Integrating AI with task based methodology in engineering education transforming the learning community

This paper introduces an innovative framework designed to integrate Artificial Intelligence (AI) with Task-Based Methodology in engineering education. The progress in AI technology holds significance for changing educational practices by enhancing the learning experience and educational outcomes. This framework is intended to bridge the gap between the advanced capabilities of AI and learning of engineering principles. The framework provides a systematic approach for incorporating AI tools and techniques into Task-Based educational settings. This study uses a quantitative method, with the help of an online survey distributed through Google forms to evaluate the importance of AI-integrated pedagogy. The study includes 100 engineering students from different branches, with varying levels of technical and digital skills. This mix of students helps make the results more reliable and applicable to a wider group of learners. The framework focuses on using AI in task-based learning by giving students personalized learning experiences, quick feedback on their work, and useful insights into their performance. This helps students stay actively involved, think critically, and improve their skills. The statistical tool used to analyse the data is Generalized Linear Mixed Models and Regression, enabling the analysis of variations among independent and dependent variables. The analysis is carried out in SPSS software. The paper delves into the theoretical underpinnings for merging AI with Task-Based learning that offers practical guidelines for integrating this approach into engineering education. The study shows that this proposed model is significant in engineering education exploring various applications and benefits. This framework lays the foundation for future research and practical implementation, with the goal of advancing engineering education through creative applications of AI technology.