Student engagement and speaking performance in AI-assisted learning environments: A mixed-methods study from Chinese middle schools

As educational technology advances, the role of artificial intelligence (AI) in enhancing language education becomes increasingly prominent. However, there is a scarcity of empirical research assessing how AI integration influences student engagement and contributes to the language learning performance. This mixed-methods study seeks to fill the gap, exploring the relationship between student engagement and speaking performance within AI-assisted learning environments for middle school students in China. Quantitative examination of 616 students through multiple regression analysis highlights emotional, behavioral and cognitive engagement as key predictors of speaking performance, with cognitive engagement being the most significant. In-depth semi-structured interviews with 12 students reveal diverse dimensions of engagement in AI-assisted language learning, highlighting how emotional, behavioral, and cognitive engagements facilitate the language learning process. The research offers a comprehensive analysis of the correlation between student engagement and learning performance in AI-assisted environments, underscoring engagement’s crucial role and proposing targeted strategies to elevate language education efficacy for educational stakeholders.