The chapter investigates how artificial intelligence (AI) technologies are incorporated into engineering education and looks at how they affect methods of instruction and learning. An overview of the many uses of AI in engineering education is given, including data analytics, intelligent tutoring systems, adaptive learning platforms, virtual laboratories, and robotics training made possible by AI. The chapter explores how AI may improve the engineering curriculum by addressing market demands and future skills, integrating AI tools and software, and introducing AI concepts into coursework. It also discusses performance monitoring, intelligent feedback, automated grading, competency assessment in engineering education, and AI-assisted assessment and feedback systems. The chapter also looks at how AI affects engineering education, including using augmented and virtual reality, flipped classrooms, personalized learning, and collaborative learning.
Top1. Introduction
Industry after industry, AI is quickly becoming a game-changer, and engineering education is no exception. The way study of students, practice, and apply engineering principles could be completely changed by incorporating AI technologies into engineering education. This chapter will examine the various uses of AI technology in engineering education as well as the advantages and difficulties of putting them into practice. We'll look at how AI can improve conventional teaching strategies and open up new possibilities for individualized, interactive learning. Adaptive learning systems are one of the main areas where AI technologies have had a big impact (Pedro, Subosa, Rivas, & Valverde, 2019). By analyzing student performance data and customizing instructional content to meet each student's needs and learning preferences, these systems make use of machine learning techniques. AI-powered systems can streamline the learning process and help students better understand complicated engineering ideas by intelligently adjusting the curriculum and offering customized feedback (Adiguzel, Kaya, & Cansu, 2023; M. M. Saeed, Mohammed, et al., 2023).
Machine Learning (ML) has various applications in the field of education in engineering. Here are some types of ML used in education in engineering such as personalized learning, intelligent tutoring systems, recommender systems, automated grading and feedback, predictive analytics, Natural Language Processing (NLP) for text analysis, virtual laboratories and simulations, and adaptive assessments:
Furthermore, by offering immersive and interactive learning environments, AI technologies like virtual reality (VR) and augmented reality (AR) have completely changed engineering education. Previously unavailable, students can now participate in realistic simulations and hands-on learning opportunities. Engineering topics can be verified, replicated, and represented using VR and AR, allowing students to improve their problem-solving skills and acquire useful talents.
Nevertheless, there are certain difficulties in integrating AI technologies into engineering education. To guarantee the responsible application of AI, ethical issues, data privacy, and algorithmic biases must be properly addressed. To successfully incorporate AI into teaching techniques, faculty professional development is necessary and may also encounter opposition to change (Alam, 2021a; M. M. Saeed, R. A. Saeed, R. A. Mokhtar, et al., 2022; M. M. Saeed, Saeed, & Saeid, 2021).
We will examine these subjects and talk about the practical applications of AI in engineering education in this chapter. We'll also look at the prospects and possible developments in this area going forward, emphasizing how AI can completely change engineering students' education and equip them for the demands of the contemporary world.
Educators and institutions can make well-informed judgments regarding incorporating AI technologies into their curricula by having a thorough awareness of the applications, advantages, difficulties, and prospects of these technologies in engineering education. The objective is to establish a stimulating, welcoming, and productive learning environment that gives engineering students the information and abilities they need to prosper in a world driven by technology (Lijia Chen, Chen, & Lin, 2020).
Come along as we explore the amazing possibilities that AI technologies hold for influencing engineering practice and learning as we set out to explore its use in engineering education. Figure 1 shows the design for Artificial Intelligent education.
Figure 1. AI education conceptual design and structure
The chapter provides insight into how AI technology might transform engineering education and learning, making several noteworthy contributions: