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

Year 2024, Volume: 12 Issue: 24, 600 - 637, 21.10.2024

Rusen Meylani

https://doi.org/10.18009/jcer.1477709

Abstract

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

Keywords

Artificial intelligence, teacher education, professional development, pedagogy, intelligent tutoring systems, AI-driven analytics, digital literacy

Ethical Statement

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

References

• Akram, H., Abdelrady, A. H., Al-Adwan, A. S., & Ramzan, M. (2022). Teachers’ perceptions of technology integration in teaching-learning practices: A systematic review. Frontiers in Psychology, 13, 920317. https://doi.org/10.3389/fpsyg.2022.920317
• Alasadi, E. A., & Baiz, C. R. (2023). Generative AI in education and research: Opportunities, concerns, and solutions. Journal of Chemical Education, 100(8), 2965–2971. https://doi.org/10.1021/acs.jchemed.3c00323
• Alenezi, F. (2023). Artificial intelligence versus Arab universities: An enquiry into the Saudi context. Humanities and Management Sciences – Scientific Journal of King Faisal University, 1–7. https://doi.org/10.37575/h/edu/220038
• 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
• Alotaibi, N., & Alshehri, A. (2023). Prospects and obstacles in using artificial intelligence in Saudi Arabia higher education institutions—the potential of AI-based learning outcomes. Sustainability, 15(13), 10723. https://doi.org/10.3390/su151310723
• Amanda, F. F., Sumitro, S. B., Lestari, S. R., & Ibrohim, I. (2022). The correlation of critical thinking and concept mastery to problem-solving skills: The role of complexity science-problem based learning model. Pedagogika, 146(2), 80–94. https://doi.org/10.15823/p.2022.146.4
• Amante, L., Batista de Souza, E., Quintas-Mendes, A., Monteiro, A. F., Miranda-Pinto, M., Osório, A., & Araújo, C. (2019). Computational thinking, programming, and robotics in basic education: Evaluation of an in-service teacher’s training b-learning experience. ICERI Proceedings, 10698–10705. https://doi.org/10.21125/iceri.2019.2626
• Baskoro, G. (2023). Innovation to improve critical thinking skills in Generation Z using peeragogy as a learning approach and artificial intelligence (AI) as a tool. Jurnal Teknik Industri, 25(2), 121–130. https://doi.org/10.9744/jti.25.2.121-130
• Benhayoun, L., & Lang, D. (2021). Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps’ identification using text mining. Human Systems Management, 40(5), 639–651. https://doi.org/10.3233/HSM-211179
• Betaubun, M. (2023). Personalized pathways to proficiency: Exploring the synergy of adaptive learning and artificial intelligence in English language learning. Technium Romanian Journal of Applied Sciences and Technology, 17, 60–66. https://doi.org/10.47577/technium.v17i.10047
• Boscardin, C. K., Gin, B., Golde, P. B., & Hauer, K. E. (2024). ChatGPT and generative artificial intelligence for medical education: Potential impact and opportunity. Academic Medicine, 99(1), 22–27. https://doi.org/10.1097/ACM.0000000000005439
• Bridges, S., Yiu, C. K. Y., & Botelho, M. G. (2016). Design considerations for an integrated, problem-based curriculum. Medical Science Educator, 26(3), 365–373. https://doi.org/10.1007/s40670-016-0255-6
• Bulathwela, S., Pérez-Ortiz, M., Holloway, C., & Shawe-Taylor, J. (2021). Could AI democratise education? Socio-Technical imaginaries of an Ed-Tech. Revolution. https://doi.org/10.48550/arxiv.2112.02034
• Burney, I. A., & Ahmad, N. (2022). Artificial intelligence in medical education: A citation-based systematic literature review. Journal of Shifa Tameer-E-Millat University, 5(1), 43–53. https://doi.org/10.32593/jstmu/Vol5.Iss1.183
• Cadiz, A. P. (2022). Pre-service teachers’ reflective practice and their teaching practicum beliefs. Jurnal Inovatif Ilmu Pendidikan, 3(2), 105–119. https://doi.org/10.23960/jiip.v3i2.22854
