AI-Empowered Social and Emotional Learning in Primary Mathematics Classrooms: Pathway Design and Practical Exploration

Against the backdrop of comprehensively advancing the digital transformation of education and implementing the fundamental task of fostering virtue through education, the deep integration of artificial intelligence (AI) technology with primary mathematics teaching offers new possibilities for addressing practical dilemmas such as the absence of emotional education and the singularity of teaching strategies in traditional classrooms. This paper presents a conceptual framework rather than an empirical validation study, proposing an “AI + SEL + Mathematics” triadic instructional model. The framework is grounded in multimodal AI technologies that infer students’ emotional states from observable behavioral indicators (facial expressions, speech features, and engagement patterns) and generate personalized learning feedback. Integrated with the CASEL five-dimensional SEL framework (self-awareness, self-management, social awareness, relationship skills, and responsible decision-making), it outlines mathematics instructional designs that incorporate SEL objectives. The study proposes a closed-loop research pathway of “theoretical construction—status diagnosis—model development—practical validation.” It builds an implementation framework through three dimensions: curriculum content integration, experiential teaching activity coordination, and shared resource platform development. Additionally, it establishes a multidimensional support system encompassing funding guarantees, curriculum integration, teacher development, and evaluation systems. As a conceptual pathway paper, this study provides a feasible exploratory framework for AI-empowered emotional education in primary mathematics, laying the groundwork for future empirical validation.