This paper focuses on AI technology empowering open education mathematics classrooms and the construction of a teaching evaluation system, which is oriented toward the digital empowerment of open education mathematics teaching applications. Aiming at the core problems of “evaluation failure” (adult learners are geographically dispersed and dominated by independent learning, while the traditional “one final examination determines the grade” model emphasizes results over processes and cannot capture learning trajectories) and “inefficient learning” (adult learners have weak mathematical foundations, heavy mathematics anxiety, and fragmented learning time, lacking personalized learning support and interesting learning scenarios) existing in open education mathematics teaching, this paper proposes AI-driven solutions and implementation paths. Specifically, it includes constructing a “cognition-behavior-emotion” multimodal evaluation system integrating learning behavior analysis, homework trajectory analysis, and emotion state identification to drive the reform of educational evaluation; creating a learning ecology of “game clearance + adaptive push” to support learners’ personalized learning through the development of mathematical mini-games and collaborative learning communities; meanwhile, building a dedicated data middle platform for the mathematics discipline with student portraits, a teaching strategy library, and a learning early-warning mechanism to realize tool collaboration and data closed-loop. The research aims to break through the bottleneck of traditional open education teaching, improve teaching quality and course pass rate, promote learners’ all-round development through the deep integration of AI technology and open education mathematics teaching, and provide theoretical and practical references for the modernization reform of open education and the AI-empowered education teaching reform.