Abstract
Artificial intelligence (AI) is bringing new developments in education. Teachers’ professional development grows with the promotion of technology, and more challenges and difficulties will be faced by teachers in the AI age. Thus, this study aimed to explore what a teacher portrait should be like in the new AI age. In order to systematically and comprehensively construct a teacher portrait for the AI age, we searched online databases using keywords, and after screening according to the inclusion and exclusion criteria, 26 journal documents were identified for in-depth analysis. It was found that there were 20 different types of frameworks that could be used to construct a teacher portrait for the AI age. This study reconstructed a teacher portrait based on the Person-Process-Content (PPC) structure of the micro ecological system theory, and finally arrived at a teacher portrait framework with three dimensions and eight sub-dimensions, including teachers’ cognition and emotion, teachers’ knowledge and skills, and interaction between teachers’ cognition and ability, which highlighted the dynamic requirements of teachers’ professional development in the AI age. In addition, the challenges faced by teachers in the AI age are mainly concentrated on four aspects: the upgrading of teacher training requirements, the change of educational environment, the teaching application of digital technology, and the ethical issues of artificial intelligence. These findings provide a direction for promoting the professional development of teachers in the AI age, and can help teachers better cope with the challenges of the new age.
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References
Ahmad, S. F., Alam, M. A., Rahmat, M. F., Mubarik, M. S., & Hyder, S. I. (2022). Academic and administrative role of artificial intelligence in education. Sustainability, 14(3), 1101. https://doi.org/10.3390/su14031101
Ally, M. (2019). Competency profile of the digital and online teacher in future education. International Review of Research in Open and Distributed Learning, 20(2). https://doi.org/10.19173/IRRODL.V20I2.4206
Álvarez, C. Á., & Falcon, S. (2023). Students’ preferences with university teaching practices: Analysis of testimonials with artificial intelligence. Educational Technology Research and Development, 71(4), 1709–1724. https://doi.org/10.1007/s11423-023-10239-8
An, X., Chai, C. S., Li, Y., Zhou, Y., Shen, X., Zheng, C., & Chen, M. (2023). Modeling English teachers’ behavioral intention to use artificial intelligence in middle schools. Education and Information Technologies, 28(5), 5187–5208. https://doi.org/10.1007/s10639-022-11286-z
Anderson, J. R., Boyle, C. F., & Reiser, B. J. (1985). Intelligent tutoring systems. Science, 228(4698), 456–462. https://doi.org/10.1126/science.228.4698.456
Anohina, A. (2007). Advances in intelligent tutoring systems: Problem-solving modes and model of hints. International Journal of Computers Communications & Control, 2(1), 48–55. https://doi.org/10.15837/ijccc.2007.1.2336
Ay, Y., Karadağ, E., & Acat, M. B. (2015). The Technological Pedagogical Content Knowledge-practical (TPACK-Practical) model: Examination of its validity in the Turkish culture via structural equation modeling. Computers & Education, 88, 97–108. https://doi.org/10.1016/j.compedu.2015.04.017
Basantes-Andrade, A., Casillas-Martín, S., Cabezas-González, M., Naranjo-Toro, M., & Guerra-Reyes, F. (2022). Standards of teacher digital competence in higher education: A systematic literature review. Sustainability, 14(21), 13983. https://doi.org/10.3390/su142113983
Beames, J. R., Christensen, H., & Werner-Seidler, A. (2021). School teachers: The forgotten frontline workers of Covid-19. Australasian Psychiatry, 29(4), 420–422. https://doi.org/10.1177/10398562211006145
Bereczki, E. O., & Kárpáti, A. (2018). Teachers’ beliefs about creativity and its nurture: A systematic review of the recent research literature. Educational Research Review, 23, 25–56. https://doi.org/10.1016/j.edurev.2017.10.003
Borg, S. (2006). Teacher Cognition and Language Education: Research and Practice. Continuum.
Bronfenbrenner, U. (1979). The Ecology of Human Development: Experiments by Nature and Design. Harvard University Press. https://doi.org/10.2307/j.ctv26071r6
Bronfenbrenner, U. (1989). Ecological systems theory. In R. Vasta (Ed.), Annals of child development: Six theories of child development - Revised formulations and current issues (pp. 187–249). Jessica Kingsley Publishers.
