Impact of ChatGPT on Educational Strategies for Future-Proof Business Data Analyst: Machine Learning Code Generation in Teaching and Learning

Colin Fu and Joseph Damonte ()
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Colin Fu: University of Surrey
Joseph Damonte: University of Surrey

Chapter Chapter 3 in Business Analytics and Decision Making in Practice, 2024, pp 25-40 from Springer

Abstract: Abstract This paper examines the role of ChatGPT in educational pedagogy, particularly its use in creating machine learning code for business applications. We assess ChatGPT Plus (version 4) against Google Bard’s July-Aug version, focusing on its ability to develop code solutions for various business problems, i.e., Customer Churn Prediction for a Telecom Company; Purchase Prediction Based on Shopper Interactions; Yield Prediction of Blueberries and Sentiment Analysis of App Reviews. The study primarily explores ChatGPT’s effectiveness in enhancing the teaching and understanding of complex machine learning concepts in higher education. The research evaluates ChatGPT’s ability to create accurate and relevant code for four different business scenarios, highlighting its potential to transform educational methods and learning experiences. ChatGPT effectively generated functional code in three scenarios, showcasing its value in practical, business-focused education. However, its limitations in sentiment analysis tasks emphasize the ongoing need for human expertise in areas where AI is less effective. The study shows that ChatGPT Plus (version 4) provides more detailed feedback and practical code improvement suggestions than Google Bard. However, the need for educators to refine AI-generated outputs, especially due to outdated references and suboptimal code structures, is a key aspect to consider. ChatGPT can be concluded to show great promise in improving machine learning education, but it still requires human expertise to ensure content quality and relevance. Additionally, our findings indicate that ChatGPT could play a crucial role in developing advanced analytical solutions for complex business problems, highlighting its potential impact on AI-driven business decision-making.

Keywords: Generative AI; Chat GPT; Machine learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-61589-4_3

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