The Future of Education: AI and ML as Tools for Stress-Free, Competency-Based Learning

This study shows a novel way to employ AI and machine learning to make schooling individualized, stress-free, and competency-based. The technique comprises assessing competency, developing a personalized learning plan, and providing adaptive reinforcement. Our method initially displays each learner's skills as a multidimensional competency vector. This process is based on test scores and learner engagement. Regression algorithms and confidence interval analysis constantly update this vector to ensure accurate and sensible student performance assessments. You'll create learning plans by solving an optimization issue to lessen the learning challenge in the second phase. Gradient-based methods solve this. These strategies include minimizing work to minimize cognitive overload. We assign learning activities based on skill gaps. Load balance and penalty functions provide smooth progression. The final stage uses adaptive reinforcement to track performance and adjust the learning path based on feedback and success. Practice weak skills without pushing the pupil too hard so they can progress safely. The test findings demonstrate that the method is more accurate, engaging, stress-reducing, and scalable than existing solutions. Its robustness, low setup cost, and high user satisfaction indicate that it can be employed in the real world. This smart, integrated strategy improves educational outcomes and gives students more independence, motivation, and growth opportunities. This strategy paves the way for future expansion and operation, ensuring effective, tailored teaching across diverse learning contexts.