Deep Integration of AI and TPACK: Reconstruction of Teachers’ Knowledge Structure in the Post-pandemic Era
DOI:
https://doi.org/10.54691/bcpep.v3i.28Keywords:
Artificial Intelligence (AI); Post-pandemic Era; Technological Pedagogical Content Knowledge (TPACK); Teachers’ Knowledge Structure.Abstract
The development of artificial intelligence (AI) technology has brought an unprecedented intelligent reform to teaching, and the COVID-19 pandemic has provided an opportunity for online and offline hybrid teaching mode. During the pandemic prevention and control period, the application of AI technology has brought about subversive changes in teaching methods, teaching content and teaching environment. This is the result of continuous adjustment in teaching practice in schools around the world, and will be a new characterization of teaching practice in the future. The social crisis caused by COVID-19 requires teachers to adjust their knowledge structure accordingly. Based on the new requirements for teachers in the era of AI and the traditional Technological Pedagogical Content Knowledge (TPACK) theory, this paper puts forward a new idea to reconstruct teachers’ knowledge structure by deep integration of AI and TPACK. This study studies how teachers should change their knowledge structure from the four dimensions of Pedagogical Content Knowledge (PCK), Technological Content Knowledge (TCK), Technological Pedagogical Knowledge (TPK) and TPACK, and puts forward suggestions for the intelligent development of education, in order to provide new theoretical support for teachers’ career development in the Post-pandemic era.
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References
Wang, Z. How Should Education be Transformed in the Post-epidemic Era? E-education Research, 2020, 41 (04): 13-20.
Liu, K., Xing, F., Bao, Z., & Wu, Y. The Research on Intention of Using Smart Teaching Tools under the Background of “Internet + Education” - A Discussion on the Influences of Novel Coronavirus Epidemic on Blended Education. Higher Education Review, 2020 (1): 181-196.
Zhong, B., & Nan, X. Overall Reflections on the Development of China’s Higher Education in the Post-pandemic Era. Educational Research, 2021, 42(05): 108-116.
Liu, C., Wan, P., Wang, Y., & Yang, G. AI Robots and Instructional Applications: Innovation Implications and Realistic Challenges. Journal of Distance Education, 2020, 38(2): 27-36.
Chen, S., & He, G. New Exploration of Educational Development and Practice Paradigm from the Perspective of Machine Intelligence: Inspirations on 2018 A National Machine Intelligence Strategy for the United States. Journal of Distance Education, 2018(3): 34-44.
Li, Q., & Yan, Y. Innovating Pedagogies with Technology: From Playful Learning to Learning with Robots: Introductions to Innovating Pedagogy Report 2019. Journal of Distance Education, 2019(2): 17-26.
Zhang, H., Huang, R., Li, J., & Yin, X. Planning Education in the Artificial Intelligence Era: Lead the Leap--An Interpretation of the Outcome Document of the International Conference on Artificial Intelligence and Education: Beijing Consensus on Artificial Intelligence and Education. Modern Distance Education Research, 2019, 31(03): 3-11.
Shulman, L. Those Who Understand: Knowledge Growth in Teaching[J]. Educational Research, 1986, 15 (2): 4-14.
Koehler, M., & Mishra, P. What Happens When Teachers Design Educational Technology? The Development of Technological Pedagogical Content Knowledge. Journal of Educational Computing Research, 2005, 32(2): 131-152.
Koehler, M., & Mishra, P. What is Technological Pedagogical Content Knowledge? Contemporary Issues in Technology and Teacher Education, 2009 (1): 60-70.
Deng, G., Li, Y., & Zhu, Y. Reconstruction of Teachers’ Knowledge Structure Driven by “AI + Education”: The Framework of AIPCEK Integrated with Ethics and Its Development Model. Journal of Distance Education, 2021, 39(01): 63-73.
Wang, L. New Concept of Knowledge: Remolding Teaching and Learning in the Age of Intelligence. Journal of East China Normal University (Educational Sciences), 2019, 37(05): 38-55.
Xie, Q., & Wang, C. “Learning via Application”: Tapping into Dark Knowledge to Transform Traditional Modes of Learning in the Age of AI. Modern University Education ,2020(2): 24-31+112.
Park, S., & Oliver, J. Revisiting the Conceptualisation of Pedagogical Content Knowledge (PCK): PCK as a Conceptual Tool to Understand Teachers as Professionals. Research in Science Education, 2008, 38 (3): 261-284.
Hong, H., Lin, Y., & Lee, Y. Developing effective knowledge-building environments through constructivist teaching beliefs and technology-integration knowledge a survey of middle-school teachers in northern Taiwan. Learning and Individual Difference, 2019(76):1-9.
Ouyang, J., & Xu, X. On the inheritance and transcendence of “teaching-academic” communication mode to tradition [J]. Journal of the Chinese Society of Education, 2016(2): 50-55.
Bay View Analytics. Digital texts in the time of COVID: Educational Resources in U.S. higher education, 2020 [EB/OL]. [2021-05-09]. https:// www. Bayview analytics. com/reports/ digital text sin the time of covid.pdf.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. (2016). Intelligence Unleashed: An argument for AI in Education [EB/ OL]. [2021-06-10]. https://www.pearson.com/content/dam/corporate/global/pearson-dot-com/files/innovation /Intelligence-Unleashed-Publication.pdf.
Yan, Z., Fu, J., Zhu, Y., & Duan, Y. AI-Technological Pedagogical Content Knowledge (AI-TPACK): Connotation, Teaching and Learning Practice and Future Issues. Journal of Distance Education, 2020, 38 (05): 23-34.
Quan, G., Gu, X., & Wu, J. Research on the “Enabling” Effect of Visual Interaction in Artificial Intelligence Education Application. Open Education Research, 2021, 27(04): 111-120.
Center for the Analytics of Learning and Teaching. Who we are and what we do [EB/OL]. [2021-05-09]. https://www.chhs.colostate.edu/alt.
Guo, J., & Hao, J. Learning Mechanism Under Artificial Intelligence Environment. Modern Distance Education Research, 2019, 31(05): 32-38.