Bronfenbrenner, U. (2005). Making Human Beings Human: Bioecological Perspectives on Human Development; Sage Publications: Thousand Oaks. CA.
Bronfenbrenner, U., & Morris, P. A. (2006). The bioecological model of human development. In W. Damon & R. M. Lerner (Eds.), Handbook of child psychology: Vol. 1. Theoretical models of child development (6th ed., pp. 793–828). Wiley
Cabero-Almenara, J., Romero-Tena, R., & Palacios-Rodríguez, A. (2020). Evaluation of teacher digital competence frameworks through expert judgement: The use of the expert competence coefficient. Journal of New Approaches in Educational Research, 9(2), 275–293. https://doi.org/10.7821/naer.2020.7.578
Caena, F., & Redecker, C. (2019). Aligning teacher competence frameworks to 21st century challenges: The case for the European Digital Competence Framework for Educators (Digcompedu). European Journal of Education, 54(3), 356–369. https://doi.org/10.1111/ejed.12345
Cantrell, S. C., & Callaway, P. (2008). High and low implementers of content literacy instruction: Portraits of teacher efficacy. Teaching and Teacher Education, 24(7), 1739–1750. https://doi.org/10.1016/j.tate.2008.02.020
Castañeda, L., Esteve-Mon, F. M., Adell, J., & Prestridge, S. (2021). International insights about a holistic model of teaching competence for a digital era: The digital teacher framework reviewed. European Journal of Teacher Education, 45(4), 493–512. https://doi.org/10.1080/02619768.2021.1991304
Celik, I. (2022). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468
Chai, C. S., Koh, J. H. L., Tsai, C. C., & Tan, L. L. W. (2011). Modeling primary school pre - service teachers’ Technological Pedagogical Content Knowledge (TPACK) for meaningful learning with information and communication technology (ICT). Computers & Education, 57(1), 1184–1193. https://doi.org/10.1016/j.compedu.2011.01.007
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
Chen, X. L., Zou, D., Xie, H. R., Cheng, G., & Liu, C. X. (2022). Two decades of artificial intelligence in education: Contributors, collaborations, research topics, challenges, and future directions. Educational Technology & Society, 25(1), 28–47.
Chu, H. T., & Ke, Q. (2017). Research methods: What’s in the name? Library & Information Science Research, 39(4), 284–294. https://doi.org/10.1016/j.lisr.2017.11.001
Chu, W., Liu, H., & Fang, F. (2021). A Tale of Three Excellent Chinese EFL Teachers: Unpacking Teacher Professional Qualities for Their Sustainable Career Trajectories from an Ecological Perspective. Sustainability, 13(12), 6721. https://doi.org/10.3390/su13126721
Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683–695. https://doi.org/10.1080/13562517.2013.827653
Coll, C., & Engel, A. (2014). Introduction: Personal learning environments in the context of formal education. Cultura & Education, 26(4), 617–630. https://doi.org/10.1080/11356405.2014.985947
Cooper, A. (2004). The inmates are running the asylum: Why high tech products drive us crazy and how to restore the sanity (2nd Edition). In Pearson Higher Education eBooks. Retrieved March 30, 2023, from https://dl.acm.org/citation.cfm?id=984201
Crompton, H., Jones, M. V., & Burke, D. (2022). Affordances and challenges of artificial intelligence in K-12 education: A systematic review. Journal of Research on Technology in Education. https://doi.org/10.1080/15391523.2022.2121344
Deng, G., & Zhang, J. (2023). Technological pedagogical content ethical knowledge (TPCEK): The development of an assessment instrument for pre-service teachers. Computers & Education, 197, 104740. https://doi.org/10.1016/j.compedu.2023.104740
Devedzic, V. (2004). Web intelligence and artificial intelligence in education. Educational Technology & Society, 7(4), 29–39.
