Integrating AI Education in Disciplinary Engineering Fields: Towards a System and Change Perspective
Project Overview
The document explores the integration of generative AI in education, particularly within engineering fields, stressing the importance of equipping students with competencies in AI tools and knowledge. It identifies the challenges and strategies for curricular transformation, pointing to the adoption of generative AI tools like ChatGPT as a significant factor in enhancing educational outcomes. By proposing a systems perspective, the paper aims to elucidate how AI education can be seamlessly incorporated into engineering programs, while also addressing various drivers and barriers to this integration. Key applications include personalized learning experiences, automated tutoring, and enhanced problem-solving capabilities, with findings suggesting that generative AI can significantly enrich educational practices and better prepare students for future technological landscapes. Overall, the document advocates for a comprehensive approach to embedding AI education across curricula, highlighting its potential to transform teaching and learning in engineering disciplines.
Key Applications
Curricular development of a Bachelor program in AI Engineering
Context: Engineering education with a focus on integrating AI competencies for students
Implementation: Developed through a collaborative process using curriculum workshops, following a re-build strategy to create an interdisciplinary curriculum
Outcomes: Students become AI engineers capable of developing data-driven solutions, bridging engineering and AI knowledge, and enhancing problem-solving skills
Challenges: Resistance to change within the institution, need for faculty training in AI, and securing resources and funding
Implementation Barriers
Institutional Culture
Resistance to change regarding novel approaches to teaching and curriculum structure
Proposed Solutions: Engaging stakeholders in the development process and demonstrating the benefits of AI integration
Resource Availability
Limited resources, especially concerning faculty and computing resources for students
Proposed Solutions: Securing external funding and support to establish programs and infrastructure
Faculty Readiness
Faculty may lack expertise and skills in AI, affecting the readiness for curriculum changes
Proposed Solutions: Providing training and support structures for faculty development in AI
Project Team
Johannes Schleiss
Researcher
Aditya Johri
Researcher
Sebastian Stober
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Johannes Schleiss, Aditya Johri, Sebastian Stober
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