Providing personalized content that meets the diverse needs of learners is a significant challenge for today's learning systems. Current educational platforms lack the capability to effectively support the needs of learners with varying intellectual abilities, learning pace, preferences, and academic backgrounds. To overcome this challenge, there is a pressing need for sustainable educational tools that can adapt to the individual needs of student learners. Our study proposes Reinforced-EDU, an intelligent and effective reinforcement learning-guided framework based on Q-Learning technique. The framework adaptively schedules course assignments and educational activities based on students' characteristics, preferences, skills, and academic backgrounds. It considers different student-centered academic factors to prescribe a suitable learning plan that maximizes the students' overall grade and satisfaction rate while reducing dropouts. We identify four distinct learning modes tailored towards effective logical reasoning that align best with the students' preferences and characteristics. Our proposed intelligent method, Reinforced-EDU, can dynamically assign an optimal learning path with 95% accuracy based on students' preferences and pre-knowledge.