International Journal of New Developments in Education, 2025, 7(11); doi: 10.25236/IJNDE.2025.071103.
Lin Du, Mengyao Cheng
School of Social Development, Tianjin University of Technology, Tianjin, 300382, China
China's aging population and the emergence of smart elderly care present significant pedagogical challenges for gerontology education. Current experimental teaching approaches in this recently established undergraduate field frequently misalign with industry requirements. This research introduces an innovative "industry-education integration and AI technology dual-driven" teaching model. By examining pedagogical limitations and industry developments, we develop a "three-tier progressive, four-dimensional integration" framework for experimental curriculum reform. Our model seeks to connect theoretical learning with practical applications, developing students' competencies for technology-enhanced elderly care environments.
Gerontology; Industry-Education Integration; Artificial Intelligence; Experimental Teaching; Smart Elderly Care
Lin Du, Mengyao Cheng. A Dual-Driven Experimental Teaching Model for Gerontology: Integrating Industry and AI Technology. International Journal of New Developments in Education (2025), Vol. 7, Issue 11: 14-18. https://doi.org/10.25236/IJNDE.2025.071103.
[1] National Bureau of Statistics of China. (2024). Statistical Bulletin of the National Economic and Social Development of the People's Republic of China for 2023 [EB/OL]. (2024-02-28)[2025-08-20]. http://www.stats.gov.cn/sj/zxfb/202402/t20240228_1947915.html
[2] State Council of the People's Republic of China. (2022). 14th Five-Year Plan for National Aging and Elderly Care Service System Development [EB/OL]. (2022-02-21)[2024-12-20]. http://www.gov. cn/zhengce/content/2022-02/21/content_5674844.html
[3] Zawacki-Richter O, Marín V I, Bond M, et al. Systematic review of research on artificial intelligence applications in higher education–where are the educators?[J]. International Journal of Educational Technology in Higher Education, 2019, 16(1): 39.
[4] Pilz M. The Future of Vocational Education and Training in a Changing World[M]. Wiesbaden: Springer VS, 2012.
[5] Kolb D A. Experiential Learning: Experience as the Source of Learning and Development[M]. Englewood Cliffs: Prentice Hall, 1984.
[6] State Council of the People's Republic of China. (2017). Opinions on Deepening Industry- Education Integration[EB/OL]. (2017-12-19)[2024-08-20]. http: //www. gov. cn/zhengce/ content/2017 -12/19/content_5248564.htm
[7] Siemens G. Connectivism: A learning theory for the digital age[J]. International Journal of Instructional Technology and Distance Learning, 2005, 2(1): 3-10.
[8] Brown T, Mann B, Ryder N, et al. Language Models are Few-Shot Learners[C]. Advances in Neural Information Processing Systems, 2020, 33: 1877-1901.
[9] Roll I, Wylie R. Evolution and revolution in artificial intelligence in education[J]. International Journal of Artificial Intelligence in Education, 2016, 26(2): 582-599.
[10] Ryan R M, Deci E L. The "What" and "Why" of Goal Pursuits: Human Needs and the Self- Determination of Behavior[J]. Psychological Inquiry, 2000, 11(4): 227-268.
[11] Eraut M. Informal learning in the workplace[J]. Studies in Continuing Education, 2004, 26(2): 247-273.
[12] Krathwohl D R. A revision of Bloom's taxonomy: An overview[J]. Theory into Practice, 2002, 41(4): 212-218.
[13] Bonk C J, Graham C R. The Handbook of Blended Learning: Global Perspectives, Local Designs [M]. San Francisco: Pfeiffer, 2005. ISBN: 978-0787977580
[14] Shulman L S. Knowledge and teaching: Foundations of the new reform[J]. Harvard Educational Review, 1987, 57(1): 1-22.
[15] Stufflebeam D L, Coryn C L S. Evaluation theory, models, and applications[M]. 2nd ed. San Francisco: Jossey-Bass, 2014. ISBN: 978-1118074565
[16] Russell S J, Norvig P. Artificial Intelligence: A Modern Approach[M]. 4th ed. Boston: Pearson, 2020. ISBN: 978-0134610993