ISEDJ

Abstract: As data mining and predictive modeling skills become increasingly crucial, educators face challenges in selecting teaching tools that effectively balance technical depth, ease of use, and real-world applicability. This paper examines the use of augmented Generative Artificial Intelligence (GenAI) data mining tools, such as SAP Analytics Cloud, and compares their educational impact to traditional programming tools such as R. Through surveys, performance analysis over two terms, and instructor reflections, we explored how these tools affect learning outcomes and teaching practices differently in an undergraduate business school data mining course. We present a brief overview of GenAI and traditional programming tools, considering their respective strengths and limitations in educational contexts. We particularly examine how each type of tool influences the learning process, technical skill development, and students' ability to apply data mining concepts. This study offers insights into the effectiveness of augmented GenAI and traditional teaching tools and presents pedagogical implications for educators seeking to optimize data analytical skills education in business schools. Unlike prior studies that examine programming tools or investigate GenAI in educational contexts separately, our work presents a direct contrast between the two approaches, allowing for a systematic comparison of their impacts on teaching and learning.

Download this article: ISEDJ - V24 N6 Page 7.pdf

Recommended Citation: Zhou, W., Ozturk, P., Hartzel, K.S., Salazar-Betancourth, C., (2026). Comparing Augmented Generative AI Platforms and Programming-Based Tools for Teaching Data Analytics to Undergraduate Business Students. Information Systems Education Journal 24(6) pp 7-20. https://doi.org/10.62273/LAZQ5486