INTERNATIONAL EXPERIENCE IN THE MANAGEMENT OF EDUCATIONAL INSTITUTIONS

Silver English teaching algorithm: when artificial intelligence encounters unquantifiable educational emotions

Authors

  • Xiao Ma Institute of Foreign Languages Peoples’ Friendship University of Russia

How to cite

GOST Ma X. Silver English teaching algorithm: when artificial intelligence encounters unquantifiable educational emotions // Education Management Review. 2025. Vol. 15. No. 7-1. P. 197-203. DOI: 10.25726/g5963-6734-8165-x
APA Ma, X. (2025). Silver English teaching algorithm: when artificial intelligence encounters unquantifiable educational emotions. Education Management Review, 15(7-1), 197-203. https://doi.org/10.25726/g5963-6734-8165-x

Abstract

The advent of artificial intelligence is transforming all aspects of society at an unprecedented pace. Amid this technological revolution, the convergence of population aging and the lifelong - learning model has rendered English education for elderly beginners a novel domain within China's educational field. This study posits that although AI exhibits certain pedagogical capabilities, it remains inherently limited in replicating the emotional aspect that is of vital significance in elderly education. Through participatory observations in three urban communities in Beijing, Chongqing, and Shenzhen, four non-negligible human factors in elderly English education have been identified: 1) the motivation for social interaction outperforms the feedback system mediated by artificial intelligence; 2) learning strategies that are culturally embedded and utilize regional language resources, such as the acquisition of English phonetics with the prosody of Chongqing dialect, are effective; 3) an emotion-driven learning impetus that goes beyond the algorithm-based participation mode; 4) the process-oriented educational values conflict with the efficiency-centered technology paradigm. The findings of this study advocate for a hybrid pedagogical framework where AI serves as cognitive scaffolding, while human educators retain the role of emotional management. The proposed model emphasizes three synergistic principles: context-aware technical mediation, culturally responsive content adaptation, and affect-protective interaction design. This study contributes to the balanced discussion of the value of technology integration and humanities education in the context of the elderly.

Keywords

Silver English teaching educational emotion AI-assisted learning сulture-responsive pedagogy human-machine collaboration.

References

Chen J., Li H., Wang Y. Human-AI collaborative teaching in lifelong education: Evidence from China's elderly learning communities //Computers & Education. 2022. № 189. рр. 104-592.

Grigoriev L.M., Makarov A.A. Economic determinants of late-life learning: Evidence from post- Soviet states // Journal of aging and society. 2022. № 42(1). pp. 45-67.

Hochschild A.R. The managed heart: Commercialization of human feeling. 3rd ed. Richmond: University of California Press, 2012. Ivanova O. Cultural-linguistic adaptation in second language acquisition among older adults // Journal of cross-cultural gerontology. 2020. № 35(3). рр. 287-301.

Ministry of Education of China. Report on the development of education for the elderly. Beijing: Higher Education Press, 2023.

National Bureau of Statistics of China. 7th national population census report. Beijing: China Statistics Press, 2023.

National Bureau of Statistics of China. China Statistical Yearbook 2023. Beijing: China Statistics Press, 2023.

Smirnova T. Intergenerational digital literacy transfer in aging societies: A Russian case study // Gerontology & Geriatrics education. 2021. № 42(4). рр. 512-527.

UNESCO. Artificial intelligence and education: Guidance for policy-makers. Р.: UNESCO Publishing, 2023.

United Nations. World population prospects 2022. NY: Department of Economic and Social Affairs, 2022.

Published

2025-07-30

Issue

Section

INTERNATIONAL EXPERIENCE IN THE MANAGEMENT OF EDUCATIONAL INSTITUTIONS

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