either game as we will explain the rules during class. Furthermore, someliterature suggests that students think gamification is an engaging form of learning regardless oftheir prior gaming experiences [22]. Based on the learning objectives from Engineering for OnePlanet, the gamified course will expose students to environmental and social sustainability andsystems thinking. Students will develop skills such as teamwork, communication,problem-solving, critical thinking, and environmental assessment. Finally, students will alsoexplore ethical and technical problems. Our game design process includes identifying gamegoals, elements, dynamics, and mechanics and connecting them to the technical knowledge andskill objectives for statics.Game GoalsThe
both the ASCE ExCEEd New Faculty Excellence in Civil Engineering Education Award (2012) and the Beer and Johnston Outstanding New Mechanics Educator Award (2013). ©American Society for Engineering Education, 2025Exploring the Capability of Generative AI as an Engineering Lab Report AssessmentAssisting Tool AbstractSince ChatGPT’s public launch in November 2022, considerable discussion and changes haveoccurred in higher education. Active educational research related to generative artificialintelligence (GAI) has been conducted in various areas, including student learning, ethics, andassessment. Although many authors have raised concerns about the impact of GAI
/PSF13/Session/E1.413. Schoenfeld, A. H. (2014). Mathematical problem solving. Elsevier. Accessed: Jan. 29, 2024. [Online]. Available: https://www.elsevier.com/books/mathematical-problem-solving/schoenfeld/978-0-12-628870 -414. Martin, T., Rayne, K., Kemp, N.J., Hart, J., & Diller, K.R. (2005). Teaching for Adaptive Expertise in Biomedical Engineering Ethics. Science and Engineering Ethics, Vol. 11(2), pp. 257-276.15. Martin, T., Rivale, S.D., & Diller, K.R. (2007). Comparison of Student Learning in Challenge-based and Traditional Instruction in Biomedical Engineering. Annals of Biomedical Engineering, Vol. 35, pp. 1312–1323.16. Rayne, K., Martin, T., Brophy, S., Kemp, N. J., Hart, J. D., & Diller, K.R. (2006
University (GWU) and at the University of Vermont. He is also a Research Affiliate at George Mason University’s Center for Assured Research and Engineering. He is a member of the GWU Human-Technology Collaboration Lab, and Founding Director of the university’s Master’s Program in Data Science. Larry specializes in areas of artificial intelligence, data science, computer science, neural computing, information systems, physics, and STEM education. He is the author of four books and over 200 publications on neural networks, AI, and physics. He serves as Co-Editor-in-Chief of AI and Ethics, Associate Editor of Neural Computing and Applications, and Policy Officer for ACM’s Special Interest Group on Artificial Intelligence
Wisconsin, Milwaukee. Papadopoulos has diverse interests in structural mechanics, sustainable construction materials (with emphasis in bamboo), engineering ethics, and engineering education. He is co-author of Lying by Approximation: The Truth about Finite Element Analysis, and after many years, he has finally (maybe) learned how to teach Statics, using an experiential and peer-based learning ”studio” model. As part of the UPRM Sustainability Engineering initiative to develop a new bachelor’s degree and curricular sequence, Papadopoulos is PI of A New Paradigm for Sustainability Engineering: A Transdisciplinary, Learner-Centered, and Diversity-Focused Approach, funded by the NSF HSI program. Papadopoulos is active in the
percentage of students described the design, manufacture, and analysis/testing ofmachinery as the defining elements of mechanical engineering, many also highlighted the goalof engineering to solve problems to improve society in a safe and ethical way. While notexplicit, their descriptions focus on machinery and do not clearly indicate the thermal-fluids orthe electronic elements of mechanical engineering. This general idea that they will use solidmechanics knowledge more than fluid mechanics, thermal sciences, or electronic integration orcontrol is also shown in what sub-discipline they expect to use most and least (Fig. 1B). Almostall our students (88%) anticipate using solid mechanics knowledge more than any otherdiscipline in their future careers
“Artificial Intelligence” or “AI” in the title. The set can beexpanded to over 100 by adding terms such as “Machine Learning”, “Large Language Models”,or “Generative”. Results are spread across most ASEE divisions, reflecting the intense interestengineering educators have in using modern AI-based tools in the classroom. Proposed uses ofAI are too many to enumerate here, but broad topics include techniques for teaching studentshow to use AI, recommendations to instructors on using AI tools to assist with curriculumdevelopment and assessment, the ethics of AI use in the classroom, and advances in AI forsolving engineering problems.Given the focus on these emerging tools by educators and students alike, it is imprudent toignore their use in any field of
design or engineering ethics. Future workbeing considered in this course is to expand the use of these real world applications beyondlectures and into homework assignments and student discussion activities. Table 2: Summary of Quantitative Survey Responses Statement 1 Statement 2 Statement 3 Statement 4 Response Creating Connections Curiosity Creating Future Value Value Agree Start: Combined 65.0% 62.8% 54.4% 65.6% End: Control 71.8% 75.0% 68.5% 70.2% End: Test 80.5% 74.2
instrument. The platform now has a bank of about 300 concept questions for Staticsand is very effective to elicit student written responses, drive conversation, and peer more deeplyinto student reasoning (Papadopoulos et al., 2022). However, even though there is a high rate ofstudent response, the corresponding grade weight was no more than 5% of the grade. In thiscontext, I have not yet established firm evidence that experience with concept questionsimproves performance on procedural test questions, although this has been previously argued(Koretsky et al., 2016)During the last decade or so, I have also begun to “contextualize” problems in both homeworkand exam settings to address issues of ethics, social justice and sustainability (Leydens &
average, students can work to understand their mistakes during the term instead of during the last week of the term after testing day. Also, an analysis of the class average design requires Statics skills but a competition-winning design requires a full consideration of the kinematics, best-case hole location, worst-case retraction angle and force, and accuracy of the laser cutter, amongst other concerns. The winning designs often have disturbingly small factors of safety, which might send a troubling message to students about engineering ethics. Simply put, the class average approach followed by Instructor C attempts to keep fundamental Statics skills at the center of the work. It also avoids the public