The rapid advancement and widespread adoption of Information Technology (IT) have significantly influenced the ways individuals engage with educational content, prompting a growing demand for more adaptive and intelligent learning environments. Social Learning Networks (SLNs), as dynamic platforms for collaborative learning, require continuous enhancement to meet the evolving needs of learners. A critical aspect of improving SLNs lies in the accurate prediction of learners' future educational requirements, which plays a fundamental role in facilitating the learning process and enhancing overall learner performance. This study introduces a novel interpreter framework specifically developed to predict the learning needs of users within SLNs. The interpreter analyzes users’ historical learning patterns and intelligently recommends subsequent topics that align with their learning trajectories. To enhance the prediction accuracy, we propose a user-based Collaborative Filtering (CF) approach, which leverages similarities among users' learning behaviors to infer future needs. To validate the effectiveness of the proposed method, experiments were conducted using a dataset extracted from a widely recognized SLN. The experimental results reveal that users with similar learning histories tend to exhibit parallel learning needs, supporting the collaborative nature of the proposed approach. The system demonstrated a strong ability to forecast approximately 60% of learners' upcoming needs, as measured by recall performance metrics. The outcomes of this research underscore the potential of personalized, data-driven recommendation techniques in advancing SLNs, ultimately contributing to more effective and targeted learning experiences.