№1, 2024


Mohammad Ali AL Qudah, Leyla Muradkhanli

This study explores the use of artificial intelligence (AI) in e-government applications, focusing on the various phases of e-government expansion and advancement. The frameworks include providing information, enabling interaction, and facilitating transactions. The main source of improvement is the integration of AI into government services, enabling computer systems to learn, reason, and make human-like decisions. The use of generator AI is expected to result in more intelligent, precise, and efficient approaches, but it is essential for organizations to formulate plans that align with advancements and consequences of intelligent technology. The goal is to achieve development goals that enable the government to adopt smart generators in its applications (pp.10-17).

Keywords: E-government, Artificial Intelligence, Generative AI, Decision-making
  • Alqudah, M. A. (2021a). Artificial Intelligence in managing the electronic customer relationship and enhancing the level of satisfaction with electronic services.
  • Alqudah, M. A. (2021b). Investment Artificial Intelligence in decision-making processes in the Jordanian Ministry of Interior. International Journal of Innovations in Engineering Research and Technology, 8(10), 40–53.
  • Alqudah, M. A. (2021c). Towards the governance of government data using artificial intelligence.
  • Alqudah, M. A. & Muradkhanli, L. (2021a). Artificial Intelligence in E-Government; Ethical Challenges and Governance in Jordan. Electronic Research Journal of Social Sciences and Humanities, 3, 65–74.
  • Alqudah, M. A. & Muradkhanli, L. (2021b). E-government in Jordan and studying the extent of the e-government development index according to the United Nations report. International Journal of Multidisciplinary: Applied Business and Education Research, 2(4), 365–375.
  • Alqudah, M. A. & Muradkhanli, L. (2021c). Electronic management and its role in developing the performance of e-government in Jordan. Electronic Research Journal of Engineering, Computer and Applied Sciences, 3, 65–82.
  • Alqudah, M. A., Muradkhanli, L., & Al-Awasa, M. (2021). Artificial Intelligence applications that support: business organizations and EGovernment in administrative decision. International Journal on Economics, Finance and Sustainable Development, 3(3), 57–72.
  • Alqudah, M. A., Muradkhanli, L., Muradkhanli, Z., & Salameh, A. A. (2023). Using Artificial Intelligence applications for E-Government services as iris recognition. 17th IEEE International Conference on Application of Information and Communication Technologies (AICT), Baku, Azerbaijan (pp. 1–7).
  • Alryalat, M. A., Rana, N. P., & Dwivedi, Y. K. (2020). Citizen's adoption of an E-Government system: Validating the Extended Theory of Reasoned Action (TRA). In I. Management Association (Eds), Open Government: Concepts, Methodologies, Tools, and Applications (pp. 651-674). IGI Global.
  • Grover, A., Dhar, M., & Ermon, S. (2018). Flow-GAN: Combining maximum likelihood and adversarial learning in generative models. Proceedings of the AAAI conference on artificial intelligence, 32(1), 3069-3076.
  • Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review, 53(8), 5455–5516.
  • Limna, P., Jakwatanatham, S., Siripipattanakul, S., Kaewpuang, P., & Sriboonruang, P. (2022). A review of artificial intelligence (AI) in education during the digital era, Advance Knowledge for Executives, 1(1), 1–9.
  • Mohan, N., Prasad, K. D. V., Soujanya, K., Dobhal, D. C., Ali, M., & Tripathi, M. A. (2023). An adaptive service-oriented business management pattern based on machine learning rule ML. 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India (pp. 1672–1676).
  • Repecka, D., Jauniskis, V., Karpus, L., Rembeza, E., Rokaitis, I., Zrimec, J., Poviloniene, S., Laurynenas, A., Viknander, S., Abuajwa, W., Savolainen, O., Meskys, R., Engqvist, M. K., & Zelezniak, A. (2021). Expanding functional protein sequence spaces using generative adversarial networks. Nature Machine Intelligence, 3(4), 324-333.
  • Ryman-Tubb, N. F., Krause, P., & Garn, W. (2018). How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark. Engineering Applications of Artificial Intelligence, 76, 130-157.
  • Sezgin, E., Sirrianni, J., & Linwood, S. L. (2022). Operationalizing and implementing pretrained, Large Artificial Intelligence Linguistic Models in the US Health Care System: Outlook of Generative Pretrained Transformer 3 (GPT-3) as a Service Model. JMIR Medical Informatics, 10(2), e32875.
  • Wang, K., Gou, C., Duan, Y., Lin, Y., Zheng, X., & Wang, F.-Y. (2017). Generative adversarial networks: introduction and outlook. IEEE/CAA Journal of Automatica Sinica, 4(4), 588–598.
  • Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial Intelligence and the public sector — applications and challenges. International Journal of Public Administration, 42(7), 596-615.