Volume 11 • Issue 1 • PP: 83–100 • 2026
An Intelligent Learning Analytics Framework for Predicting AI Proficiency Using BER-SC-Optimized MLP
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© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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The fast adoption of artificial intelligence (AI) and machine learning (ML) in the sphere of higher education has become a catalyst that requires more precise and data-driven models that could assess the level of proficiency and interest in new technologies in students. This work is driven by the fact that there are currently no personalized and analytically rigorous frameworks for assessing the level of AI literacy among college students and proposes an optimized learning analytics model that hyperparameters a Multilayer Perceptron (MLP) by utilizing the Binary Al-Biruni Earth Radius with Sine Cosine Algorithm (BER-SC) metaheuristic algorithm to close the performance gap between human and artificial intelligence. The suggested BER-SC + MLP hybrid was created and evaluated using a sample consisting of 258 student responses from Grand Canyon University, which encompassed AI knowledge and usage behavior as well as career interest. The model achieved an accuracy of 0.9312 after optimization, compared to the MLP’s pre-optimization accuracy (0.8996), and a significant decrease in Mean Squared Error (MSE = 0.0002808). The results demonstrate that BER-SC is far superior to nontraditional optimization techniques in terms of convergence efficiency and prediction accuracy. The contribution of the study is that it created a scalable and interpretable, and high-performing educational data modeling framework that has the potential to support adaptive educational learning analytics and institutional decision-making. The results suggest that intelligent optimization added to the ML-based assessment systems has a high possibility of enhancing the quality, efficiency, and feasibility of AI-based education and allowing more adaptable, more affordable, and more individually focused learning settings.
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