Dijk, E. E., Tartwijk, J. V., Schaaf, M. F., & Kluijtmans, M. (2020). What makes an expert university teacher? A systematic review and synthesis of frameworks for teacher expertise in higher education. Educational Research Review, 31, 100365. https://doi.org/10.1016/j.edurev.2020.100365
DiSanti, J. S., & Erickson, K. (2020). Challenging our understanding of youth sport specialization: An examination and critique of the literature through the lens of Bronfenbrenner’s Person-Process-Context-Time Model. International Review of Sport and Exercise Psychology, 14(1), 28–50. https://doi.org/10.1080/1750984x.2020.1728779
Edwards, E., & Burns, A. (2016). Language teacher-researcher identity negotiation: An ecological perspective. Tesol Quarterly, 50(3), 735–745. https://doi.org/10.1002/tesq.313
Etzioni, A., & Etzioni, O. (2017). Incorporating Ethics into Artificial Intelligence. The Journal of Ethics, 21(4), 403–418. https://doi.org/10.1007/s10892-017-9252-2
Falloon, G. (2020). From digital literacy to digital competence: The teacher digital competency (TDC) framework. Educational Technology Research and Development, 68(5), 2449–2472. https://doi.org/10.1007/s11423-020-09767-4
Fernández, M., Alférez, A. R., Vidal, S., Fernández, M., & Albareda, S. (2016). Methodological approaches to change consumption habits of future teachers in Barcelona, Spain: Reducing their personal Ecological Footprint. Journal of Cleaner Production, 122, 154–163. https://doi.org/10.1016/j.jclepro.2016.02.026
Gabbard, C., & Krebs, R. (2012). Studying environmental influence on motor development in children. Physical Educator, 69(2), 136–149. Retrieved May 9, 2023, from https://www.proquest.com/scholarly-journals/studying-environmental-influence-on-motor/docview/1372483819/se-2
García, C. N., & Lozano, A. C. (2021). The ethical dimension of digital competence in teacher training. Education and Information Technologies, 26(3), 3529–3541. https://doi.org/10.1007/s10639-021-10436-z
Garzón-Artacho, E., Martínez, T. S., Rodríguez, J. M. R., & García, G. G. (2021). Teachers’ perceptions of digital competence at the lifelong learning stage. Heliyon, 7(7), e07513. https://doi.org/10.1016/j.heliyon.2021.e07513
Gilster, P. (1997). Digital literacy. Wiley.
Golombek, P. R., & Doran, M. (2014). Unifying cognition, emotion, and activity in language teacher professional development. Teaching and Teacher Education, 39, 102–111. https://doi.org/10.1016/j.tate.2014.01.002
Guerrero, S. (2005). Teacher Knowledge and a New Domain of Expertise: Pedagogical Technology Knowledge. Journal of Educational Computing Research, 33(3), 249–267. https://doi.org/10.2190/blq7-at6t-2x81-d3j9
Gümüs, M. M., & Kukul, V. (2022). Developing a digital competence scale for teachers: Validity and reliability study. Education and Information Technologies, 28(3), 2747–2765. https://doi.org/10.1007/s10639-022-11213-2
Hannafin, M. J., & Land, S. M. (2000). Technology and student-centered learning in higher education: Issues and practices. Journal of Computing in Higher Education, 12(1), 3–30. https://doi.org/10.1007/BF03032712
Hong, J. C., Ye, J. N., Ye, J. H., Wang, C. M., & Cui, Y. T. (2021). Perceived helicopter parenting related to vocational senior high school students’ academic achievement and smartphone addiction. J. Res. Educ. Sci., 66, 1–33.
