This chapter reviews research on learning engagement in the AI era, which has evolved through three stages: intelligent recognition, correlation analysis with other factors, and adaptive intervention research. In the chapter on intelligent recognition, various methods for identifying behavioral, emotional, and cognitive engagement are introduced, utilizing advanced technologies like video analysis, facial expression recognition, and speech signal analysis. With AI and deep learning, researchers can more accurately measure engagement and provide real-time feedback to optimize teaching strategies. Furthermore, the relationship between learning engagement and factors such as learning outcomes, motivation, and emotional changes is explored. Empowered by AI, researchers can analyze dynamic changes in engagement and correlate them with other factors, offering deeper insights for targeted educational interventions. Lastly, the discussion shifts to adaptive intervention research, focusing on strategies that adjust learning content, pace, and real-time feedback based on students’ emotional and cognitive states. These adaptive interventions enhance participation and significantly improve learning outcomes.