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 necessity for students to acquire competencies in AI tools and data analysis. It employs a systems perspective to analyze both internal and external factors that influence the adoption of AI in engineering curricula, proposing a change model to facilitate this integration. The authors identify key curriculum change strategies, including add-on, integration, and re-build approaches, and substantiate their recommendations with insights drawn from case studies and existing literature. The findings indicate that effectively incorporating generative AI into educational frameworks can enhance learning experiences and better prepare students for the evolving demands of the workforce, ultimately fostering a more competent and innovative generation of engineers.
Key Applications
Bachelor program of AI engineering at Otto von Guericke University Magdeburg
Context: Higher education program aimed at integrating AI and engineering disciplines for students aspiring to become AI engineers.
Implementation: Developed through a collaborative and participatory process using curriculum workshops; structured into fundamental courses, specializations, projects, electives, an internship, and a thesis.
Outcomes: Creates interdisciplinary skillsets focusing on competencies to work with data and AI models, and encourages systematic problem-solving and effective communication across disciplines.
Challenges: Resistance to change in teaching approaches, need for significant faculty training, resource availability, and balancing internal and external influences in curriculum design.
Implementation Barriers
Cultural Barrier
Initial resistance to changes in teaching and curriculum structure within the institution.
Proposed Solutions: Engagement with faculty and stakeholders to demonstrate the value of AI education and the benefits of integrating AI tools.
Resource Barrier
Limited availability of resources, especially concerning faculty staff and computing resources for students.
Proposed Solutions: Securing external funding and support to enhance resources and staffing for effective AI education.
Curriculum Development Barrier
The complexity of integrating AI competencies into existing engineering curricula.
Proposed Solutions: Adopting a systematic framework for curricular change and utilizing collaborative workshops for curriculum design.
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