Enhancing Deep Learning and Motivation in University English Education through AI Technology: A Quasi-Experimental Study
Huang Qiuyang *
Faculty of Economics and Management, Jiangxi Arts and Ceramics Technology Institute Gongmei Avenue, Jingdezhen 333000, Jiangxi, China.
Li Wenling
School of Education Science, Guangxi Minzu Normal University, Chongzuo 532200, Guangxi, China.
Zhao Yanmei
School of Foreign Languages, Yuxi Normal University Yuxi, China.
*Author to whom correspondence should be addressed.
Abstract
Aims: This study explores the impact of AI-assisted learning on academic achievement and learning motivation in university English education. It aims to analyze the effectiveness of different AI-integrated learning environments in enhancing students’ engagement and performance.
Study Design: A mixed-methods approach was employed, incorporating both exploratory and confirmatory quasi-experimental designs. Twenty-four students (six from each group) are selected for eye-tracking.
Place and Duration of Study: The study was conducted at University in China over a period of four months.
Methodology: The study involved two experimental phases. The exploratory phase analyzed students’ academic achievement and learning motivation in three groups: AI-driven cognitive learning (G group), multimedia-based AI learning (M group), and a control group (D group) using traditional methods. Academic performance data were collected through standardized tests, while motivation levels were assessed using a validated questionnaire. The confirmatory quasi-experimental phase compared the impact of AI-assisted learning in two different classroom types, utilizing academic performance assessments, eye-tracking data, and learning motivation surveys to measure cognitive engagement and learning effectiveness. Statistical analyses, including ANOVA and regression models, were applied to determine significant differences among the groups.
Results: Findings indicated that the G group outperformed both the M and D groups in academic achievement, with an average score of 85.6 (SD = 4.2) compared to 78.3 (SD = 5.8) and 72.1 (SD = 7.5), respectively. Eye-tracking data revealed higher attention levels in AI-assisted learning environments. Additionally, students in AI-integrated learning environments exhibited increased motivation and engagement, as reflected in their questionnaire responses.
Conclusion: AI-assisted learning significantly enhances students’ academic achievement and motivation in university English education. The results suggest that AI-driven cognitive learning environments are more effective than multimedia-based AI approaches. These findings provide valuable insights for educators and policymakers aiming to optimize AI integration in higher education. Further research is recommended to explore long-term effects and refine AI-based pedagogical strategies.
Keywords: AI technology, deep learning, learning motivation, university English, educational technology