Enhancing Information Technology Governance in Universities: A Smart Chatbot System based on Information Technology Infrastructure Library
Received: 1 September 2024 | Revised: 23 September 2024 | Accepted: 27 September 2024 | Online: 2 December 2024
Corresponding author: Souad Ahriz
Abstract
The rapid evolution of information and communication technologies has created a pressing need for higher education institutions to modernize their Information Technology (IT) governance practices. This article proposes an innovative solution to enhance the governance and efficiency of IT services while optimizing and personalizing user experience. The proposed solution consists of a chatbot using Artificial Intelligence (AI) and Natural Language Processing (NLP) combined with the Information Technology Infrastructure Library (ITIL) standard to automate the management of IT services in the digital work environment (ENT). Intended for students, teachers, and administrators, this chatbot provides reactive support by responding to requests, reducing waiting times, and improving satisfaction. It also helps decrease operational costs and the workload of support teams by autonomously handling recurring requests. Beyond efficiency improvements, the chatbot contributes significantly to IT governance by providing structured service management, improving decision-making through real-time data, and supporting compliance with governance frameworks. An online survey conducted among 120 students revealed slow processing of requests and unavailability of services, justifying the need for this chatbot. The chatbot was designed with advanced NLP and Machine Learning (ML) technologies. Preliminary tests demonstrate the chatbot’s response reliability, with an accuracy rate of 96% and a response time decrease to an average of 4.17 seconds. The use of chatbots has considerable potential for universities to improve the efficiency of digital services offered to students.
Keywords:
artificial intelligence, machine learning, ITIL, chatbot, university, NLP, LLMDownloads
References
Y. K. Dwivedi et al., "Opinion Paper: ‘So what if ChatGPT wrote it?’ Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy," International Journal of Information Management, vol. 71, Aug. 2023, Art. no. 102642.
H. Jiang, Y. Cheng, J. Yang, and S. Gao, "AI-powered chatbot communication with customers: Dialogic interactions, satisfaction, engagement, and customer behavior," Computers in Human Behavior, vol. 134, Sep. 2022, Art. no. 107329.
J. Dahmen et al., "Artificial intelligence bot ChatGPT in medical research: the potential game changer as a double-edged sword," Knee Surgery, Sports Traumatology, Arthroscopy, vol. 31, no. 4, pp. 1187–1189, Apr. 2023.
S. Bayona, Y. Baca, and G. Vela, "IT service management using ITIL v3: A case study," in 12th Iberian Conference on Information Systems and Technologies, Lisbon, Portugal, Jun. 2017, pp. 1–6.
The Official Introduction to the ITIL Service Lifecycle. London, UK: The Stationery Office, 2007.
Nugroho, A. W. Rahardjo Emanuel, and A. J. Santoso, "Analysis of Information Technology Services Using ITIL v3 and ISO 21001:2018 in Academic Information Systems at Institut Seni Indonesia Yogyakarta," in 8th International Conference on Information Technology and Digital Applications, Yogyakarta, Indonesia, Nov. 2023, pp. 1–8.
M. B. Akbar and T. Sutabri, "Analysis of New Student Admission Service Management Using The ITIL Version 3 Framework in The Service Design Domain at Senior High School of 22 Palembang," Media Jurnal Informatika, vol. 15, no. 2, pp. 136–142, Dec. 2023.
S. Ahriz, N. Benmoussa, A. E. Yamami, K. Mansouri, and M. Qbadou, "A new approach for information technology service management in time of crisis using information technology infrastructure library," in 13th International Conference on Education and New Learning Technologies, Jul. 2021, pp. 10158–10167.
C. Zhang and Y. Chen, "A Review of Research Relevant to the Emerging Industry Trends: Industry 4.0, IoT, Blockchain, and Business Analytics," Journal of Industrial Integration and Management, vol. 5, no. 1, pp. 165–180, Mar. 2020.
F. Ouyang and P. Jiao, "Artificial intelligence in education: The three paradigms," Computers and Education: Artificial Intelligence, vol. 2, Jan. 2021, Art. no. 100020.
L. T. Liu, S. Wang, T. Britton, and R. Abebe, "Reimagining the machine learning life cycle to improve educational outcomes of students," Proceedings of the National Academy of Sciences, vol. 120, no. 9, Feb. 2023, Art. no. e2204781120.
S. G. Essa, T. Celik, and N. E. Human-Hendricks, "Personalized Adaptive Learning Technologies Based on Machine Learning Techniques to Identify Learning Styles: A Systematic Literature Review," IEEE Access, vol. 11, pp. 48392–48409, Jan. 2023.
B. Alshawi, "Utilizing GANs for Credit Card Fraud Detection: A Comparison of Supervised Learning Algorithms," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12264–12270, Dec. 2023.
A. Smith-Renner, R. Rua, and M. Colony, "Towards an Explainable Threat Detection Tool," in IUI Workshops, Los Angeles, CA, USA, Mar. 2019, pp. 1–6.
S. Ge, G. Ma, S. Xie, and P. S. Yu, "Securing Behavior-based Opinion Spam Detection." arXiv, Nov. 08, 2018.
V. Couteaux, O. Nempont, G. Pizaine, and I. Bloch, "Towards Interpretability of Segmentation Networks by Analyzing DeepDreams," in Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support, K. Suzuki, M. Reyes, T. Syeda-Mahmood, E. Konukoglu, B. Glocker, R. Wiest, Y. Gur, H. Greenspan, and A. Madabhushi, Eds. New York, NY, USA: Springer, 2019, pp. 56–63.
U. Jan, S. Ji, and C. Kim, "What (de) motivates customers to use AI-powered conversational agents for shopping? The extended behavioral reasoning perspective," Journal of Retailing and Consumer Services, vol. 75, Nov. 2023, Art. no. 103440.
M. M. Mariani, I. Machado, V. Magrelli, and Y. K. Dwivedi, "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, vol. 122, Apr. 2023, Art. no. 102623.
Z. Yin, K. Chang, and R. Zhang, "DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks," in 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, Aug. 2017, pp. 2131–2139.
A. Raza, M. Latif, M. U. Farooq, M. A. Baig, M. A. Akhtar, and Waseemullah, "Enabling Context-based AI in Chatbots for conveying Personalized Interdisciplinary Knowledge to Users," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12231–12236, Dec. 2023.
A. Kumar, R. Sharma, and P. Bedi, "Towards Optimal NLP Solutions: Analyzing GPT and LLaMA-2 Models Across Model Scale, Dataset Size, and Task Diversity," Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14219–14224, Jun. 2024.
A. Anand, "ITIL 4 Explained - IT Service Management Practices," ITSM.tools, Jan. 01, 2024.
A. Darvishi, H. Khosravi, S. Sadiq, D. Gasevic, and G. Siemens, "Impact of AI assistance on student agency," Computers & Education, vol. 210, Mar. 2024, Art. no. 104967.
Downloads
How to Cite
License
Copyright (c) 2024 Souad Ahriz, Hiba Gharbaoui, Nezha Benmoussa, Abdelilah Chahid, Khalifa Mansouri
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
