Adaptive Curriculum Learning for Accelerating Convergence in Data-Driven Model Training

Eko Risdianto (Universitas Bengkulu)
Muhammet Esad Kuloğlu (Abdullah Gül University)
Maria Ulfa (Universitas Bina Darma)

International Journal of Advances in Artificial Intelligence and Machine Learning
Vol. 3 No. 2 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni

Background: Efficient training of machine learning models remains a critical challenge in data-driven artificial intelligence systems, particularly when training samples vary in difficulty, noise level, and representational complexity.Aims: This study proposes and evaluates an adaptive curriculum learning framework that accelerates convergence in machine learning model training by dynamically adjusting sample ordering based on the model's evolving competence.Methods: The proposed approach introduces a feedback-driven curriculum mechanism that integrates difficulty estimation and model competence evaluation. Training samples are initially ranked by estimated difficulty and are progressively updated during training using validation-based performance signals. Experiments are conducted on three benchmark datasets, including UCI Adult Income, Breast Cancer Wisconsin, and CIFAR-10, using multilayer perceptron and convolutional neural network architectures. The framework is compared against random sampling, static curriculum learning, and hard-first sampling strategies. Performance is evaluated based on convergence speed, predictive accuracy, robustness to noise, class imbalance handling, and computational overhead.Result: The proposed adaptive curriculum learning framework consistently accelerates convergence across all datasets compared to baseline methods. The model achieves faster stabilization of training dynamics while maintaining or slightly improving predictive performance on accuracy, F1-score, and AUC. Furthermore, the framework demonstrates improved robustness under noisy-label conditions and class-imbalance scenarios, with reduced performance degradation compared to conventional sampling strategies. The computational overhead introduced by the adaptive mechanism remains minimal compared with the gains in training efficiency.Conclusion: The study concludes that aligning sample ordering with evolving model competence significantly improves training efficiency in machine learning systems.