• Caner, M., & Aydin, S. (2021). Self-efficacy beliefs of preservice teachers on technology integration. Turkish Online Journal of Distance Education, 79–94. https://doi.org/10.17718/tojde.961820
• Casal-Otero, 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(1). https://doi.org/10.1186/s40594-023-00418-7
• Charow, R., Jeyakumar, T., Younus, S., Dolatabadi, E., Salhia, M., Al-Mouaswas, D., & Wiljer, D. (2021). Artificial intelligence education programs for health care professionals: Scoping review. JMIR Medical Education, 7(4), e31043. https://doi.org/10.2196/31043
• Chaudhry, M., Cukurova, M., & Luckin, R. (2022). A transparency index framework for AI in education. https://doi.org/10.35542/osf.io/bstcf
• Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
• Chiu, T. K. F., & 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
• Chiu, T. K. F., Meng, H., Chai, C.-S., King, I., Wong, S., & 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
• Cukurova, M., Kent, C., & Luckin, R. (2019). Artificial intelligence and multimodal data in the service of human decision‐making: A case study in debate tutoring. British Journal of Educational Technology, 50(6), 3032–3046. https://doi.org/10.1111/bjet.12829
• Dergunova, Y., Aubakirova, R. Z., Yelmuratova, B. Z., Gulmira, T. M., Yuzikovna, P. N., & Antikeyeva, S. (2022). Artificial intelligence awareness levels of students. International Journal of Emerging Technologies in Learning (IJET), 17(18), 26–37. https://doi.org/10.3991/ijet.v17i18.32195
• Dimitriadou, E., & 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). https://doi.org/10.1186/s40561-023-00231-3
• Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., ... & Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
• Ekşi, H., Özgenel, M., & Metlilo, E. (2020). The effect of motivation of success of university students on personal-professional competence: Mediation role of lifelong learning tendency. International Journal of Evaluation and Research in Education, 9(3), 583–591. https://doi.org/10.11591/ijere.v9i3.20664
• Fahimirad, M., & Kotamjani, S. S. (2018). A review on the application of artificial intelligence in teaching and learning in educational contexts. International Journal of Learning and Development, 8(4), 106–116. https://doi.org/10.5296/ijld.v8i4.14057
• Faria, L., Silva, A., Vale, Z., & Marques, A. (2009). Training control centers' operators in incident diagnosis and power restoration using intelligent tutoring systems. IEEE Transactions on Learning Technologies, 2(2), 135–147. https://doi.org/10.1109/TLT.2009.16
• Fidan, M., & Gencel, N. (2022). Supporting instructional videos with chatbot and peer feedback mechanisms in online learning: Effects on learning performance and intrinsic motivation. Journal of Educational Computing Research, 60(7), 1716–1741. https://doi.org/10.1177/07356331221077901
• Fryberg, S. A., & Markus, H. R. (2007). Cultural models of education in American Indian, Asian American, and European American contexts. Social Psychology of Education, 10(2), 213–246. https://doi.org/10.1007/s11218-007-9017-z
• Ghanim, H. A. A., & Kovács, L. (2021). Development of an ontology-based model to support the learning process in LMS. Indonesian Journal of Electrical Engineering and Computer Science, 24(1), 507–518. https://doi.org/10.11591/ijeecs.v24.i1.pp507-518
• Ghnemat, R., Shaout, A., & Al-Sowi, A. M. (2022). Higher education transformation for artificial intelligence revolution: A transformation framework. International Journal of Emerging Technologies in Learning (IJET), 17(19), 224–241. https://doi.org/10.3991/ijet.v17i19.33309
• Hamal, O., El Faddouli, N. E., Harouni, M. H. A., & Lu, J. (2022). Artificial intelligence in education. Sustainability, 14(5), 2862. https://doi.org/10.3390/su14052862
