A Roles-based Competency Framework for Integrating Artificial Intelligence (AI) in Engineering Courses
Project Overview
The document explores the integration of Artificial Intelligence (AI) into engineering education, emphasizing the growing necessity for AI literacy within engineering curricula. It introduces a roles-based competency framework, RCAIE, designed to help educators identify and implement essential AI competencies effectively. Illustrated through two case studies—Predictive Maintenance and Quality Control and Testing—the document showcases practical applications of AI in real-world engineering contexts. It underscores the importance of interdisciplinary approaches to AI education while addressing various challenges educators face in its implementation. Overall, the findings highlight the significance of equipping students with AI skills to prepare them for the demands of the modern engineering landscape.
Key Applications
AI-Driven Predictive Quality Assurance
Context: Applied in manufacturing and civil engineering to enhance both predictive maintenance and quality control processes. The target audience includes engineering students and professionals who require knowledge in maintaining equipment and ensuring product quality.
Implementation: Implemented through AI-based solutions that analyze machine condition data for predictive maintenance and utilize automatic defect detection for quality assessment, employing similar technologies and methodologies for data analysis and machine learning.
Outcomes: ['Reduces unplanned downtime', 'Enhances overall equipment effectiveness', 'Increases product quality', 'Reduces defects through enhanced testing capabilities']
Challenges: ['Requires understanding of AI and data analysis among faculty', 'Dependence on the availability of quality data', 'Need for faculty training in AI technologies']
Implementation Barriers
Educational Barrier
Educators often lack a background in AI, making it hard to integrate AI into curricula.
Proposed Solutions: Provide training and resources for educators to develop AI competencies.
Data Availability Barrier
Data quality and availability can be inconsistent, affecting AI applications.
Proposed Solutions: Implement strategies for data collection and management to ensure quality inputs.
Project Team
Johannes Schleiss
Researcher
Aditya Johri
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Johannes Schleiss, Aditya Johri
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