Comparative Impact of AI and Search Technologies on Outcome-Based Learning in Engineering Education

Abstract

AI and advanced search technologies are transforming engineering education by enhancing student outcomes, entrepreneurship, and employability through the development of tech-driven skills. This study employs outcome-based experimental learning to analyze the comparative impact ofGenerative AI (e.g., ChatGPT), non-generative AI tools, and traditional search engines (e.g., Google Search) on engineering education. Ninety students participated in a controlled experiment, solving a technical problem using these three methods. The study evaluated solution efficiency and learning outcomes based on a range of assessment criteria. Results revealedthat Generative AI facilitated faster problem-solving but often ledto weaker conceptual understanding, while non-generative AItools provided more accurate solutions, albeit with slower response times. Google Search was effective for information retrieval but posed challenges in synthesizing coherent and integrated solutions. These findings underscore the importance ofadopting a balanced approach that combines the strengths of AI tools with rigorous assessment methods to promote deep learning and critical thinking. The study highlights the relevance of evaluating evolving technologies in education and calls for curriculum and assessment strategies that adapt to these advancements. Future research should explore the long-term impact of AI on knowledge retention and its application across diverse engineering domains.