2025 ASEE Annual Conference & Exposition

Students' Perception of Artificial Intelligence Applications in Workload Distribution, Performance Monitoring, And Improving Collaboration in Engineering Teams.

Presented at AI in the Engineering Management Classroom

The transformative potential of artificial intelligence (AI) has yet to be fully embraced by both academia and industry. While many have adopted and applied AI technologies, there remains skepticism regarding their role in shaping the future of work. Recently, attention has been focused on understanding the ethical use and implications of AI. Though progress has been made, much work is still required to ensure that AI serves the common good. One undeniable fact, however, is the significant impact AI is already making. The integration of AI into workplaces is transforming organizational operations. When implemented effectively, AI can enhance efficiency, automate repetitive tasks, and enable informed, timely decision-making. Technologies such as neural networks and natural language processing streamline tasks and improve customer interactions. In industries like manufacturing and logistics, engineering managers are using AI to optimize supply chains, improve product quality, and reduce downtime. They also leverage AI for predictive maintenance, performance evaluation, and project monitoring and control. Despite this progress, ethical concerns and limited knowledge about AI's potential may hinder its early adoption in engineering. This paper reviews engineering management students’ perceptions of AI in workload distribution, performance monitoring, and enhancing collaboration in engineering teams. Preliminary findings reveal concerns about job displacement, data privacy, and transparency. While many see the potential benefit of AI in a collaborative workspace, a significant number of the respondents express their lack of readiness to accept AI integration for performance monitoring and workload assignment. Thus, since many engineering students are eventually going to graduate and become engineering managers, who may utilize AI tools, engineering educators and researchers must continue to explore ways to enhance students’ familiarity and proficiency with AI systems.

Authors
  1. Dr. Philip Appiah-Kubi University of Dayton [biography]
  2. Dr. Khalid Zouhri University of Dayton [biography]
  3. Dr. Yooneun Lee University of Dayton [biography]
Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025