The explosion of digital content and the growing diversity of STEM learners have elevated recommender systems from a convenience to a pedagogical necessity. This chapter charts a practical roadmap for education researchers who wish to harness AI-driven recommendation to personalize instruction, boost engagement, and surface hidden learning opportunities. We first introduce the three foundational paradigms—content-based filtering, collaborative filtering, and large language model approaches—highlighting the distinct data signals and inference strategies each employs. Building on that foundation, we distil five key components that underwrite any effective STEM recommender: dynamic user modelling, rigorous resource representation, continuous feedback loops, algorithmic transparency and robust privacy safeguards. With these design pillars in place, the chapter presents a trio of illustrative use cases that demonstrate how the paradigms translate into actionable research workflows—complete with sample data, code fragments, and evaluation ideas—dwelling on implementation minutiae. We conclude by mapping future directions in multimodal data fusion, context-aware adaptation, affect-sensitive tutoring, and ethical governance. Throughout, the emphasis remains on methodological clarity and practical guidance, enabling researchers to move confidently from conceptual curiosity to classroom-ready innovation.