Work in Progress: AI-Powered Engineering-Bridging Theory and Practice
Project Overview
The document explores the role of generative AI in engineering education, emphasizing its potential to automate and enhance systems engineering processes. It details how AI tools can analyze and classify system requirements against established standards, thereby streamlining the educational workflow. The research evaluates the effectiveness of AI in this context by comparing its performance to that of seasoned engineers, while also considering ethical implications and the necessity for responsible AI use in educational settings. The findings indicate that the integration of AI not only improves learning outcomes but also helps students develop practical skills and critical thinking abilities, ultimately preparing them for real-world engineering challenges.
Key Applications
AI-driven Requirements Analysis and Classification
Context: Engineering education for students in systems engineering programs
Implementation: Integration of Natural Language Processing (NLP) and Machine Learning (ML) techniques to classify and analyze system requirements based on INCOSE's criteria.
Outcomes: Increased efficiency in analyzing requirements, improved understanding of quality issues, and enhanced learning outcomes for students.
Challenges: AI's tendency to generate misinterpretations or 'hallucinations' and the need for ethical considerations in AI applications.
Implementation Barriers
Technical Barrier
AI models may struggle with contextual misunderstandings and inaccuracies.
Proposed Solutions: Refinement of AI algorithms, training models specifically for systems engineering tasks, and iterative improvements based on expert feedback.
Ethical Barrier
Potential risks associated with AI applications in critical systems and the need for responsible use.
Proposed Solutions: Fostering ethical awareness in engineering education and integrating discussions on AI ethics into curricula.
Project Team
Oz Levy
Researcher
Ilya Dikman
Researcher
Natan Levy
Researcher
Michael Winokur
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Oz Levy, Ilya Dikman, Natan Levy, Michael Winokur
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai