AI-Based Student Performance Prediction with Dynamic Syllabus Adjustment

In the contemporary educational landscape, the latency between student assessment and pedagogical intervention remains a critical bottleneck. Traditional evaluation mechanisms, such as Optical Mark Recognition (OMR) or manual grading, often provide quantitative metrics without qualitative conceptual analysis. This delay often results in a lost "cognitive window" where remedial instruction is most effective. This paper presents the Adaptive AI Learning System, a multimodal framework designed to automate the evaluation of subjective answer sheets and generate instant, personalized remedial content. Leveraging a fine-tuned version of Google's Gemini 2.5 Flash model, the system processes unstructured data—specifically handwritten images and PDFs—to diagnose specific conceptual gaps. Unlike passive performance predictors that rely on historical regression data, this system actively prescribes a 3-step remedial syllabus and dynamically generates adaptive quizzes tailored to the user's weaknesses. We discuss the system's architecture, the parameter-efficient fine-tuning (PEFT) strategy employed, and the advantages over traditional Learning Management Systems (LMS). Experimental deployment demonstrates the system's capacity to reduce feedback latency by over 98% while providing granular, actionable insights that rival human-level tutoring, effectively bridging the gap between assessment and learning.