Generative AI Learning Environment for Non-Computer Science Engineering Students: Coding Versus Generative AI Prompting

TY - GEN

T1 - Generative AI Learning Environment for Non-Computer Science Engineering Students:

T2 - 4th International Conference on AI Research

AU - Alphinas, Robert

AU - Tambo, Torben

N1 - Conference code: 4

PY - 2024/12/6

Y1 - 2024/12/6

N2 - As the integration of artificial intelligence (AI) proliferates across various disciplines, the demand for understanding AI concepts and applications extends beyond traditional computer science domains. This paper addresses the need for a positive and effective learning environment for engineering students in non-computer science fields to grasp Generative AI principles while navigating the intricate balance between its application and developmental insights. Drawing from pedagogical theories and cognitive science, especially Leinenbach and Corey (2004)’s Universal Design for Learning, this study proposes a framework tailored to the unique needs and backgrounds of engineering students. The framework emphasizes active learning strategies, collaborative problem-solving, and real-world applications to engage learners in meaningful experiences with Generative AI concepts. Additionally, the framework underscores the importance of cultivating a supportive and inclusive learning environment that fosters curiosity, experimentation, and resilience in the face of challenges inherent in AI development. The central learning context is a MSc program in management engineering with a course/training opportunity in Machine Learning Fundamentals using Python based on Google Collab. The introduction of Generative AI is based on selected Google libraries for Python.Furthermore, the paper explores various instructional approaches and tools to scaffold students' understanding of Generative AI, including hands-on projects, case studies, and interactive simulations. It also addresses ethical considerations and societal implications associated with Generative AI deployment, encouraging students to critically reflect on the broader impacts of their technical decisions. Through a synthesis of pedagogical best practices and AI development principles, this paper contributes to the ongoing discourse on effective AI education for non-computer science disciplines. By embracing a holistic approach that integrates theory with practical application, educators can empower engineering students to harness the transformative potential of Generative AI while navigating its complexities responsibly and ethically.

AB - As the integration of artificial intelligence (AI) proliferates across various disciplines, the demand for understanding AI concepts and applications extends beyond traditional computer science domains. This paper addresses the need for a positive and effective learning environment for engineering students in non-computer science fields to grasp Generative AI principles while navigating the intricate balance between its application and developmental insights. Drawing from pedagogical theories and cognitive science, especially Leinenbach and Corey (2004)’s Universal Design for Learning, this study proposes a framework tailored to the unique needs and backgrounds of engineering students. The framework emphasizes active learning strategies, collaborative problem-solving, and real-world applications to engage learners in meaningful experiences with Generative AI concepts. Additionally, the framework underscores the importance of cultivating a supportive and inclusive learning environment that fosters curiosity, experimentation, and resilience in the face of challenges inherent in AI development. The central learning context is a MSc program in management engineering with a course/training opportunity in Machine Learning Fundamentals using Python based on Google Collab. The introduction of Generative AI is based on selected Google libraries for Python.Furthermore, the paper explores various instructional approaches and tools to scaffold students' understanding of Generative AI, including hands-on projects, case studies, and interactive simulations. It also addresses ethical considerations and societal implications associated with Generative AI deployment, encouraging students to critically reflect on the broader impacts of their technical decisions. Through a synthesis of pedagogical best practices and AI development principles, this paper contributes to the ongoing discourse on effective AI education for non-computer science disciplines. By embracing a holistic approach that integrates theory with practical application, educators can empower engineering students to harness the transformative potential of Generative AI while navigating its complexities responsibly and ethically.

KW - artificial intelligence

KW - machine learning

KW - generative ai

KW - industrial engineering

KW - production engineering

KW - operations management

KW - digitalisation

U2 - 10.34190/icair.4.1.3220

DO - 10.34190/icair.4.1.3220

M3 - Article in proceedings

BT - Proceedings of the 4th International Conference on AI Research, ICAIR 2024

PB - Academic Publishing

Y2 - 5 December 2024 through 6 December 2024

ER -