2026 ASEE Annual Conference & Exposition

Streamlining Industry-Sponsored Capstone Documentation with MBSE: Enabling Architecture Trade Studies and Early System Level Simulation

Presented at Design in Engineering Education Division (DEED) Technical Session 1: Artificial Intelligence as a Design Partner in the Classroom

This full paper reports an evidence-informed academic practice from the Electrical and Computer Engineering Department at the University of South Florida that applies Model-Based Systems Engineering (MBSE) to address a recurring challenge in industry-sponsored capstone projects: documentation that is inconsistent across teams and difficult for sponsors and faculty to review efficiently, particularly when multiple design alternatives must be compared and justified. Building on a program-wide systems engineering foundation aligned with the V-model, USF integrates MBSE across the curriculum, from the Professional Formation of Engineers (PFE) sequence, through the two-semester capstone, supported by a Canvas module with curated MBSE and Systems Modeling Language (SysML) project examples. In Capstone I, teams use an MBSE-informed workflow implemented with modeling tools to produce a model-centered “digital thread,” using diagrams and tables that link stakeholder needs, requirements, functions, candidate architectures, trade-study criteria, and verification intent.
Within this workflow, teams document architecture alternatives and conduct trade-off analysis using explicit evaluation criteria tied to requirements, supporting transparent design decisions. Where appropriate, teams also use AI-assisted tools to support requirements refinement and accelerate architecture exploration. The workflow requires an early feasibility check in the form of a high-level system simulation, defined here as a simplified executable representation that validates key functional behavior and interfaces at the system level, rather than detailed component modeling, completed by the end of Capstone I. Observed outcomes and lessons learned are synthesized from sponsor/SME feedback, poster-session evaluator input, including a standardized rubric summarized descriptively using Fall 2025 as an illustrative compiled example, and faculty observations. Across these evidence sources, stakeholders consistently emphasized clearer requirements, stronger decision traceability, more effective stakeholder communication, and increased attention to early verification planning. The paper describes the curricular integration and minimum artifact set, summarizes recurring patterns identified across evidence sources, and outlines next evaluation steps and tooling directions as SysML capabilities evolve.

Authors

Dr. Pallavi Singh University of North Florida [biography]

Pallavi Singh, PhD is an Assistant Professor in the Department of Electrical Engineering at the University of North Florida, Jacksonville, Florida. Her research interests include engineering education, STEM education, STEM workforce development, digital manufacturing, sustainability, industrial automation, machine vision, and biomedical research, with a focus on image analysis and machine learning models for biomedical image analysis.
She served as the Program Coordinator for the Manufacturing Certificate Program from 2024 to 2025 and has been an active participant in Florida manufacturing associations and manufacturing-focused workforce development initiatives.

Dr. Singh received her Ph.D. in Electrical Engineering in 2024 from the University of South Florida. She earned her first Master’s degree in Electrical Engineering from the University of South Florida, Tampa, Florida, in 2019. Her work in engineering education focuses on enhancing student performance and learning at the undergraduate and graduate levels through the application of machine learning, model-based systems engineering (MBSE), and educational data mining.
Luis Miguel Quevedo http://orcid.org/0009-0006-0018-792X University of South Florida [biography]

Luis Miguel Quevedo is a Mechatronics Engineer and M.S. Electrical Engineering candidate at the University of South Florida, where his work focuses on AI-enabled engineering workflows, MBSE/SysML 2.0, and FPGA-based prototyping for reliable next-generation electronic systems. He currently works as an Embedded Engineer Intern and has contributed to firmware, documentation, and production readiness for embedded platforms. He is also President and Founder of the USF IEEE Electron Devices Society Student Chapter and a co-author of recent IEEE LAEDC 2025 papers, including a Best Paper award.
Joel Howell University of South Florida [biography]

Joel Howell is a Professor of Practice in the University of South Florida's Electrical Engineering Department. His focus is to help students within the department develop professional skills and competencies through experiential learning activities, including student organization leadership, internships, research, and community volunteering.
Dr. Chung Seop Jeong University of South Florida [biography]

Chung Seop Jeong is a Professor of Instruction in the Electrical and Computer Engineering Department at the University of South Florida
Dr. Wilfrido A. Moreno P.E. University of South Florida [biography]

Wilfrido A. Moreno received his M.S. and Ph.D. degrees in Electrical Engineering from the University of South Florida (USF). His teaching and research interests include control systems, systems engineering, and Model-Based Systems Engineering (MBSE). Dr. Moreno has led multiple initiatives integrating industry-sponsored capstone projects and systems thinking into the curriculum and has been actively involved in international engineering education and research collaborations. He is currently a Professor in the Department of Electrical and Computer Engineering at USF.

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