An AI-interpretable multidimensional intelligence-driven framework for student performance prediction using local LLM-based narrative reasoning

Abstract

Predicting student performance has become a vital objective in educational data mining, however existing models remain limited by limited feature sets, opaque decision processes, and the absence of real-time, context-aware interpretative intelligence. This study presents a novel hybrid Artificial Intelligence (AI) architecture that integrates cognitive, emotional, and general intelligence indicators with formative assessment/class performance data to construct a multidimensional demonstration of student performance. The system utilizes a mathematically formalized feature-weighting mechanism that allows domain experts to regulate the influence of key predictors prior to training, enabling a tunable and educator-configurable modeling process. Interaction between cognitive, emotional, and behavioral properties are explicitly engineered to capture complex effects often overlooked in prior research. An ensemble predictive model combining Random Forests, Gradient Boosting, and Linear Regression is used to predict student grades, with uncertainty measured through variance-based confidence intervals. The integrated system exhibited strong predictive capability, attaining high accuracy (R 2 = 0.9014, 95% CI [0.8882, 0.9121], 10,000 bootstrap resamples) with low prediction error, confirming the stability and reliability of the ensemble model. Understanding why a model produces a particular prediction is often as important as the prediction itself. To address the interpretability gap, the system incorporates SHapley Additive exPlanations (SHAP)-based global and local explanations, a counterfactual simulation engine that estimates the marginal impact of improving specific attributes, and a local generative AI layer deployed via Ollama that creates these outputs into structured, real-time narrative feedback. Running the Large Language Model (LLM) locally ensures privacy-preserving, institution-compliant interpretability while permitting interactive, student specific explanations. This integration of predictive modeling, explainability, simulation, and local generative reasoning creates a unified decision-support framework that exceeds traditional black-box approaches. Experimental assessment shows that the model not only achieves viable predictive accuracy but also provides actionable insights into feature influence, model stability, and potential intervention pathways.