• Heng, T., & Tabunshchyk, V. (2021). Teaching tech to talk: K-12 conversational artificial intelligence literacy curriculum and development tools. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15655–15663. https://doi.org/10.1609/aaai.v35i17.17844
• Humble, N. (2023). Need-based game design: Developing a CT game based on educational needs. European Conference on Games Based Learning, 17(1), 269–277. https://doi.org/10.34190/ecgbl.17.1.1489
• Huriye, A. Z. (2023). The ethics of artificial intelligence: Examining the ethical considerations surrounding the development and use of AI. American Journal of Technology, 2(1), 37–45. https://doi.org/10.58425/ajt.v2i1.142
• Jendia, J. (2023). Developing personalised reading materials for Malaysian primary school pupils using ChatGPT: A review. International Journal of Academic Research in Business and Social Sciences, 13(12). https://doi.org/10.6007/ijarbss/v13-i12/20172
• Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
• Kang, Y., Forkan, A., Jayaraman, P., Wieland, N., Kollias, E., Du, H., & Li, Y. (2021). An AI-based solution for enhancing the delivery of digital learning for future teachers. arXiv. https://doi.org/10.48550/arxiv.2112.01229
• Kim, C. S., Samaniego, C. S., Sousa Melo, S. L., Brachvogel, W. A., Baskaran, K., & Rulli, D. (2023). Artificial intelligence (AI) in dental curricula: Ethics and responsible integration. Journal of Dental Education, 87(11), 1570–1573. https://doi.org/10.1002/jdd.13337
• Kim, J., Lee, H., & Cho, Y. H. (2022). Learning design to support student-AI collaboration: Perspectives of leading teachers for AI in education. Education and Information Technologies, 27(5), 6069–6104. https://doi.org/10.1007/s10639-021-10831-6
• Köbis, L., & Mehner, C. (2021). Ethical questions raised by AI-supported mentoring in higher education. Frontiers in Artificial Intelligence, 4, 624050. https://doi.org/10.3389/frai.2021.624050
• Komasawa, N., & Yokohira, M. (2023). Simulation-based education in the artificial intelligence era. Cureus, 15(9), e40940. https://doi.org/10.7759/cureus.40940
• Kreinsen, M., & Schulz, S. (2023). Towards the triad of digital literacy, data literacy, and AI literacy in teacher education – A discussion in light of the accessibility of novel generative AI. OSF. https://doi.org/10.35542/osf.io/xguzk
• Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M. D., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424. https://doi.org/10.3390/su131810424
• Kuo, P.-C., Huang, J.-H., & Liu, M.-D. (2011). Avian influenza risk perception and preventive behavior among traditional market workers and shoppers in Taiwan: Practical implications for prevention. PLOS ONE, 6(9), e24157. https://doi.org/10.1371/journal.pone.0024157
• Lebovitz, S., Lifshitz-Assaf, H., & Levina, N. (2022). To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis. Organization Science, 33(1), 126–148. https://doi.org/10.1287/orsc.2021.1549
• Lee, I., & Perret, B. (2022). Preparing high school teachers to integrate AI methods into STEM classrooms. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12783–12791. https://doi.org/10.1609/aaai.v36i11.21557
• Lee, S., Mott, B., Ottenbreit-Leftwich, A., Scribner, A., Taylor, S., Park, K., Rowe, J., Glazewski, K., Hmelo-Silver, C. E., & Lester, J. (2021). AI-infused collaborative inquiry in upper elementary school: A game-based learning approach. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15591–15599. https://doi.org/10.1609/aaai.v35i17.17836
• Levanova, E., Galustyan, O., Seryakova, S., Pushkareva, T., Serykh, A., & Yezhov, A. (2020). Students' project competency within the framework of STEM education. International Journal of Emerging Technologies in Learning (iJET), 15(21), 268. https://doi.org/10.3991/ijet.v15i21.15933
• Lin, H. (2022). Influences of artificial intelligence in education on teaching effectiveness. International Journal of Emerging Technologies in Learning (IJET), 17(24), 144–156. https://doi.org/10.3991/ijet.v17i24.36037
• Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50(6), 2824–2838. https://doi.org/10.1111/bjet.12861
• Mahligawati, F., Allanas, E., Butarbutar, M. H., & Nordin, N. A. N. (2023). Artificial intelligence in physics education: A comprehensive literature review. Journal of Physics: Conference Series, 2596(1), 012080. https://doi.org/10.1088/1742-6596/2596/1/012080
• Mårell-Olsson, E. (2022). Teachers’ perception of gamification as a teaching design. Interaction Design and Architecture(s), 53, 70–100. https://doi.org/10.55612/s-5002-053-004
• Marino, M. T., Vasquez, E., Dieker, L., Basham, J., & Blackorby, J. (2023). The future of artificial intelligence in special education technology. Journal of Special Education Technology, 38(3), 404–416. https://doi.org/10.1177/01626434231165977
• Mehta, N., Harish, V., Bilimoria, K., Morgado, F., Ginsburg, S., Law, M., & Das, S. (2021). Knowledge and attitudes on artificial intelligence in healthcare: A provincial survey study of medical students. MedEdPublish, 10(1). https://doi.org/10.15694/mep.2021.000075.1
• Memduhoğlu, H. B., & Keleş, E. (2016). Evaluation of the relation between critical-thinking tendency and problem-solving skills of pre-service teachers. Journal of Educational Sciences Research, 6(2), 75–94. https://doi.org/10.12973/jesr.2016.62.5
• Monteiro, A. F., Miranda-Pinto, M., Osório, A. J., & Araújo, C. (2021). Coding as literacy: Case studies at pre-primary and elementary school. INTED Proceedings, 3458–3464. https://doi.org/10.21125/inted.2021.0718
• Narang, A., Velagapudi, P., Rajagopalan, B., LeBude, B., Kithcart, A. P., Snipelisky, D., & Sinha, S. S. (2018). A new educational framework to improve lifelong learning for cardiologists. Journal of the American College of Cardiology, 71(4), 454–462. https://doi.org/10.1016/j.jacc.2017.11.045
• Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022a). Teachers’ trust in AI-powered educational technology and a professional development program to improve it. British Journal of Educational Technology, 53(4), 914–931. https://doi.org/10.1111/bjet.13232
• Nazaretsky, T., Bar, C., Walter, M., & Alexandron, G. (2022b). Empowering teachers with AI: Co-designing a learning analytics tool for personalized instruction in the science classroom. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW1), 1–23. https://doi.org/10.1145/3506860.3506861
• Ng, D. (2023). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive, and ethical approach. British Journal of Educational Technology, 55(3), 1082-1104. https://doi.org/10.1111/bjet.13411
• Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137–161.
• Ng, D., Leung, J., Chu, K., & Qiao, M. (2021). AI literacy: Definition, teaching, evaluation and ethical issues. Proceedings of the Association for Information Science and Technology, 58(1), 504-509. https://doi.org/10.1002/pra2.487
• Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. 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
• Nguyen, G., Nguyen, N., & Giang, N. T. H. (2022). Situation and proposals for implementing artificial intelligence-based instructional technology in Vietnamese secondary schools. International Journal of Emerging Technologies in Learning (iJET), 17(18), 53–75. https://doi.org/10.3991/ijet.v17i18.31503
• Nwana, H. (1990). Intelligent tutoring systems: An overview. Artificial Intelligence Review, 4(4), 251–277. https://doi.org/10.1007/BF00168958
• Olivares Funes, J., Valero Kari, E. R., & Martin, P. (2024). Description of parameter variation learning with artificial intelligence and GeoGebra in students of a differential equations course. Journal of Physics: Conference Series, 2701(1), 012049.
• Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P. (2023). Integration of artificial intelligence performance prediction and learning analytics to improve student learning in an online engineering course. International Journal of Educational Technology in Higher Education, 20(1), 4. https://doi.org/10.1186/s41239-022-00372-4
