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Artificial Intelligence in the Education of Teachers: A Qualitative Synthesis of the Cutting-Edge Research Literature

Yıl 2024, , 600 - 637, 21.10.2024
https://doi.org/10.18009/jcer.1477709

Öz

The integration of Artificial Intelligence (AI) into teacher education has been transformative, offering personalized learning experiences, enhanced professional development, and improved teaching methodologies. AI technologies such as Intelligent Tutoring Systems (ITS), AI-driven analytics, and automated assessment tools have become central to modern educational practices, significantly improving engagement, adaptability, and effectiveness. This study employs a qualitative thematic analysis of current literature on AI in teacher education, examining peer-reviewed articles and reports using thematic coding to identify key patterns, opportunities, and challenges. The findings reveal that AI enhances teacher education by providing personalized learning pathways, fostering critical thinking, and supporting ongoing professional growth. Technologies like ITS, Virtual Reality (VR), and AI-driven analytics have proven effective in promoting motivation and engagement among teachers. However, ethical challenges such as biases in AI systems and concerns regarding data privacy require continuous attention. Furthermore, a gap in teacher preparedness, particularly in developing AI literacy and integrating AI tools into classroom practices, is evident. Despite these challenges, AI offers substantial benefits, transforming teaching practices and enabling personalized, adaptive instruction that supports both teachers and students. The study emphasizes the need for comprehensive teacher training programs focusing on digital literacy and ethical AI use, ensuring educators can navigate an AI-enhanced educational environment effectively. This research contributes to the ongoing discourse by highlighting the need for ethical guidelines and robust teacher training programs, offering actionable insights for educators, policymakers, and institutions aiming to integrate AI into teacher education

Etik Beyan

Acknowledgement Due to the scope and method of the study, ethics committee permission was not required.

Kaynakça

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Artificial Intelligence in the Education of Teachers: A Qualitative Synthesis of the Cutting-Edge Research Literature

Yıl 2024, , 600 - 637, 21.10.2024
https://doi.org/10.18009/jcer.1477709

Öz

The integration of Artificial Intelligence (AI) into teacher education has been transformative, offering personalized learning experiences, enhanced professional development, and improved teaching methodologies. AI technologies such as Intelligent Tutoring Systems (ITS), AI-driven analytics, and automated assessment tools have become central to modern educational practices, significantly improving engagement, adaptability, and effectiveness. This study employs a qualitative thematic analysis of current literature on AI in teacher education, examining peer-reviewed articles and reports using thematic coding to identify key patterns, opportunities, and challenges. The findings reveal that AI enhances teacher education by providing personalized learning pathways, fostering critical thinking, and supporting ongoing professional growth. Technologies like ITS, Virtual Reality (VR), and AI-driven analytics have proven effective in promoting motivation and engagement among teachers. However, ethical challenges such as biases in AI systems and concerns regarding data privacy require continuous attention. Furthermore, a gap in teacher preparedness, particularly in developing AI literacy and integrating AI tools into classroom practices, is evident. Despite these challenges, AI offers substantial benefits, transforming teaching practices and enabling personalized, adaptive instruction that supports both teachers and students. The study emphasizes the need for comprehensive teacher training programs focusing on digital literacy and ethical AI use, ensuring educators can navigate an AI-enhanced educational environment effectively. This research contributes to the ongoing discourse by highlighting the need for ethical guidelines and robust teacher training programs, offering actionable insights for educators, policymakers, and institutions aiming to integrate AI into teacher education

Etik Beyan

Acknowledgement Due to the scope and method of the study, ethics committee permission was not required.

