Enhancing Engineering Pedagogy through Deep Learning: Applications of Explainable AI and Neural Networks

Conventional engineering pedagogy frequently fails to deliver individualized learning experiences and places a strong emphasis on one-size-fits-all methods of instruction. Meeting the requirements of every individual student and improving learning outcomes become more challenging as a result. Modern instructional tools, however state-of-the-art, typically cannot offer personalized, data-driven insights. Since many systems are clear, it might be challenging for educators and learners to comprehend the recommendations made. The concept-based transformer network presented in this work is intended to improve the personalized learning experience in engineering education. Using focus, this new neural system focuses on the most relevant elements of a student’s education, providing personalized recommendations and assessments. Transparency is achieved through the inclusion of semantic AI, so that teachers and students can understand and validate the recommendations made by the system The proposed approach begins with the collection of large amounts of data, which including demographic information, contact records, and measures of student performance. Pre-processing assures data accuracy and quality. Personalized learning strategies and recommendations are generated through feedforward neural networks and integrated into the instructional platform. Performance measurement and user feedback for incremental change and continuous monitoring. Key metrics such as student engagement, outcomes for learning, and satisfaction levels are used to assess the effectiveness of the model. A comparison of the proposed model with current methods demonstrates its ability to provide scalable, transparent and customizable learning solutions.