Research on the Limited-Rational Engage of "Teacher-Machine-Student" Teaching Paradigm based on Evolutionary Game Analysis

The essence of the human-machine collaborative "teacher-machine-student" educational paradigm lies in the collaborative interplay among all participants, aimed at elevating instructional quality and enhancing student performance. This study constructs a trilateral evolutionary game model aimed at maximizing educational effectiveness based on the beneficial relationships among teachers, machines, and students within the educational paradigm. It explores the strategic choices of each party during the game process, investigates the evolutionary paths of their behavioral strategies, and identifies the conditions for gradual stability. Matlab is utilized to analyze how changes in influencing factors within the evolutionary game model impact strategy selection. The research identifies that the optimal strategy equilibrium for enhancing educational efficacy involves teachers choosing proactive engagement, machines opting for proactive involvement, and students committing to active participation. For each entity, adjustments in teacher reputation gains, machine self-evolution benefits, and student learning gains influence their strategic decisions. These findings underscore the inherent principles of collaboration among teachers, machines, and students, offering concrete implementation strategies to promote human-machine cooperation and mutual educational advancement.