The optimization strategy of personalized english teaching in higher education based on fuzzy control
Year: 2026
Type: article
Abstract: This study investigates the effectiveness of a fuzzy control-based personalized English teaching strategy through a comparative experiment involving 200 participants (100 experimental group, 100 control group). The experimental group received instruction dynamically adjusted by fuzzy logic algorithms based on individual learning assessments, while the control group followed traditional standardized teaching methods. Results demonstrated statistically significant improvements ( p < 0.01) in the experimental group, with average test scores increasing by 19.2% compared to 5.3% in the control group. Notably, students in the bottom performance quartile showed 26.1% greater improvement than their counterparts in traditional instruction, confirming the strategy’s effectiveness in addressing learning disparities. The fuzzy control system successfully reduced performance variance by 38% in the experimental group, indicating enhanced adaptability to individual differences. While validating the potential of fuzzy control in personalized education, limitations include the single-institution sample and reliance on quantitative assessments. Future research should expand to multi-institutional trials and incorporate qualitative learning analytics. The findings support integrating fuzzy control with AI technologies to develop more responsive educational systems for diverse learner populations.
Source: Discover Artificial Intelligence
Author: Tingting Tao
Institution: Suzhou City University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W7128774386