In the field of educational data mining, Knowledge Tracing (KT) serves as a core technology for online learning systems, enabling personalized learning through dynamic modeling and real-time updates of learners’ knowledge states. While existing research primarily focuses on assessing knowledge-point-level mastery, real-world educational contexts are characterized by substantial heterogeneity among students in terms of their cognitive ability and efficiency of knowledge acquisition. To address this, this article proposes an Ability-Enhanced Knowledge Tracing (AEKT) model under the Outcome-Based Education (OBE) theoretical framework, which integrates knowledge mastery with ability development. The AEKT model comprises four key components: a student ability acquisition module, an ability-enhanced knowledge assessment module, a foundational knowledge acquisition module, and a predictive module. Leveraging a Long Short-Term Memory (LSTM)-based deep learning architecture, AEKT extracts ability-specific features and knowledge hierarchy relevant to target problems, while dynamically modeling knowledge points, cognitive abilities, and the ability-aware knowledge acquisition process. Final student performance predictions are interpretable outputs constructed using an IRT-based model. The experimental results indicate that our proposed approach achieves superior performance compared to classical baseline methods on all four real-world public datasets. Ablation studies further validate the effectiveness of each module.