The analysis develops a method to integrate edge devices with STEM educational systems that operate in real-time. The difference between routine education exists in its lack of personalized support since artificial intelligence develops tailored feedback for every learner through machine learning algorithms. The proposed method unites AI processing with edge devices embedded directly into the system which enables time-sensitive operations together with enhanced data security while operating through real-time systems. Student inputs are evaluated then immediate customized instruction follows from the implementation of sensors and speech recognition and natural language processing (NLP). Information technology restrictions along with low energy efficiency and unequal distribution of AI feedback present the biggest challenges. The solution requires strategic measures to handle ethical matters including the presence of learning model biases. The challenges in AI-driven tutoring enable both conceptual growth and individualized temporal learning and enhanced student interest in education. Teachers can make learning activities more effective by using real-time data-driven tools within educational programs thus improving the STEM teaching space.