Optimizing Problem-Solving in Technical Education: An Adaptive Learning System Based on Artificial Intelligence

The increasing complexity of educational challenges in technical disciplines highlights the need for personalized learning systems to address diverse student needs. Traditional methods, often relying on static activities or predefined rules, limit their ability to adapt to individual progress, hindering the development of critical skills such as problem-solving. Based on rules or machine learning, existing adaptive systems offer varying levels of personalization and efficiency but face significant scalability and computational demand barriers. This study proposes an adaptive learning system powered by deep learning algorithms designed to optimize problem-solving skills in technical college students. The system dynamically adjusts the difficulty of activities based on real-time performance data, ensuring a personalized and practical learning experience. A controlled experimental study was conducted with 200 students over eight weeks, divided into pretest, intervention, and posttest phases. The experimental group, which used the adaptive system, showed a 14% improvement in precision (from 71.8% to 85%) compared to 5% for the control group. In addition, the experimental group reduced its average time per activity by 15%, achieving 105 seconds compared to 124 seconds for the control group. These results demonstrate the system’s ability to improve precision, efficiency, and motivation in problem-solving tasks. By balancing computational efficiency with high personalization, this proposal offers a scalable and innovative solution that responds to current limitations in adaptive learning technologies.