Whole-Person Education for AI Engineers
Project Overview
The document examines the transformative potential of generative AI in education, advocating for a holistic, whole-person approach in AI engineering education that merges technical skills with ethical considerations and social responsibility. It critiques traditional educational paradigms that prioritize efficiency at the expense of broader societal implications, emphasizing the necessity for engineering students to engage with real-world challenges to foster critical thinking and creativity. Key applications of AI in education are highlighted, particularly its ability to enhance reasoning and communication skills among students. However, the document also addresses challenges such as inherent biases in AI systems and the need for explicit instruction to navigate these tools effectively. Overall, the findings suggest that by integrating diverse perspectives and interdisciplinary collaboration, educational institutions can cultivate responsible AI technologies capable of addressing global challenges while promoting social justice.
Key Applications
AI-Enhanced Feedback and Holistic Education
Context: AI tools are integrated into engineering education to support future engineers in enhancing their technical skills, ethical reasoning, and communication abilities. This includes providing feedback on student writing and promoting a whole-person education approach through collaborative methods involving diverse stakeholders.
Implementation: Integration of AI tools that provide feedback on student writing and reasoning, coupled with collaborative autoethnography involving diverse stakeholders. This approach fosters the connection between technical skills and ethical/social responsibilities.
Outcomes: Students improve their complex reasoning, communication skills, and ethical awareness, leading to a more holistic understanding of their roles in society and responsible technology development.
Challenges: Resistance to change in traditional curricula, the need for interdisciplinary collaboration, ensuring effective use of AI tools, and embedding ethical frameworks in technical education.
Implementation Barriers
Curricular Barrier
Current engineering curricula often treat ethics as an add-on rather than a core component of technical training. Additionally, students may not learn to use AI effectively without structured guidance.
Proposed Solutions: Integrate ethical frameworks into the engineering curriculum, emphasizing critical thinking about societal implications of technology. Incorporate explicit instruction in AI use within the curriculum to develop teamwork and reasoning skills.
Cultural Barrier
Siloed disciplines lead to a lack of interdisciplinary collaboration, hindering the development of well-rounded engineers.
Proposed Solutions: Promote interdisciplinary learning experiences and partnerships between engineering and social sciences.
Technical Barrier
Crafting prompts that elicit valuable insights from AI tools can be challenging. Students may struggle to use AI effectively without structured guidance.
Proposed Solutions: Provide explicit instruction on using AI tools and iterative feedback on prompt effectiveness.
Project Team
Rubaina Khan
Researcher
Tammy Mackenzie
Researcher
Sreyoshi Bhaduri
Researcher
Animesh Paul
Researcher
Branislav Radeljić
Researcher
Joshua Owusu Ansah
Researcher
Beyza Nur Guler
Researcher
Indrani Bhaduri
Researcher
Rodney Kimbangu
Researcher
Nils Ever Murrugarra Llerena
Researcher
Hayoung Shin
Researcher
Lilianny Virguez
Researcher
Rosa Paccotacya Yanque
Researcher
Thomas Mekhaël
Researcher
Allen Munoriyarwa
Researcher
Leslie Salgado
Researcher
Debarati Basu
Researcher
Curwyn Mapaling
Researcher
Natalie Perez
Researcher
Yves Gaudet
Researcher
Paula Larrondo
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Rubaina Khan, Tammy Mackenzie, Sreyoshi Bhaduri, Animesh Paul, Branislav Radeljić, Joshua Owusu Ansah, Beyza Nur Guler, Indrani Bhaduri, Rodney Kimbangu, Nils Ever Murrugarra Llerena, Hayoung Shin, Lilianny Virguez, Rosa Paccotacya Yanque, Thomas Mekhaël, Allen Munoriyarwa, Leslie Salgado, Debarati Basu, Curwyn Mapaling, Natalie Perez, Yves Gaudet, Paula Larrondo
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