Learning Mexican Sign Language (MSL) benefits from interactive systems that provide immediate feedback without requiring specialized sensors. This work presents a virtual training platform that operates with a conventional RGB camera and applies computer vision techniques to guide learners in real time. A dataset of 335 videos was recorded across 12 lessons with professional interpreters and used as the reference material for practice sessions. From each video, 48 keypoints corresponding to hands and facial landmarks were extracted using MediaPipe, normalized, and compared with user trajectories through Dynamic Time Warping (DTW). A sign is accepted when the DTW distance is below a similarity threshold, allowing users to receive quantitative feedback on performance. Additionally, an experimental baseline using video embeddings generated by the Qwen2.5-VL, VideoMAEv2, and VJEPA2 models and classified via Matching Networks was evaluated for scalability. Results show that the DTW-based module provides accurate and interpretable feedback for guided practice with minimal computational cost, while the embedding-based approach serves as an exploratory baseline for larger-scale classification and semi-automatic labeling. A user study with 33 participants evidenced feasibility and perceived usefulness (all category means significantly above neutral; Cronbach’s $\alpha =0.81$). Overall, the proposed framework offers an accessible, low-cost, and effective solution for inclusive MSL education and represents a promising foundation for future multimodal sign-language learning tools.