SavedAdd your journal

Journals / Bitlis Eren Üniversitesi Fen Bilimleri Dergisi / 2025 / Volume: 14 Issue: 4

Design and Implementation of an AI-Based Decision Support System for IT Support Units

Şahin Zambak(GAZİ ÜNİVERSİTESİ, BİLİŞİM ENSTİTÜSÜ)0009-0004-2942-2845
Tahsin Çetinyokuş(GAZİ ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ENDÜSTRİ MÜHENDİSLİĞİ BÖLÜMÜ, ENDÜSTRİ MÜHENDİSLİĞİ PR.)0000-0002-9963-5174
Pages
—
Publication Date
2025-12-31
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results showed that 88% of responses were considered helpful, while the average time-to-first-suggestion was 1.68 ± 0.21 seconds (client side).

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Artificial Intelligence (AI) is a key driver of digital transformation in organizational processes.
📊
Key Findings & Data:When integrated into Decision Support Systems (DSS), it enables faster, data-driven, and more consistent decision-making, especially in operational settings such as IT support units.
💡
Conclusion & Impact:Results showed that 88% of responses were considered helpful, while the average time-to-first-suggestion was 1.68 ± 0.21 seconds (client side).
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Artificial Intelligence · Decision Support Systems · Natural Language Processing

Abstract

Artificial Intelligence (AI) is a key driver of digital transformation in organizational processes. When integrated into Decision Support Systems (DSS), it enables faster, data-driven, and more consistent decision-making, especially in operational settings such as IT support units. This study presents the design and implementation of an AI-powered DSS that combines a Natural Language Processing (NLP)–based chatbot with structured data analytics to improve problem resolution, reduce human workload, and generate managerial insights. The system was implemented as a modular web application using the ASP.NET Core MVC framework. A chatbot interface interacts with users in natural language, processes queries through OpenAI’s language-model API, and records sessions in a Microsoft SQL Server database. At the end of each support session, users provide feedback, and the system can generate an automatic summary of the solution process. Administrators access these records through a dashboard that presents analytical visualizations, frequently encountered problems, and performance indicators for chatbot-assisted resolutions. A scenario-based simulation with 50 typical IT support cases was conducted to evaluate the system. Results showed that 88% of responses were considered helpful, while the average time-to-first-suggestion was 1.68 ± 0.21 seconds (client side). AI-generated summaries were also evaluated by human reviewers (Cohen’s κ = 0.87) and found to be coherent and contextually accurate. These findings indicate that AI-integrated DSS architectures can enhance user satisfaction, reduce response time, and support organizational learning by transforming tacit problem-solving interactions into analyzable knowledge assets.

🔖

Cite This Article

Zambak, Ş., Çetinyokuş, T. (2025). Design and Implementation of an AI-Based Decision Support System for IT Support Units. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Volume: 14 Issue: 4. https://doi.org/10.17798/bitlisfen.1759111

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (17)

  1. S. Chowdhury, P. Budhwar, P. K. Dey, S. Joel-Edgar, and A. Abadie, “AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems and organisational socialisation framework,” Journal of Business Research, vol. 144, pp. 31–49, 2022.
  2. G. Madhumita, P. Dolly Diana, N. P. C., P. B. N. Kiran, S. Aggarwal, and A. S. Nargunde, “AI-powered performance management: Driving employee success and organizational growth,” in Proc. 5th Int. Conf. Recent Trends in Computer Science and Technology (ICRTCST), 2024.
  3. M. Casillo, F. Colace, L. Fabbri, M. Lombardi, A. Romano, and D. Santaniello, “Chatbot in Industry 4.0: An approach for training new employees,” in Proc. IEEE Int. Conf. Teaching, Assessment, and Learning for Engineering (TALE), 2020.
  4. D. Calvaresi, S. Eggenschwiler, Y. Mualla, M. Schumacher, and J.-P. Calbimonte, “Exploring agent-based chatbots: A systematic literature review,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 8, pp. 11207–11226, 2023.
  5. A. Zirar, S. I. Ali, and N. Islam, “Worker and workplace artificial intelligence (AI) coexistence: Emerging themes and research agenda,” Technovation, vol. 124, Art. no. 102747, 2023.
  6. M. C. M. Lee, H. Scheepers, A. K. H. Lui, and E. W. T. Ngai, “The implementation of artificial intelligence in organizations: A systematic literature review,” Information & Management, vol. 60, no. 5, Art. no. 103816, 2023.
  7. E. Ahumada-Tello, R. D. Evans, D. Romero-Gómez, J. López-García, and M. Castañón-Puga, “Impact of AI on employee well-being and decision-making: Insights from OECD member countries,” in Proc. IEEE Global Conf. Artificial Intelligence and Internet of Things (GCAIoT), 2023, pp. 121–126.
  8. H. İnce, S. E. İmamoğlu, and S. Z. İmamoğlu, “Yapay zeka uygulamalarının karar verme üzerine etkileri: Kavramsal bir çalışma,” International Review of Economics and Management, 2021.
  9. R. Sharda, D. Delen, E. Turban, and P. L. Ting, Business Intelligence and Analytics: Systems for Decision Support. Upper Saddle River, NJ, USA: Prentice Hall, 2014.
  10. E. Turban, J. E. Aronson, and T.-P. Liang, Decision Support Systems and Intelligent Systems. Upper Saddle River, NJ, USA: Prentice Hall, 2005.
  11. I. Nonaka and H. Takeuchi, “The knowledge-creating company: How Japanese companies create the dynamics of innovation,” Long Range Planning, vol. 29, no. 4, pp. 592–, 1996.
  12. I. Nonaka, “A dynamic theory of organizational knowledge creation,” Organization Science, vol. 5, no. 1, pp. 14–37, 1994.
  13. S. U. Singh and A. S. Namin, “A survey on chatbots and large language models: Testing and evaluation techniques,” Natural Language Processing Journal, vol. 10, Art. no. 100128, 2025.
  14. U. Barreto and Y. Abarca, “Integration of the SECI model and ChatGPT in higher education,” Heliyon, vol. 11, no. 4, Art. no. e42814, 2025.
  15. H. H. Nap et al., “The evaluation of a decision support system integrating assistive technology for people with dementia at home,” Frontiers in Dementia, vol. 3, Art. no. 1400624, 2024.
  16. Z. Jan et al., “Artificial intelligence for Industry 4.0: Systematic review of applications, challenges, and opportunities,” Expert Systems with Applications, vol. 216, Art. no. 119456, 2023.
  17. S. K. Gudipati, “Chatbot evaluation frameworks: From BLEU and F1 to multi-dimensional real-world benchmarks,” in Proc. Int. Conf. Management Science and Computer Engineering, 2025, pp. 228–235.