Huang, X. D. (2021). Aims for cultivating students’ key competencies based on artificial intelligence education in China. Education and Information Technologies, 26(5), 5127–5147. https://doi.org/10.1007/s10639-021-10530-2
Huang, L., & Zheng, Y. H. (2018). Development problems and prospects of artificial intelligence education. Science & Technology Review, 36(17), 102–105. https://doi.org/10.3981/j.issn.1000-7857.2018.17.013
Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artifcial intelligence in education. Computers and Education: Artifcial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001
Instefjord, E., & Munthe, E. (2016). Preparing pre-service teachers to integrate technology: An analysis of the emphasis on digital competence in teacher education curricula. European Journal of Teacher Education, 39(1), 77–93. https://doi.org/10.1080/02619768.2015.1100602
Jacobs, J. C. G., Van Luijk, S. J., Galindo-Garré, F., Muijtjens, A. M. M., Van Der Vleuten, C. P., Croiset, G., & Scheele, F. (2014). Five teacher profiles in student-centred curricula based on their conceptions of learning and teaching. BMC Medical Education, 14(1). https://doi.org/10.1186/1472-6920-14-220
Janssen, J., Stoyanov, S., Ferrari, A., Punie, Y., Pannekeet, K., & Sloep, P. (2013). Experts’ views on digital competence: Commonalities and diferences. Computers & Education, 68, 473–481. https://doi.org/10.1016/j.compedu.2013.06.008
Jeram, R., & Davids, N. (2020). In support of practice-based teacher professional learning. South African Journal of Higher Education, 34(3), 112–127. https://doi.org/10.20853/34-3-3539
Jiang, H., & Yin, X. H. (2022). Association between community psychological label and user portrait model based on multimodal neural network. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.918274
Jimoyiannis, A. (2013). Developing a pedagogical framework for the design and the implementation of e-portfolios in educational practice. Themes in Science and Technology Education, 5, 107–132.
Jones, M. (1985). Applications of artificial intelligence within education. Computers & Mathematics with Applications, 11(5), 517–526. https://doi.org/10.1016/0898-1221(85)90054-9
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., ... Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kim, S., Raza, M., & Seidman, E. (2019). Improving 21st-century teaching skills: The key to effective 21st-century learners. Research in Comparative and International Education, 14(1), 99–117. https://doi.org/10.1177/1745499919829214
Kim, J., Merrill, K., Xu, K., & Sellnow, D. D. (2020). My teacher is a machine: Understanding students’ perceptions of AI teaching assistants in online education. International Journal of Human-Computer Interaction, 36(20), 1902–1911. https://doi.org/10.1080/10447318.2020.1801227
Koh, J. H. L., & Chai, C. S. (2016). Seven design frames that teachers use when considering technological pedagogical content knowledge (TPACK). Computers & Education, 102, 244–257. https://doi.org/10.1016/j.compedu.2016.09.003
Koponen, M., Asikainen, M. A., Viholainen, A., & Hirvonen, P. E. (2019). Using network analysis methods to investigate how future teachers conceptualize the links between the domains of teacher knowledge. Teaching and Teacher Education, 79, 137–152. https://doi.org/10.1016/j.tate.2018.12.010
Krumsvik, R. J. (2014). Teacher educators’ digital competence. Scandinavian Journal of Educational Research, 58(3), 269–280. https://doi.org/10.1080/00313831.2012.726273
Kurtz, G., & Peled, Y. (2016). Digital Learning Literacies - A validation study. Issues in Informing Science and Information Technology, 13, 145–158. https://doi.org/10.28945/3479
Li, M., & Su, Y. (2020). Evaluation of online teaching quality of basic education based on artificial intelligence. International Journal of Emerging Technologies in Learning (ijet), 15(16), 147. https://doi.org/10.3991/ijet.v15i16.15937
Li, J., Tan, X., & Hu, Y. H. (2021). Research on the framework of intelligent classroom based on artificial intelligence. International Journal of Electrical Engineering Education. https://doi.org/10.1177/0020720920984000
Li, Z., Guang, Z., & Sun, W. (2022). Evaluation and analysis of assisted instruction and ability improvement based on artificial intelligence. Journal of Sensors, 2022(5), 1–13. https://doi.org/10.1155/2022/9979275
Liao, W., Liu, M., Wang, Z., & Qin, K. (2022). Chinese expert teachers’ critical thinking strategies for professional growth. Professional Development in Education, 1–15. https://doi.org/10.1080/19415257.2022.2097290
Lin, D. B. (2016). Application of big data platform in course of Java language programming. International Journal of Emerging Technologies in Learning, 11(10), 16–21. https://doi.org/10.3991/ijet.v11i10.6264
Liu, Q. F., & Deng, W. Y. (2022). Animation user value portrait based on RFM model under big data. Mathematical Problems in Engineering, 2022, 1–6. https://doi.org/10.1155/2022/8246540
Liu, J. H., Wang, C. P., & Xiao, X. C. (2021). Internet of things (IoT) technology for the development of intelligent decision support education platform. Scientific Programming, 6482088. https://doi.org/10.1155/2021/6482088
Long D., & Magerko B. (2020). What is AI Literacy? Competencies and Design Considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20). Association for Computing Machinery, New York, NY, USA, 1–16. https://doi.org/10.1145/3313831.3376727
Long, J. F., & Hoy, A. W. (2006). Interested instructors: A composite portrait of individual differences and effectiveness. Teaching and Teacher Education, 22(3), 303–314. https://doi.org/10.1016/j.tate.2005.11.001
Lucas, M., Bem-Haja, P., Siddiq, F., Moreira, A. C., & Redecker, C. (2021). The relation between in-service teachers’ digital competence and personal and contextual factors: What matters most? Computers & Education, 160, 104052. https://doi.org/10.1016/j.compedu.2020.104052
Mandinach, E. B., & Gummer, E. S. (2016). What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions. Teaching and Teacher Education, 60, 366–376. https://doi.org/10.1016/j.tate.2016.07.011
Margerum-Leys, J., & Marx, R. W. (2004). The nature and sharing of teacher knowledge of technology in a student teacher/mentor teacher pair. Journal of Teacher Education, 55(5), 421–437. https://doi.org/10.1177/0022487104269858
Maria-Cristina, F. (2015). Portrait of teacher actions from the perspective of school managers. Procedia Social and Behavioral Sciences, 180, 162–169. https://doi.org/10.1016/j.sbspro.2015.02.100
McCarthy, J. J., Minsky, M., Rochester, N., & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, August 31, 1955. Ai Magazine, 27(4), 12. https://doi.org/10.1609/aimag.v27i4.1904
McFadden, A., & Williams, K. E. (2020). Teachers as evaluators: Results from a systematic literature review. Studies in Educational Evaluation, 64, 100830. https://doi.org/10.1016/j.stueduc.2019.100830
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLOS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). 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
Nazari, M., Karimi, M. N., & De Costa, P. (2022). Emotion and identity construction in teachers of young learners of English: An ecological perspective. System, 112, 102972. https://doi.org/10.1016/j.system.2022.102972
Ng, D. T. K., Lee, M. G., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. W. (2022). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies. https://doi.org/10.1007/s10639-022-11491-w
Nieto-Isidro, S., Martínez-Abad, F., & Rodríguez-Conde, M. J. (2021). Presente y futuro de la Competencia Informacional Docente en educación obligatoria. Revista Española De Pedagogía, 79(280), 477–496. https://doi.org/10.22550/rep79-3-2021-07
Nobre, F. X., Valentini, N. C., & Rudisill, M. E. (2020). Applying the bioecological theory to the study of fundamental motor skills. Physical Education and Sport Pedagogy, 25(1), 29–48. https://doi.org/10.1080/17408989.2019.1688772
Olari, V., & Romeike, R. (2021). Addressing AI and Data Literacy in Teacher Education: A Review of Existing Educational Frameworks. In Proceedings of the 16th Workshop in Primary and Secondary Computing Education (WiPSCE '21). Association for Computing Machinery, New York, NY, USA, Article 17, 1–2. https://doi.org/10.1145/3481312.3481351
Olofson, M. W., Swallow, M. J. C., & Neumann, M. D. (2016). TPACKing: A constructivist framing of TPACK to analyze teachers’ construction of knowledge. Computers & Education, 95, 188–201. https://doi.org/10.1016/j.compedu.2015.12.010
Pan, X. L. (2021). Educational reform in the era of artificial intelligence. In 2021 2nd International Conference on Computers, Information Processing and Advanced Education (CIPAE 2021). Association for Computing Machinery, New York, NY, USA, 1328–1332. https://doi.org/10.1145/3456887.3457517
Pantelimon, F. V., Bologa, R., Toma, A., & Posedaru, B. S. (2021). The evolution of AI-Driven educational systems during the COVID-19 pandemic. Sustainability, 13(23), 13501. https://doi.org/10.3390/su132313501
Peled, Y., Kurtz, G., & Avidov-Ungar, O. (2021). Pathways to a knowledge society: A proposal for a hierarchical model for measuring digital literacy among Israeli pre-service teachers. Electronic Journal of E-learning, 19(3), 118–132. https://doi.org/10.34190/ejel.19.3.2217
Pressley, T. (2021). Factors Contributing to Teacher Burnout During COVID-19. Educational Researcher, 50(5), 325–327. https://doi.org/10.3102/0013189x211004138
Prestridge, S. (2017). Examining the shaping of teachers’ pedagogical orientation for the use of technology. Technology Pedagogy and Education, 26(4), 367–381. https://doi.org/10.1080/1475939X.2016.1258369
Qin, F., Zeng, W., Li, L., & Zhao, R. (2020). Construction of big data monitoring platform for teaching quality under intelligent education. 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus, pp. 1594–1597, https://doi.org/10.1109/IWCMC48107.2020.9148224.
