Enhancing Engineering Education: The Role of Artificial Intelligence in Personalizing Learning and Outcomes
Gabriela Dorfman Furman
Abstract
The emergence of artificial intelligence (AI) in educational contexts presents transformative potential for higher engineering education. This paper conducts a comprehensive literature review to explore the role of AI-driven analytics such as personalized learning pathways and adaptive assessment techniques, in personalizing learning experiences and improving educational outcomes for engineering students. Through a systematic analysis of existing research, we identify how AI technologies - such as machine learning algorithms, data mining, and natural language processing - can be utilized to tailor educational content, improve learning engagement, and optimize curriculum design. A synthesis of the findings highlights the significant benefits of AI in identifying diverse learning styles, predicting academic performance, and providing real-time feedback, thus fostering a more individualized and effective learning environment. In addition, this article discusses the challenges and ethical considerations associated with implementing AI in education, including data privacy, algorithmic bias, and the need for human oversight to ensure ethical AI deployment. Building upon insights gleaned from a thorough review of published literature, we propose a conceptual framework for integrating AI into engineering education, with the aim of equipping the students with the skills and knowledge required to navigate the complexity of the modern engineering landscape. This theoretical exploration underscores the transformative potential of AI in engineering education and sets a foundation for future empirical research to further investigate and expand upon these benefits and strategies.