With the continuous changes in the educational environment, the traditional teacher development path model is facing the challenges of multidimensionality and dynamism. The application of artificial intelligence technology, especially machine learning and optimization algorithms, provides new perspectives and methods for teacher development in education. Based on artificial intelligence algorithms, this study proposes a model for optimizing teachers’ career development path. First, a mathematical model of the teacher career development path is constructed by combining the basic information of teachers, work performance and external environmental factors. Genetic algorithm and particle swarm optimization algorithm are used to optimize the path to maximize teachers’ career potential and the utilization efficiency of school resources. In order to further improve the prediction accuracy, ARIMA and LSTM models were combined to model the linear and nonlinear parts of the time series data, respectively. The ARIMA model was used to obtain a smooth sequence through difference processing and to make preliminary predictions on the teacher career development data. Subsequently, the residual data from the ARIMA model was input into the LSTM model to capture the nonlinear trend and achieve a more accurate prediction of teacher career paths. The experimental results show that the ARIMA-LSTM hybrid model has a mean square error (MSE) of 1.0795, a root mean square error (RMSE) of 0.9456, and a mean absolute error (MAE) of 0.4516, which is significantly better than the traditional ARIMA and LSTM models. The optimization model provides new methods and ideas for the scientific planning of teachers’ career development path.
