Research Article

Rule-Based Chatbot System Design for Decision-Making Processes: A Structured Approach to Statistical Analysis Selection

Volume: 9 Number: 1 March 27, 2026

Rule-Based Chatbot System Design for Decision-Making Processes: A Structured Approach to Statistical Analysis Selection

Abstract

In this study, a rule-based chatbot system based on a Finite State Machine (FSM) was designed, devel-oped, and piloted to support the selection process for multivariate statistical analysis. Uncertainties and methodological errors encountered in statistical analysis selection make the decision-making process difficult for researchers and necessitate systematic support for this process. The system developed in this context follows a deterministic decision flow based on structured user inputs. It provides guidance on appropriate statistical analysis methods, taking into account factors such as the dependent variable type, data structure, and research objective. The system architecture consists of a user interface, dialogue management, an FSM-based decision engine, a statistical knowledge base, and output generation components. The system was developed following a waterfall model, and the analysis selection process was structured using a decision tree with finite states and transition logic. Within the scope of the pilot implementation, assumption checks for linear regression analysis were modeled using rule-based methods, and a web-based interactive system prototype was developed. The developed system was evaluated by five experts in statistics and data analytics on usability, clarity of decision logic, and perceived benefit. Expert evaluations show that the system has a practical, structurally consistent, and scalable architecture. These findings indicate that rule-based chatbot approaches are a suitable solution for decision-support scenarios where explainability and consistency are paramount.

Keywords

Ethical Statement

Ethics committee approval was not required for this study, as it does not involve human participants, personal data, or experimental interventions.

References

  1. W. Han, and H. J. Schulz, ”Providing visual analytics guidance through decision support,” Information Visualization, vol. 22, no. 2, pp. 140–165, 2023.
  2. J. Mbotwa, I. Singini, and M. Mukaka, ”Discrepancy between statistical analysis method and study design in medical research: Examples, implications, and potential solutions,” Malawi Medical Journal, vol. 29, no. 1, pp. 63–65, 2017.
  3. N. A. M. N. Azmay, R. Rosli, S. Maat, and M. Mahmud, ”Educational research trends on statistical reasoning and statistical thinking: A systematic literature review,” International Journal of Academic Research in Progressive Education and Development, vol. 12, no. 2, pp. 586–600, 2023.
  4. P. Mishra, C. M. Pandey, U. Singh, A. Keshri, and M. Sabaretnam, ”Selection of appropriate statistical methods for data analysis,” Annals of Cardiac Anaesthesia, vol. 22, no. 3, pp. 297–301, 2019.
  5. J. Zeiser, ”Owning decisions: AI decision-support and the attributability-gap,” Science and Engineering Ethics, vol. 30, no. 4, Art. No. 27, 2024.
  6. R. Alaaeldin, E. Asfoura, G. Kassem, and M. S. Abdel-Haq, ”Developing a chatbot system to support decision making based on big data analytics,” Journal of Management Information and Decision Sciences, vol. 24, no. 2, pp. 1–15, 2021.
  7. K. K. Nirala, N. K. Singh, and V. S. Purani, ”A survey on providing customer and public administration based services using AI: Chatbot,” Multimedia Tools and Applications, vol. 81, no. 16, pp. 22215–22246, 2022.
  8. D. Prayogo, W. Wilonotomo, and O. P. Martadireja, ”Systematic literature review chatbot: Suatu perbandingan pendekatan,” JIIP-Jurnal Ilmiah Ilmu Pendidikan, vol. 8, no. 4, pp. 4172–4178, 2025.

Details

Primary Language

English

Subjects

Artificial Intelligence (Other)

Journal Section

Research Article

Early Pub Date

March 27, 2026

Publication Date

March 27, 2026

Submission Date

January 22, 2026

Acceptance Date

February 12, 2026

Published in Issue

Year 2026 Volume: 9 Number: 1

APA
Demircioğlu Diren, D. (2026). Rule-Based Chatbot System Design for Decision-Making Processes: A Structured Approach to Statistical Analysis Selection. Sakarya University Journal of Computer and Information Sciences, 9(1), 216-225. https://doi.org/10.35377/saucis...1869872
AMA
1.Demircioğlu Diren D. Rule-Based Chatbot System Design for Decision-Making Processes: A Structured Approach to Statistical Analysis Selection. SAUCIS. 2026;9(1):216-225. doi:10.35377/saucis.1869872
Chicago
Demircioğlu Diren, Deniz. 2026. “Rule-Based Chatbot System Design for Decision-Making Processes: A Structured Approach to Statistical Analysis Selection”. Sakarya University Journal of Computer and Information Sciences 9 (1): 216-25. https://doi.org/10.35377/saucis. 1869872.
EndNote
Demircioğlu Diren D (March 1, 2026) Rule-Based Chatbot System Design for Decision-Making Processes: A Structured Approach to Statistical Analysis Selection. Sakarya University Journal of Computer and Information Sciences 9 1 216–225.
IEEE
[1]D. Demircioğlu Diren, “Rule-Based Chatbot System Design for Decision-Making Processes: A Structured Approach to Statistical Analysis Selection”, SAUCIS, vol. 9, no. 1, pp. 216–225, Mar. 2026, doi: 10.35377/saucis...1869872.
ISNAD
Demircioğlu Diren, Deniz. “Rule-Based Chatbot System Design for Decision-Making Processes: A Structured Approach to Statistical Analysis Selection”. Sakarya University Journal of Computer and Information Sciences 9/1 (March 1, 2026): 216-225. https://doi.org/10.35377/saucis. 1869872.
JAMA
1.Demircioğlu Diren D. Rule-Based Chatbot System Design for Decision-Making Processes: A Structured Approach to Statistical Analysis Selection. SAUCIS. 2026;9:216–225.
MLA
Demircioğlu Diren, Deniz. “Rule-Based Chatbot System Design for Decision-Making Processes: A Structured Approach to Statistical Analysis Selection”. Sakarya University Journal of Computer and Information Sciences, vol. 9, no. 1, Mar. 2026, pp. 216-25, doi:10.35377/saucis. 1869872.
Vancouver
1.Deniz Demircioğlu Diren. Rule-Based Chatbot System Design for Decision-Making Processes: A Structured Approach to Statistical Analysis Selection. SAUCIS. 2026 Mar. 1;9(1):216-25. doi:10.35377/saucis. 1869872

 

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