2020). Trust, transparency, and openness: How inclusion of cultural values shapes Nordic national public policy strategies for artificial intelligence (AI). Technology in Society, 63, 1-15.
  • Rodríguez-García, J. D., Moreno-Le´on, J., Rom´an-Gonz´alez, M., & Robles, G. (2021, March). Evaluation of an online intervention to teach artificial intelligence with LearningML to 10-16-year-old students. In Proceedings of the 52nd ACM technical symposium on computer science education (pp. 177–183). ACM.
  • Russell Stuart, J., & Norvig, P. (2009). Artificial intelligence: A modern approach. Prentice Hall.
  • Rychen, D. S. E., & Salganik, L. H. E. (2003). Key competencies for a successful life and a well-functioning society. Cambridge, MA: Hogrefe & Huber Publishers.
  • Southworth, J., Migliaccio, K., Glover, J., Glover, J., Reed, D., McCarty, C., Brendemuhl, J., & Thomas, A. (2023). Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, 1-10. https://doi.org/10.1016/j.caeai.2023.100127
  • Su, J., Zhong, Y., & 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 & Education: Artificial Intelligence, 3, 1-18. https://doi.org/10.1016/j.caeai.2022.100065
  • St Louis, A. T., Thompson, P., Sulak, T. N., Harvill, M. L., & Moore, M. E. (2021). Infusing 21st century skill development into the undergraduate curriculum: The formation of the iBEARS network. Journal of Microbiology & Biology Education, 22(2), 1-8. https://doi.org/10.1128/jmbe.00180-21
  • Stokel-Walker, C. (2022). AI bot ChatGPT writes smart essays-should professors worry? Nature. https://doi.org/10.1038/d41586-022-04397-7
  • Terwiesch, C. (2023). Would Chat GPT3 get a Wharton MBA? A prediction based on its performance in the operations management. The Wharton School of the University of Pennsylvania. Available at: https://mackinstitute.wharton.upenn.edu/wp-content/uploads/2023/01/Christian-Terwiesch-Chat-GTP.pdf
  • Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019, July). Envisioning AI for K-12: What should every child know about AI?. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, pp. 9795–9799). No. 01.
  • University, M. (2023). Acknowledging the use of generative artificial intelligence. Available at:https://www.monash.edu/learnhq/builddigitalcapabilities/createonline/acknowledging-the-use-of-generative-artificial-intelligence
  • Vincent-Lancrin, S., & Van der Vlies, R. (2020). Trustworthy artificial intelligence (AI) in education: Promises and challenges. OECD.
  • Weston-Sementelli, J. L., Allen, L. K., & McNamara, D. S. (2018). Comprehension and writing strategy training improves performance on content-specific source-based writing tasks. International Journal of Artificial Intelligence in Education, 28(1), 106–137.
  • Wong, G. K., Ma, X., Dillenbourg, P., & Huan, J. (2020). Broadening artificial intelligence education in K-12: Where to start? ACM Inroads, 11(1), 20–29.
  • World Economic Forum. (2022). Global issue: Artificial intelligence. Curation: Desautels Faculty of Management, McGill University. Retrieved from: Strategic Intelligence we forum.org. (Accessed 28 May 2023).
  • Yang, Q., et al. (2018). Grounding interactive machine learning tool design in how non-experts actually build models. ACM DISC, 573–584.
  • Zhai, X. (2022). ChatGPT user experience: Implications for education. Available at SSRN 4312418.
Toplam 70 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yükseköğretim Çalışmaları (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

İsmail Helvacı 0000-0002-5778-4278

Gönderilme Tarihi 1 Ekim 2024
Kabul Tarihi 20 Nisan 2025
Yayımlanma Tarihi 25 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 33 Sayı: 2

Kaynak Göster

APA Helvacı, İ. (2025). Preparing Teachers for a ChatGPT-Influenced Workforce. Kastamonu Education Journal, 33(2), 401-410. https://doi.org/10.24106/kefdergi.1683517

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