designs. Introducing these toolswithin this course exposes students to modern computational methods that are becomingincreasingly relevant in real-world mechanical engineering applications.Additionally, Kinematics of Machines, though fundamental, provides a well-defined problemspace with clear inputs and outputs. This structured environment allows for the gradualintroduction of ML/AI, providing students with digestible and understandable applications ofadvanced techniques without overwhelming them. MATLAB, which is commonly used in thecourse, serves as an accessible platform for integrating these tools, given its extensive support forboth numerical analysis and machine learning toolkits.Modification 1: Position Analysis using computer based
Assessment:At the start of the semester, after completing the first unit of the course, students will participatein a preliminary Java programming lab. This unit will cover fundamental concepts such asprinting statements in Java, declaring variables (e.g., int, double, String), and simple functions.Students will not receive instruction on how to format decimal places, a concept introduced as achallenge in the lab.The lab will consist of four programming tasks: 1. Print: “Hello, How are you?” 2. Print: “Hello, my name is .” 3. Calculate and print the result of the expression (8 + 9) * 4. 4. Calculate the average of 85.5 and 90.75, then print the result with two decimal places. (Note: Students have not been taught how to format decimal
students in theresponsible and acceptable use of AI platforms, providing them with opportunities and guidanceto explore and leverage this new technology. The potential of ChatGPT in the classroom has beenanalyzed in various studies [3-5], highlighting its applications as a writing assistant, study tool,and personal tutor [6]. However, there are also concerns that the overreliance on ChatGPT mayadversely affect students' critical thinking and problem-solving skills [7]. As AI becomes moreprevalent in higher education, it is essential for educators, curriculum designers, andpolicymakers to understand the implications of integrating these tools into the educationalcontext.According to research [7], ChatGPT has the ability to respond immediately to
applications overtheoretical foundations is often influenced by the need to align with industry requirements.Lissenden et al. from Mechanical Engineering of Penn State reported that finding out a consensusfrom faculty, student, and industry on the optimum learning objectives of finite element course wasvery difficult. The following question is very typical during the planning of a finite element course:should the focus be given on writing codes, understanding finite element results, masteringcommercial finite element software, or understanding the finite element method itself [6]. Watkinsreported a curriculum change of the Finite Element Analysis (FEA) course at California StateUniversity Chico. The course initially focused heavily on theoretical
for thediscussions. Required standard academic qualifications to teach engineering courses will bebriefly stated through a review of current practices at colleges and universities in the US and insome other parts of the world in conjunction with personal observations and interviews madesporadically over the years by the author. As will be shown later, despite proven and numerousreal advantages of such a practice, legitimate concerns and possible fundamental flaws exist aswell.Connection between Mathematics and Science; and Engineering:Engineering is highly intertwined with science and mathematics. The connection betweenengineering with science and mathematics manifests itself in so many ways and at variousdomains [1]. It starts with K-12
Paper ID #45369The Impact of AI Assistance on Student Learning: A Cross-DisciplinaryStudy in STEM EducationProf. Matthew Fried, SUNY Farmingdale Matthew Fried is an Assistant Professor with a research focus in machine learning. His work includes the application of advanced mathematical techniques, such as the Choquet integral, to deep neural networks (DNNs). He has presented multiple papers on this topic at international conferences, contributing to the ongoing development of noise reduction and performance optimization in DNNs. ©American Society for Engineering Education, 2024 The Impact
Paper ID #45316LEVERAGING GENERATIVE AI TO ENHANCE ENGINEERING EDUCATIONAT BOTH LOW-LEVEL AND HIGH-LEVEL STUDYDr. Zhou Zhang, SUNY Farmingdale State College I am an Assistant Professor at SUNY Farmingdale State College. My teaching and research interests include robotics and virtual reality in engineering education. I have a Ph.D. and a bachelor’s degree in Mechanical Engineering, and my master’s degree is in Electrical Engineering. I have over seven years of industrial experience as an electrical and mechanical engineer. I also have extensive teaching and research experience with respect to various interdisciplinary