Qu, J. J., Zhao, Y. A., & Xie, Y. P. (2022). Artificial intelligence leads the reform of education models. Systems Research and Behavioral Science., 39(3), 581–588. https://doi.org/10.1002/sres.2864
Quaicoe, J. S., & Pata, K. (2020). Teachers’ digital literacy and digital activity as digital divide components among basic schools in Ghana. Education and Information Technologies, 25(5), 4077–4095. https://doi.org/10.1007/s10639-020-10158-8
Rahman, M. M., & Watanobe, Y. (2023). ChatGPT for Education and Research: Opportunities, Threats, and Strategies. Applied Sciences, 13(9), 5783. https://doi.org/10.3390/app13095783
Salas-Pilco, S. Z., & Yang, Y. (2022). Artificial intelligence applications in Latin American higher education: a systematic review. International Journal of Educational Technology in Higher Education, 19(1). https://doi.org/10.1186/s41239-022-00326-w
Sanchez-Cruzado, C., Campion, R. S., & Sanchez-Compana, M. T. (2021). Teacher digital literacy: The indisputable challenge after COVID-19. Sustainability, 13(4), 1858. https://doi.org/10.3390/su13041858
Shi, Y. (2021). The interactive effect of EFL teachers’ emotions and cognitions on their pedagogical practices. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.811721
Socratous, C., & Ioannou, A. (2022). Evaluating the impact of the curriculum structure on group metacognition during collaborative problem-solving using educational robotics. TechTrends, 66(5), 771–783. https://doi.org/10.1007/s11528-022-00738-5
Sun, J. M., Ma, H. L., Zeng, Y., Han, D., & Jin, Y. B. (2022). Promoting the AI teaching competency of K-12 computer science teachers: A TPACK-based professional development approach. Education and Information Technologies, 28(2), 1509–1533. https://doi.org/10.1007/s10639-022-11256-5
Tan, C. (2020). Digital Confucius? Exploring the implications of artificial intelligence in spiritual education. Connection Science, 32(3), 280–291. https://doi.org/10.1080/09540091.2019.1709045
Tang, L., Gu, J. J., & Xu, J. L. (2022). Constructing a digital competence evaluation framework for in-service teachers’ online teaching. Sustainability, 14(9), 5268. https://doi.org/10.3390/su14095268
Tschannen-Moran, M., & Hoy, A. W. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education, 17(7), 783–805. https://doi.org/10.1016/s0742-051x(01)00036-1
Uerz, D., Volman, M., & Kral, M. (2018). Teacher educators’ competences in fostering student teachers’ proficiency in teaching and learning with technology: An overview of relevant research literature. Teaching and Teacher Education, 70, 12–23. https://doi.org/10.1016/j.tate.2017.11.005
Villar, L. B. V., Herrero, L. L., & Álvarez-López, G. (2022). UNESCO strategy and digital policies for teacher training: The deconstruction of innovation in Spain. Journal of New Approaches in Educational Research, 11(1), 15–30. https://doi.org/10.7821/naer.2022.1.812
Wachs, T. D., Gurkas, P., & Kontos, S. (2004). Predictors of preschool children’s compliance behavior in early childhood classroom settings. Journal of Applied Developmental Psychology, 25(4), 439–457. https://doi.org/10.1016/j.appdev.2004.06.003
Weng, S. S., Liu, Y., Dai, J., & Chuang, Y. C. (2020). A novel improvement strategy of competency for education for sustainable development (ESD) of university teachers based on data mining. Sustainability, 12(7), 2679. https://doi.org/10.3390/su12072679
