Revisión sistemática de la literatura: Sistemas de tutoría inteligente

Learning to program involves a steep learning curve, especially in the early stages, due to cognitive load, concept abstraction, and the inherent difficulties of programming languages. This study aims to identify recent technological approaches that integrate Intelligent Tutoring Systems (ITS) and Large Language Models (LLM) to support the programming teaching process. A systematic review was conducted following Kitchenham’s guidelines, consulting specialized databases, and applying inclusion and exclusion criteria in three phases to select relevant works in the field. The analyzed studies include proposals featuring conversational feedback, adaptive learning, and automated code analysis, showing improvements in concept comprehension, increased confidence, and higher task completion rates among beginner students. However, challenges were identified, such as the difficulty in maintaining context during prolonged interactions and the presence of erroneous responses or “hallucinations” in the models. It is concluded that multimodal integration, user-centered design, and optimized data management represent key areas to enhance the personalization and effectiveness of these systems in educational environments, fostering both skill development and continuous learning monitoring.