Modern Social Science Research

The rapid development of artificial intelligence (AI) is reshaping college students’ ideas and strategies for English learning. Based on the framework of evolutionary game theory, this study constructs an evolutionary game model including educational policies, technological management, and social discipline to systematically reveal the evolutionary paths and stabilization mechanisms of college students’ English learning strategies in the AI era. By analyzing the interactions among three strategies—proactive learning, neutral learning, and passive learning—and considering the synergistic effects of AI teaching accreditation weights, AI monitoring mechanisms, and recognition of AI collaboration by enterprises, this study explores the evolutionary trajectories of these strategies. The findings show that (1) under a strong regulatory framework, the synergy between policy incentives and competency premiums can drive active learning strategies to dominate. (2) In contrast, weak regulatory environments may lead to the proliferation of passive learning strategies due to continuous improvements in AI substitution efficiency, forming an “AI dependency trap”. (3) The stability of neutral learning strategies relies on the dynamic balance between human-AI collaboration and the attenuation of social signals, exhibiting phased fluctuations in their evolutionary paths. Based on these insights, the study proposes a hierarchical governance framework to achieve coordinated development between AI-enabled empowerment and autonomous English learning.

Evolutionary game theory; Artificial Intelligence; AI dependency; English education; English learning