Wiens, P. D., Beck, J. S., & Lunsmann, C. J. (2022). Assessing teacher pedagogical knowledge: The Video Assessment of Teacher Knowledge (VATK). Educational Studies, 48(2), 273–289. https://doi.org/10.1080/03055698.2020.1750350
Xu, B. (2021). Artificial intelligence teaching system and data processing method based on big data. Complexity, 2021, 1–11. https://doi.org/10.1155/2021/9919401
Xu, W., & Ouyang, F. (2021). A systematic review of AI role in the educational system based on a proposed conceptual framework. Education and Information Technologies, 27(3), 4195–4223. https://doi.org/10.1007/s10639-021-10774-y
Xu, S., Wang, T., Dai, J., & Wu, D. (2022). Design and implementation of intelligent teaching system based on artificial intelligence and computer technology. Security and Communication Networks, 2022, 1–12. https://doi.org/10.1155/2022/6300299
Xue, Y., & Wang, Y. (2022). Artificial intelligence for education and teaching. Wireless Communications and Mobile Computing, 2022, 1–10. https://doi.org/10.1155/2022/4750018
Yang, H. (2019). The nexus between pre-service teachers’ emotional experience and cognition during professional experience. Australian Educational Researcher, 46(5), 799–825. https://doi.org/10.1007/s13384-019-00320-8
Ye, R., Sun, F., & Li, J. (2021). Artificial intelligence in education: Origin, development and rise. Lecture Notes in Computer Science, 13016, 545–553. https://doi.org/10.1007/978-3-030-89092-6_49
Ye, J. H., He, Z., Yang, X., Lee, Y., Nong, W., Ye, J., & Wang, C. (2023). Predicting the Learning Avoidance Motivation, Learning Commitment, and Silent Classroom Behavior of Chinese Vocational College Students Caused by Short Video Addiction. Healthcare, 11(7), 985. https://doi.org/10.3390/healthcare11070985
Yeh, Y. C., Chan, K. K. H., & Hsu, Y. S. (2021). Toward a framework that connects individual TPACK and collective TPACK: A systematic review of TPACK studies investigating teacher collaborative discourse in the learning by design process. Computers & Education, 171(4), 104238. https://doi.org/10.1016/j.compedu.2021.104238
Zhao, Y., Pugh, K. J., Sheldon, S. H., & Byers, J. L. (2002). Conditions for classroom technology innovations. Teachers College Record, 104(3), 482–515. https://doi.org/10.1111/1467-9620.00170
Zheng, X. H. (2017). Application of distance education combined with artificial intelligence. Agro Food Industry Hi-Tech, 28(1), 555–559.
Zlatic, L., Bjekic, D., Marinkovic, S., & Bojovic, M. (2014). Development of teacher communication competence. Procedia - Social and Behavioral Sciences, 116, 606–610. https://doi.org/10.1016/j.sbspro.2014.01.265
Zmyzgova, T. R., Polyakova, E. N., & Karpov, E. K. (2020). Digital transformation of education and artificial intelligence. 2nd International Scientific and Practical Conference “Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth” (MTDE 2020), (pp. 824–829), Atlantis Press. https://doi.org/10.2991/aebmr.k.200502.134
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This research is supported by National Social Science Fund of China, “Teacher Portrait and Application Research from the Perspective of Artificial Intelligence”, grant No. BCA 220206.
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Hu, X., Sui, H., Geng, X. et al. Constructing a teacher portrait for the artificial intelligence age based on the micro ecological system theory: A systematic review. Educ Inf Technol 29, 16679–16715 (2024). https://doi.org/10.1007/s10639-024-12513-5
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DOI: https://doi.org/10.1007/s10639-024-12513-5