• Park, W., & Kwon, H. (2023). Implementing artificial intelligence education for middle school technology education in Republic of Korea. International Journal of Technology and Design Education, 33(1), 73–98. https://doi.org/10.1007/s10798-023-09812-2
• Pelaez, A., Jacobson, A., Trias, K., & Winston, E. (2022). The Turing teacher: Identifying core attributes for AI learning in K-12. Frontiers in Artificial Intelligence, 5, 1031450. https://doi.org/10.3389/frai.2022.1031450
• Polak, S., Schiavo, G., & Zancanaro, M. (2022). Teachers’ perspective on artificial intelligence education: An initial investigation. Proceedings of the 2022 ACM Conference on Innovation and Technology in Computer Science Education V.1, 349–354. https://doi.org/10.1145/3491101.3519866
• Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22. https://doi.org/10.1186/s41039-017-0062-8
• Porayska-Pomsta, K. (2016). AI as a methodology for supporting educational praxis and teacher metacognition. International Journal of Artificial Intelligence in Education, 26(2), 679–700. https://doi.org/10.1007/s40593-016-0101-4
• Portugal, R. C., Guerrero, L. A., & Fuller, D. A. (2000). Desktop, a system based on virtual spaces to support and to promote collaborative learning (poster session). Proceedings of the ACM Conference on Computer Supported Cooperative Work, 351006–351045. https://doi.org/10.1145/351006.351045
• Preiksaitis, C. (2023). Opportunities, challenges, and future directions of generative artificial intelligence in medical education: Scoping review. JMIR Medical Education, 9, e48785. https://doi.org/10.2196/48785
• Priya Gupta, K., & Bhaskar, P. (2020). Inhibiting and motivating factors influencing teachers’ adoption of AI-based teaching and learning solutions: Prioritization using analytic hierarchy process. Journal of Information Technology Education: Research, 19, 693–723. https://doi.org/10.28945/4640
• Qin, Q., & Ao, L. (2022). A new world of open space: Reflections and perspectives of Chinese scholars on the digital transformation of education and future education research. International Conference on Web-Based Learning, 302-313. Cham: Springer International Publishing. https://doi.org/10.55162/mcet.05.153
• Rathore, A. (2023). Artificial intelligence and curriculum prospects for elementary school. Pakistan Journal of Humanities and Social Sciences, 11(4). https://doi.org/10.52131/pjhss.2023.v11i4.1909
• Rigley, E. (2023). Evaluating international AI skills policy: A systematic review of AI skills policy in seven countries. Global Policy, 15(1), 204–217. https://doi.org/10.1111/1758-5899.13299
• Sajinčič, N., Sandak, A., & Istenič, A. (2022). How do Slovenian educators feel about gamification? Interested to know more. Education and Self Development, 17(1), 99–109. https://doi.org/10.26907/esd.17.1.09
• 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
• Vasylyuk-Zaitseva, S., Kosenyuk, H., Tanasiichuk, I., & Boyko, J. (2023). Application of artificial intelligence in Ukrainian education of the future. Futurity Education, 3(3), 79–107.
• Vergel, J., Stentoft, D., & Montoya, J. (2017). Extending the theoretical framework for curriculum integration in pre-clinical medical education. Perspectives on Medical Education, 6(4), 246–255. https://doi.org/10.1007/s40037-017-0348-y
• Wang, R., Sun, Z., & Zhou, Y. (2023). Research on the new model of “internet + education” based on artificial intelligence. Proceedings of SPIE, 36(7), 1–12. https://doi.org/10.1117/12.2670426
• Wang, S. (2023). Improving college teachers (CT) information-based teaching ability in the era of artificial intelligence (AI). Proceedings of the International Conference on Educational Technology and Computers, 11, 1188–1192. https://doi.org/10.2991/978-94-6463-040-4_177
• 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