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Enhancing Education Through Thoughtful Integration of Large Language Models in Assigned Work

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Conference

2024 ASEE-GSW

Location

Canyon, Texas

Publication Date

March 10, 2024

Start Date

March 10, 2024

End Date

March 12, 2024

Page Count

9

DOI

10.18260/1-2--45377

Permanent URL

https://peer.asee.org/45377

Download Count

134

Paper Authors

biography

Tonia Haikal Texas A&M University

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Toni Haikal is currently a senior Computer Engineering student with plans to pursue a master’s degree in computer
engineering while engaging in the industry. Her research interests are deeply rooted in machine learning models and
artificial intelligence, specifically their application and influence in the domains of cybersecurity and cloud computing.
Haikal is particularly focused on the development of advanced AI-driven security protocols and the optimization of
cloud infrastructure through intelligent algorithms.

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biography

Robert Harold Lightfoot Jr Texas A&M University Orcid 16x16 orcid.org/0000-0002-6446-5857

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Robert Lightfoot received his master's degree in software engineering from Southern Methodist University and his bachelor's degree in computer science from Texas A&M. Before joining Texas A&M as and Associate Professor of Practice, he worked at Ericsson (now Sony-Ericsson) in the network division, then with DSC/Motorola in the Cellular Infrastructure Group.

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Abstract

In a world where technology is evolving rapidly, it is essential to note its significant intrusion into the field of education. Technology has made vast amounts of information accessible to students, making them over-reliant on technology and less reliant on nurturing their knowledge and imagination. While limiting technology's usage is impossible to stop, learning how to incorporate it efficiently in the educational system is essential. Integrating machine learning (ML) and artificial intelligence (AI) in education is a significant shift in educational methodologies. This transformation offers the possibility to change learning approaches while presenting challenges in the ethical field. This research paper explores the impact of machine learning (ML) and artificial intelligence (AI), particularly large language models like Chat GPT, on education in our classrooms. This topic is essential because it signifies a change in the methods that educators and students use to engage in a course, transforming the learning outcomes while upholding ethical principles. The application of ML and AI in education has attracted increasing attention, but the long-term effects of these technologies on learning achievements require further investigation. Therefore, we aim to find an approach that allows the integration of ML and AI, specifically Chat GPT, while maintaining high expectations in our classrooms. While tools like Chat GPT hold transforming educational potentials, their integration must be navigated thoughtfully, balancing technological advancements with concept learning and acquisition. In this paper, we utilize a mixed-methods approach, combining quantitative analysis of educational outcomes with observational research to understand the impact of our different approaches in our assignments. This approach allows us to see firsthand how these technologies are integrated into the classroom and how they affect teaching and learning dynamics.

Haikal, T., & Lightfoot, R. H. (2024, March), Enhancing Education Through Thoughtful Integration of Large Language Models in Assigned Work Paper presented at 2024 ASEE-GSW, Canyon, Texas. 10.18260/1-2--45377

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