Harnessing Multi-Agent LLMs for Complex Engineering Problem-Solving: A Framework for Senior Design Projects

Document Type

Conference Proceeding

Source of Publication

IEEE Global Engineering Education Conference Educon

Publication Date

6-3-2025

Abstract

Multi-Agent Large Language Models (LLMs) are gaining attention for their ability to harness collective intelligence in complex problem-solving, decision-making, and planning tasks. This aligns with the wisdom of crowds concept, where diverse agents collectively generate effective solutions, making them well-suited for educational settings. Senior design projects, pivotal in engineering education, integrate theoretical knowledge with practical application, fostering critical thinking, teamwork, and real-world problem-solving skills. These projects often involve multidisciplinary considerations and conflicting objectives, such as optimizing technical performance while addressing ethical, social, and environmental concerns. In this paper, we explore a framework where distinct LLM agents embody expert perspectives, including problem formulation, system complexity, societal and ethical considerations, and project management. These agents engage in rich, collaborative dialogues, leveraging multi-agent system principles like coordination, cooperation, and negotiation. Prompt engineering is employed to create diverse personas, simulating human engineering teams and incorporating swarm AI principles to balance contributions efficiently. To evaluate the framework, we analyzed six senior capstone project proposals from engineering and computer science, comparing Multi-Agent and single-agent LLMs using metrics developed with engineering faculty and widely used NLP-based measures. These metrics assess technical quality, ethical considerations, social impact, and feasibility, aligning with the educational objectives of engineering design. Our findings suggest that Multi-Agent LLMs can provide a richer, more inclusive problem-solving environment compared to single-agent systems with 89% alignment with engineering-faculty scores, offering a promising tool for enhancing the educational experience of engineering and computer science students by simulating the complexity and collaboration of real-world engineering and computer science practice. By supporting senior design projects, this tool not only aids in achieving academic excellence but also prepares students for the multifaceted challenges they will face in their professional engineering careers. We have open-sourced our framework for further development and adaptation on GitHub11Copilot is available at GitHub Repository: https://github.com/AbdullahMushtaq78/Multi-Agent-SDP-Copliot.

ISBN

[9798331539498]

ISSN

2165-9559

Publisher

IEEE

Disciplines

Computer Sciences | Education

Keywords

Agentic AI, Gen AI, Large Language Models, LLM Agents, LLM-Based Multi-Agent Systems, Multi-Agent Collaboration

Scopus ID

105008184573

Indexed in Scopus

yes

Open Access

no

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