USE OF ARTIFICIAL INTELLIGENCE SUPPORTED PROMPT TOOLS IN OFFICE APPLICATIONS

Yıl 2026, Cilt: 26 Sayı: 2 , 303 - 312 , 01.04.2026

Cemal Çelik

https://doi.org/10.21121/eab.20260210

https://izlik.org/JA86GR96TX

Öz

Artificial Intelligence (AI)-enabled prompting technologies have begun to produce more functional solutions for improving personal productivity and creativity by providing office software users with simpler information search and prompting commands. Prompt technologies that facilitate ways of accessing data and information sources from different systems aim to strengthen the effects of individual contributions to the improvement of business processes by contributing to the creation of more creative documents, presentations, e-mails and spreadsheets. Copilot is an AI-enabled prompting technology developed for this purpose. It is an assistant that enables users to reveal their more productive and creative sides with the features of gathering and transferring information and documents created in Microsoft 365 and other office applications. In this article, the basic components and functionality of the AI-supported Copilot prompt technology are introduced, and its individual and corporate effectiveness regarding the company processes is tried to be explained with an application. In the application process, it is aimed to support the decision processes of a national company operating in the e-commerce market on pet foods by processing the data of a national company operating in the e-commerce market with Copilot AI assistant integrated into Excel application.

Anahtar Kelimeler

Prompt , Prompt Engineer , Data Analysisi , Prompt Tools

Etik Beyan

In this study, no application was made at the ethics committee level.

Kaynakça

• Agrawal, A., Gans, J. S. & Goldfarb, A. (2019). Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction. Journal of Economic Perspectives, 33(2), 31-50. https://doi.org/10.1257/jep.33.2.31
• Ahuja, M. (2023). 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 Knowledge Management, 71, 1-63. https://doi.org/10.1016/j.ijinfomgt.2023.102642
• Berente, N., Gu, B., Recker, J. & Santhanam, R. (2021). Managing Artifıcial Intelligence. MIS Quarterly, 45(3), 1433-1448. https://doi.org/10.25300/MISQ/2021/16274
• Bird, C., Ford, D. & Zimmermann, T. (2022). Taking Flight with Copilot. Communications of the ACM, 66(6), 56-62. https://doi.org/10.1145/3589996
• Cusumano, M. A. (2023). Generative AI as a New Innovation Platform. Communications of the ACM , 66(10), 18-21. https://doi.org/10.1145/3615859
• Çapar, M. C. & Ceylan, M. (2022). Durum Çalışması ve Olgubilim Desenlerinin Karşılaştırılması. Anadolu Üniversitesi SBD, 22(2), 295-313. https://doi.org/10.18037/ausbd.1227359
• Darvishi, A.,Khosravi, H., Sadiq, S., Gasevic, D. & Siemens, G. (2024). Impact of AI assistance on student agency. Computers & Education, 210, 1-18. https://doi.org/10.1016/j.compedu.2023.104967
• Devlin, J., Lee, Chang, M., Lee, Kenton. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Paper presented at the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.(pp.4171-4186). https://aclanthology.org/N19-1423/
• Fiannaca, A. J., Kulkarni, C. & Carrie J Cai, M. T. (2023). Programming without a Programming Language: Challenges and Opportunities for Designing Developer Tools for Prompt Programming. Paper presented at the CHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. (pp.1-7) https://doi.org/10.1145/3544549.3585737
• France, S. L. (2024). Navigating software development in the ChatGPT and GitHub Copilot era. Business Horizons, 67(5), 1-13. https://doi.org/10.1016/j.bushor.2024.05.009
• Giray, L. (2023). Prompt Engineering with ChatGPT: A Guide for Academic Writers. Biomedical Engineering Society, 51, 2629–2633. https://doi.org/10.1007/s10439-023-03272-4
• Goloujeh, A. M., Sullivan, A. & Magerko, B. (2024). Is It AI or Is It Me? Understanding Users Prompt Journey with Text-to-Image Generative AI Tools, Paper presented at the CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems.(pp.1-13) https://dl.acm.org/doi/proceedings/10.1145/3613904
• Goktas, P. & Grzybowski, A. (2024). Assessing the Impact of ChatGPT in Dermatology: A Comprehensive Rapid Review. Journal of Clinical Medicine,13(19), 5909. https://doi.org/10.3390/jcm13195909
• Goktas, P. & Grzybowski, A. (2025). Shaping the Future of Healthcare: Ethical Clinical Challenges and Pathways to Trustworthy AI. Journal of Clinical Medicine, 14(5), 1605. https://doi.org/10.3390/jcm14051605
• Goktas, P., Kucukkaya, A. & Karacay, P. (2023). Utilizing GPT 4.0 with prompt learning in nursing education: A case study approach based on Benner's theory. Teaching and Learning in Nursing, 19(2), 358-368. https://doi.org/10.1016/j.teln.2023.12.014
• Goktas, P., Kucukkaya, A. & Karacay, P. (2023). Leveraging the efficiency and transparency of artificial intelligence-driven visual Chatbot through smart prompt learning concept. Skin Research and Technology, 1-2. https://doi.org/10.1111/srt.13417
• Gu, X., Yoo, X. M. & Lee, S.-W. (2021). Response Generation with Context-Aware Prompt Learning. arXiv, 1-10. https://doi.org/10.48550/arXiv.2111.02643
• Gupta, R., Nair, K., Mishra, M., Ibrahim, B. & Bharwaj, S. (2024). Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda. International Journal of Information Management Data Insights, 4(1), 100232. https://doi.org/10.1016/j.jjimei.2024.100232
• Haghighat, P., Nguyen, T. & Valizadeh, M. (2023). Effects of an intelligent virtual assistant on office task performance and workload in a noisy environment. Applied Ergonomics, 109, 1-10. https://doi.org/10.1016/j.apergo.2023.103969
• Jeff, C. & Stephen, J. (2024). The rise of AI copilots: How LLMs turn data into actions, advance the business intelligence industry and make data accessible company-wide. Applied Marketing Analytics, 9(3), 207-214. https://doi.org/10.69554/SJEI6374
• Kar, A. K., Varsha, P. S. & Rajan, S. (2023). Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature. Global Journal of Flexible Systems Management, 24(5), 659-689. https://doi.org/10.1007/s40171-023-00356-x
• Knoth, N., Tolzin, A., Janson, A. & Leimeister, J. M. (2024). AI literacy and its implications for prompt engineering strategies, Computers and Education Artificial Intelligence, 6, 1-14. https://doi.org/10.1016/j.caeai.2024.100225
• Kyto, M. (2024). Copilot for Microsoft 365: A Comprehensive End-user Training Plan for Organizations. (Yüksek Lisans Tezi), Haaga-Helia University of Applied Sciences
• Li, Y., Sha, L., Yan, L. & Lin, J. (2023). Can large language models write reflectively. Computers and Education: Artificial Intelligence, 4, 1-11. https://doi.org/10.1016/j.caeai.2023.100140
• Lim, W. M., Gunasekara, A. & Pallant, J. L. (2023). Generative AI and the future of education: Ragnarok or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 1-13. https://doi.org/10.1016/j.ijme.2023.100790
• Liu, P., Yuan, W., Fu, J. & Jiang, Z. (2021). Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Computing Surveys, 55(9), 1-35, https://doi.org/10.1145/3560815
• Luitse, D. & Denkena, W. (2021). The great Transformer: Examining the role of large language models in the political economy of AI. Big Data & Society, 1-14. https://doi.org/10.1177/20539517211047734
• Mahdi, M. & Yekta, J. (2024). The general intelligence of GPT–4, its knowledge diffusive and societal influences, and its governance. Meta-Radiology, 2(2), 1-17. https://doi.org/10.1016/j.metrad.2024.100078 Microsoft. (2024). Copilot Studio/ Fundamentals. https://learn.microsoft.com/tr-tr/microsoft-copilot-studio/fundamentals-what-is-copilot-studio
• Moorhouse, B. L. & Kohnke, L. (2024). The effects of generative AI on initial language teacher education: The perceptions of teacher educators. System, 122, 1-10. https://doi.org/10.1016/j.system.2024.103290
• Morgan, D. L. (2023). Exploring the Use of Artificial Intelligence for Qualitative Data Analysis: The Case of ChatGPT. International Journal of Qualitative Methods, 22 , 1-10. https://doi.org/10.1177/16094069231211248
• Nah, F. H., Zheng, R., Cai, J., Siau, K. & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal Of Information Technology Case And Application Research, 25(3), 277-297. https://doi.org/10.1080/15228053.2023.2233814
• Nazari, M. & Saadi, G. (2023). Developing efective prompts to improve communication with ChatGPT: a formula for higher education stakeholders. Discover Education, 3(45),1-17. https://doi.org/10.1007/s44217-024-00122-w
• Peres, R., Schreier, M., Schweidel, D. & Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(1), 269-275. https://doi.org/10.1016/j.ijresmar.2023.03.001
• Shin, T., Razeghi, Y., Wallace, E. & Singh, S. (2020). Autoprompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. Paper presented at the Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, (pp.4222–4235). https://doi.org/10.48550/arXiv.2010.15980
• Tepe, M. & Emekli, E. (2024). Decoding medical jargon: The use of AI language models (ChatGPT-4, BARD, microsoft copilot) in radiology reports. Patient Education and Counseling, 126,1-5. https://doi.org/10.1016/j.pec.2024.108307
• White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Henry, G., S.,S., Jesse. & Schmidt, D. C. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. Paper Presented at the PLoP '23: Proceedings of the 30th Conference on Pattern Languages of Programs.(pp. 1-31.https://dl.acm.org/doi/10.5555/3721041.3721046
• Yoşumaz, İ. (2024). Prompt Engineering Awareness: A Study on Google Trends Data. International Journal Of Social And Economic Studies,5(2), 248-268. https://doi.org/10.62001/gsijses.1532474
• Zamfirescu-Pereira, J., Wong, R. Y., Hartmann, B. & Yang, Q. (2023). Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts, Paper Presented at the Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems.(pp. 1-21). https://doi.org/10.1145/3544548.3581388
• Zhang, B., & Dafoe, A. (2019). Artificial Intelligence: American Attitudes and Trends. Oxford: Oxford Press. http://dx.doi.org/10.2139/ssrn.3312874