The AI Companion in Education: Analyzing the Pedagogical Potential of ChatGPT in Computer Science and Engineering
Zhangying He, Thomas Nguyen, Tahereh Miari, Mehrdad Aliasgari, Setareh, Rafatirad, Hossein Sayadi

TL;DR
This paper systematically evaluates ChatGPT's pedagogical potential in Computer Science and Engineering education, analyzing its strengths and limitations through diverse practice problems and a comprehensive reliability framework.
Contribution
It introduces a novel five-factor reliability analysis framework and provides insights into effective use cases and challenges of ChatGPT in CSE education.
Findings
ChatGPT performs well on conceptual knowledge questions.
Personalized and project-based tasks reveal more limitations.
Correlation analysis identifies key factors affecting response quality.
Abstract
Artificial Intelligence (AI), with ChatGPT as a prominent example, has recently taken center stage in various domains including higher education, particularly in Computer Science and Engineering (CSE). The AI revolution brings both convenience and controversy, offering substantial benefits while lacking formal guidance on their application. The primary objective of this work is to comprehensively analyze the pedagogical potential of ChatGPT in CSE education, understanding its strengths and limitations from the perspectives of educators and learners. We employ a systematic approach, creating a diverse range of educational practice problems within CSE field, focusing on various subjects such as data science, programming, AI, machine learning, networks, and more. According to our examinations, certain question types, like conceptual knowledge queries, typically do not pose significant…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education · Online Learning and Analytics