Kaynakça

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  • Salas-Pilco, S. Z. (2020). The impact of AI and robotics on physical, social‐emotional, and intellectual learning outcomes: An integrated analytical framework. British Journal of Educational Technology, 51(5), 1808–1825. https://doi.org/10.1111/bjet.12984
  • Sánchez-Prieto, J. C., Cruz-Benito, J., Therón, R., & García-Peñalvo, F. J. (2019). How to measure teachers’ acceptance of AI-driven assessment in eLearning. Proceedings of the 2019 ACM Symposium on Information Visualization in Data Science, 233–242. https://doi.org/10.1145/3362789.3362918
  • Santos, O. C. (2016). Training the body: The potential of AIED to support personalized motor skills learning. International Journal of Artificial Intelligence in Education, 26(2), 730–755. https://doi.org/10.1007/s40593-016-0103-2
  • Sapci, A., & Sapci, H. (2020). Artificial intelligence education and tools for medical and health informatics students: Systematic review. JMIR Medical Education, 6(1), e19285. https://doi.org/10.2196/19285
  • Schleiss, J. (2023). AI course design planning framework: Developing domain-specific AI education courses. Education Sciences, 13(9), 954. https://doi.org/10.3390/educsci13090954
  • Sedrakyan, G., Malmi, L., Verbert, K., Järvelä, S., & Kirschner, P. A. (2020). Integrating learning analytics into learning design: A systematic review and research agenda. Educational Technology and Society, 23(1), 58–76.
  • Şen, N., & Yıldız Durak, H. (2022). Examining the relationships between English teachers’ lifelong learning tendencies with professional competencies and technology integrating self-efficacy. Education and Information Technologies, 27(5), 5953–5988. https://doi.org/10.1007/s10639-021-10867-8
  • Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(1), 54. https://doi.org/10.1186/s41239-021-00292-9
  • Shahzad, A., Hussain, I., Ali, R., Valcke, M., & Khurshid, K. (2017). Typologies of didactical strategies and teachers’ pedagogical beliefs: A theoretical review. Eurasia Journal of Mathematics, Science and Technology Education, 13(10), 6645–6658. https://doi.org/10.12973/ejmste/78159
  • Shaik, T., Tao, X., Li, Y., Dann, C., McDonald, J., Redmond, P., & Galligan, L. (2022). A review of the trends and challenges in adopting natural language processing methods for education feedback analysis. IEEE Access, 10, 56720–56739.
  • Sharma, S., Singh, G., Islam, N., & Dhir, A. (2024). Why do SMEs adopt artificial intelligence-based chatbots? IEEE Transactions on Engineering Management, 71, 1773–1786.
  • Shih, S. C., Tsai, H. Y., & Chen, M. L. (2021). The effect of a one-on-one dialogue-based mathematical intelligent tutoring system for learning equivalent fractions. Proceedings of the 2021 International Conference on Educational Data Mining, 451–456.
  • Shin, S. (2021). A study on the framework design of artificial intelligence thinking for artificial intelligence education. International Journal of Information and Education Technology, 11(9), 392–397. https://doi.org/10.18178/ijiet.2021.11.9.1540
  • Su, J., & Yang, W. (2023). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education, 6(3), 355–366. https://doi.org/10.1177/20965311231168423
  • Sudjitjoon, W., Hengpraprohm, S., & Hengpraprohm, K. (2022). AI learning modules for elementary students. International Journal of Health Sciences, 12239(12249), 512. https://doi.org/10.53730/ijhs.v6nS4.11859
  • Tapalova, O., & Zhiyenbayeva, N. (2022). Artificial intelligence in education: AIED for personalised learning pathways. Electronic Journal of e-Learning, 20(5), 639–653. https://doi.org/10.34190/ejel.20.5.2597
  • Tondeur, J., van Braak, J., Siddiq, F., & Scherer, R. (2016). Time for a new approach to prepare future teachers for educational technology use: Its meaning and measurement. Computers and Education, 94, 134–150. https://doi.org/10.1016/j.compedu.2015.11.009
  • Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K-12: What should every child know about AI? Proceedings of the AAAI Conference on Artificial Intelligence, 33(1), 9795–9799. https://doi.org/10.1609/aaai.v33i01.33019795
  • Tubino, L., & Adachi, C. (2022). Developing feedback literacy capabilities through an AI-automated feedback tool. ASCILITE Publications. https://doi.org/10.14742/apubs.2022.39
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  • Webb, P., Sellar, S., & Gulson, K. (2019). Anticipating education: Governing habits, memories and policy-futures. Learning Media and Technology, 45(3), 284–297. https://doi.org/10.1080/17439884.2020.1686015
  • Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235.
  • Wu, C., Li, Y., Li, J., Zhang, Q., & Wu, F. (2021). Web-based platform for K-12 AI education in China. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15687–15694.
  • 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(1), 17–34.
  • Yan, X., & Song, B. (2015). An intelligent tutoring system based on EGL. Proceedings of the International Conference on Machine Learning and Computing, 23, 101–107. https://doi.org/10.2991/meici-15.2015.265
  • Yu, L., & Yu, Z. (2023). Qualitative and quantitative analyses of artificial intelligence ethics in education using VOS viewer and CitNet Explorer. Frontiers in Psychology, 14, 1061778. https://doi.org/10.3389/fpsyg.2023.1061778
  • Zawacki‐Richter, O., Marín, V. I., Bond, M., & 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(1), 1–27.
  • Zhang, H., Lee, I., Ali, S., DiPaola, D., Cheng, Y., & Breazeal, C. (2022). Integrating ethics and career futures with technical learning to promote AI literacy for middle school students: An exploratory study. International Journal of Artificial Intelligence in Education, 33(2), 1–35. https://doi.org/10.1007/s40593-022-00293-3
  • Zhang, J., & Zhang, Z.-M. (2023). Ethics and governance of trustworthy medical artificial intelligence. BMC Medical Informatics and Decision Making, 23(1), 7. https://doi.org/10.1186/s12911-023-02103-9
  • Zhao, L., Wu, X., & 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
Toplam 115 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Öğretim Teknolojileri, Öğretmen Eğitimi ve Eğitimcilerin Mesleki Gelişimi, Eğitim Teknolojisi ve Bilgi İşlem
Bölüm İnceleme Makalesi
Yazarlar

Rusen Meylani 0000-0002-3121-6088

Erken Görünüm Tarihi 23 Eylül 2024
Yayımlanma Tarihi 21 Ekim 2024
Gönderilme Tarihi 3 Mayıs 2024
Kabul Tarihi 22 Eylül 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Meylani, R. (2024). Artificial Intelligence in the Education of Teachers: A Qualitative Synthesis of the Cutting-Edge Research Literature. Journal of Computer and Education Research, 12(24), 600-637. https://doi.org/10.18009/jcer.1477709

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Standardizasyonun sağlanabilmesi ve YÖK ile birlikte yürütülecek ortak çalışmalarda ORCID kullanılacağı için, TR Dizin’de yer alan veya yer almak üzere başvuran dergilerin, yazarlardan ORCID bilgilerini talep etmeleri ve dergide/makalelerde bu bilgiye yer vermeleri tavsiye edilmektedir. ORCID, Open Researcher ve Contributor ID'nin kısaltmasıdır.  ORCID, Uluslararası Standart Ad Tanımlayıcı (ISNI) olarak da bilinen ISO Standardı (ISO 27729) ile uyumlu 16 haneli bir numaralı bir URI'dir. http://orcid.org adresinden bireysel ORCID için ücretsiz kayıt oluşturabilirsiniz. "