AI-Driven Personalized Learning Effect Prediction Based on Machine Learning Algorithms

Generative and learning analytics artificial intelligence (AI) is rapidly entering middle school classrooms, expected to enhance personalized learning, improve formative assessment, and reduce the burden on teachers. Australia has formed a relatively clear ecosystem at the policy and curriculum levels: The federal education department has released the "Generative AI Framework for Australian Schools", putting forward principles such as safety, ethics, transparency and capacity building; In the V9 version of the ACARA course, resources for the connection between AI and disciplinary capabilities are provided. NAPLAN has fully shifted to online adaptive evaluation, providing the institutional and technical foundation for personalized assessment. Meanwhile, UNESCO advocates "people-oriented" generative AI education governance, while the OECD emphasizes the value of learning analytics and data-driven adoption. To address the limitations of existing algorithms, this paper proposes a regression prediction algorithm based on multi-head attention mechanism to optimize the bidirectional long short-term memory network (BiLSTM). The study first conducted a correlation analysis, and at the same