The secondary use of training materials as fine-tuning data for Intelligent Tutoring Systems (ITS) and Adaptive Learning Platforms (ALP) represents a transformative approach in educational technology. This method enhances the effectiveness of ITS by re-using existing educational resources to improve personalized learning experiences. As demand for adaptive learning solutions increases, the integration of fine-tuning and in-context learning methodologies underscores the potential to create robust systems that respond to diverse learner needs, supporting better academic outcomes in various educational settings. This strategy provides tailored transfer learning, instruction-tuning, and alignment-tuning that can accommodate individual learning styles, allowing for greater engagement and longer retention of the training materials. However, the application of fine-tuning techniques also presents challenges, such as ensuring the quality and relevance of training data, which is crucial for mitigating issues like overfitting. Additionally, educators must consider the architectural designs of ITS to ensure alignment with pedagogical goals, as not all systems support every instructional strategy effectively. Addressing these concerns is essential for maximizing the potential of ITS in providing equitable and effective learning experiences. To demonstrate the strategy a simplified triples-based methodology is proposed for the use of existing training materials as fine-tuning data in ITS. A case study of the use of existing training materials helps evaluate the methodology. The step-by-step case study of the project risk management trainer highlights both the opportunities and challenges in the effort for optimized learning experiences.