AI-Augmented Complexity Learning: Design, Automation, and Learning Impact for Conceptual Mastery in Derandomization Through Intelligent Tutoring and Real-Time Feedback

Authors

  • Mahadevaswamy Associate Professor, Dept. of ECE, Vidyavardhaka College of Engineering, Mysuru
  • Sreenath M. V. Associate Professor, Dept. of ISE, Nitte Meenakshi Institute of Technology, Bengaluru
  • Sathyanarayana N. Associate Professor, Dept. of ISE, Vemana Institute of Technology, Bengaluru
  • Nagaraj P. Y. Gokulam 3rd Stage, Mysuru, 4Kadaganchi, Kalaburagi
  • B. P. Pradeep Kumar Professor, Dept. of CSD, Atria Institute of Technology, Bengaluru

DOI:

https://doi.org/10.16920/jeet/2026/v39is2/26004

Abstract

This research examined the effectiveness of an AI-enhanced tutoring system on teaching stochastic and deterministic algorithms by using QuickSelect and the Median-of-Medians method as our primary samples. A total of 60 college students were randomly assigned to either a control group, which did their assignments on paper, and an experimental group, which had access to an interactive computer-based tutoring system that included stepwise assistance, categorization of errors, and immediate feedback. Both groups completed assignments to learn how to select good pivots, what constitutes the average and worst case time complexity, and how to select the best pivot in a deterministic algorithm. The results revealed that the AI group achieved greater improvements in their ability to complete the assignments than did the control group with less variance in performance and significantly greater ability to resolve conceptual errors than did the control group. Student participants in the study reported that their experience using the AI tools improved their understanding of how to choose good pivots, they were aided in developing recursive thinking processes, and the abstract nature of time complexity made it easier to grasp. The overall findings of the study suggest that AI-enabled tutoring provides a solid foundation for improving students' comprehension of stochastic QuickSelect and its deterministic no-throw method.

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Published

2026-02-18

How to Cite

Mahadevaswamy, M. V., S., N., S., P. Y., N., & Pradeep Kumar, B. P. (2026). AI-Augmented Complexity Learning: Design, Automation, and Learning Impact for Conceptual Mastery in Derandomization Through Intelligent Tutoring and Real-Time Feedback. Journal of Engineering Education Transformations, 39(Special Issue 2), 30–39. https://doi.org/10.16920/jeet/2026/v39is2/26004

Issue

Volume 39, Special Issue 2, January 2026

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

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