Intelligent Tutoring Systems (ITSs) have shown great potential in enhancing how education is delivered. Many existing ITSs leverage Reinforcement Learning (RL) to optimize the sequence of exercises proposed to the learner. These systems adapt content based on the student’s performance on previous exercises, addressing knowledge gaps while advancing through mastered concepts. However, they typically operate at the whole-exercise level, without visibility into the intermediate steps. In reality, learners may fail to solve an exercise because they encounter difficulties with specific sub-steps. Existing ITSs rely on datasets that do not include exercise decomposition in steps. To overcome this limitation, in this paper, we employ GPT-o3-mini to generate synthetic step-by-step solutions for mathematics exercises from the Junyi Academy dataset. To evaluate if these synthetic steps are useful in reaching the final solution, we use three models of varying size from the Llama family to simulate students of different knowledge levels (i.e., low, medium, high) and verify if the step-by-step guidance increases their problem-solving capabilities. By comparing direct answers for exercises to answers that leverage an incremental step guidance strategy, models successfully solve up to 42% more exercises. This evaluation serves as a foundation for creating synthetic step-by-step solutions that can be employed to develop next-generation step-aware ITSs tailored to students’ specific knowledge gaps.