Formal Languages and Automata constitute a fundamental domain in Computer Science; however, courses addressing this subject are often associated with high academic failure rates. Intelligent Tutoring Systems (ITS) have been employed to support student learning yet providing effective and meaningful feedback remains a significant challenge. Recent research has explored the integration of Machine Learning techniques and Large Language Models (LLMs) to enhance instructional personalization, performance diagnosis, and automated feedback generation. Despite their potential, the increasing sophistication of these models has raised concerns regarding the lack of transparency in pedagogical decision-making processes. Such opacity undermines trust, fairness, and ethical accountability in ITS, thereby fostering the development and adoption of Explainable Artificial Intelligence (XAI) approaches. In this context, this paper proposes the development of an XAI-based Intelligent Tutoring System for teaching Automata.