Unraveling Indonesian Public R&D Institutions’ Perspectives on Chatgpt: An Analytical Approach To Decoding Open-Ended Surveys Through Topic Modeling

Andi Budiansyah; Purnama Alamsyah; Yovita Isnasari; Indri Juwita Asmara; Sigit Setiawan; Mia Rahma Romadona

doi:10.14203/STIPM.2024.386

Unraveling Indonesian Public R&D Institutions’ Perspectives on Chatgpt: An Analytical Approach To Decoding Open-Ended Surveys Through Topic Modeling

Andi Budiansyah, Purnama Alamsyah, Yovita Isnasari, Indri Juwita Asmara, Sigit Setiawan, Mia Rahma Romadona

Abstract

The rapid development of artificial intelligence (AI) technologies like ChatGPT has made global management easier. Since 2020, AI has been widely utilized for all purposes, notably due to its ease in access for scientific production. This study aims to analyze open-ended questions and responses from web-based survey in Indonesian public research and development (R&D) institutions using topic modeling. A total of 205 data points were obtained through web-based surveys conducted among researchers in Indonesian public R&D institutions. To learn their thoughts on the use of ChatGPT (Chat Generative Pre-Trained Transformer), two AI language topic modeling, Latent Dirichlet Allocation (LDA) and Principal Component Analysis (PCA), were employed to detect survey topic structures and show the results. Three theme groups represent the institution's research cohort. Based on generational disparities in birth year and functional position level, this study selected the seven most popular topics from three themes of researchers' opinions on ChatGPT. Researchers of certain age generations and functional position levels focused on new AI technologies, efficiency, and production gains, while others valued methodological innovation, ethics, and scientific integrity. When formulating a strategy for incorporating AI into the public R&D institution's future research agendas, it is imperative to include diverse perspectives.

Full Text:

PDF

References

Jobin, A., Lenca, M., & Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. Nature Machine Intelligence, 1, 389–399. doi:https://doi.org/10.1038/s42256-019-0088-2

Makridakis, S. (2017). The Forthcoming Artificial Intelligence (AI) Revolution: Its impact on society and firms. Futures, 90, 46-60. doi:10.1016/j.futures.2017.03.006

Grimaldi, G., & Ehrler, B. (2023). AI et al.: Machines Are About to Change Scientific Publishing Forever. ACS Energy Letter, 8(1), 878-880. doi:10.1021/acsenergylett.2c02828

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What If The Devil is My Guardian Angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10. doi:10.1186/s40561-023-00237-x

Maedche, A., Legner, C., Benlian, A., Berger, B., Gimpel, H., Hess, T., . . . Söllner, M. (2019). AI-based Digital Assistants: Opportunities, Threats, and Research Perspectives. Business and Information Systems Engineering, 61(4), 535-544. doi:10.1007/s12599-019-00600-8

Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., S. Q., . . . Zhang, J. (2021). Artificial Intelligence: A powerful paradigm for scientific research. The Innovation, 2(4). doi:10.1016/j.xinn.2021.100179

Borger, J. G., Ng, A. P., Anderton, H., Ashdown, G. W., Auld, M., Blewitt, M. E., . . . Naik, S. H. (2023). Artificial intelligence takes center stage: exploring the capabilities and implications of ChatGPT and other AI-assisted technologies in scientific research and education. Immunologi & Cell Biology, 101(10), 923-935. doi:10.1111/imcb.12689

Sallam, M. (2023). ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns. Healthcare, 11(6). doi:10.3390/healthcare11060887

Alagheband, M. R., Mashatan, A., & Zihayat, M. (2020). Time-based Gap Analysis of Cybersecurity Trends in Academic and Digital Media. ACM Transactions on Management Information Systems, 11(4), 1-20. doi:10.1145/3389684

Karami, A., Shah, V., Vaezi, R., & Bansal, A. (2019). Twitter speaks: A case of national disaster situational awareness. Journal of Information Science, 46(3), 313 - 324. doi:10.1177/0165551519828620

Chubb, J., Cowling, P., & Reed, D. (2022). Speeding up to keep up: exploring the use of AI in the research process. AI & Society, 37, 1439–1457. doi:10.1007/s00146-021-01259-0

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.

Kirmani, A. R. (2022). Artificial Intelligence-Enabled Science Poetry. ACS Energy Letters, 8(1), 574-576. doi:10.1021/acsenergylett.2c02758

Dönmez, I., Idil, S., & Gulen, S. (2023). Conducting Academic Research with the AI Interface ChatGPT: Challenges and Opportunities. Journal of STEAM Education, 6(2), 101-118. doi:10.55290/steam.1263404

Fang, D., Yang, H., Gao, B., & Li, X. (2018). iscovering research topics from library electronic references using latent Dirichlet allocation. Library Hi Tech, 36(3), 400-410. doi:10.1108/LHT-06-2017-0132

Salvagno, M., Taccone, F. S., & Gerli, A. G. (2023). Can artificial intelligence help for scientific writing? Critical Care, 27(75). doi:10.1186/s13054-023-04380-2

Okey, O. D., Udo, E. U., Rosa, R. L., Rodríguez, D. Z., & Kleinschmidt, J. H. (2023). Investigating ChatGPT and Cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security, 135. doi:10.1016/j.cose.2023.103476

Resch, B., Usländer, F., & Havas, C. (2017). Combining Machine-learning Topic Models and Spatiotemporal Analysis of Social Media Data for Disaster Footprint and Damage Assessment. Cartography and Geographic Information Science, 45(4), 362-376. doi:10.1080/15230406.2017.1356242

Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent Dirichlet Allocation (LDA) and Topic Modeling: models, applications, a survey. Multimedia Tools and Applications, 78, 15169–15211. doi:10.1007/s11042-018-6894-4

Sumakul, D. T., Hamied, F. A., & Sukyadi, D. (2022). Students’ Perceptions of the Use of AI in a Writing Class. 67th TEFLIN International Virtual Conference & the 9th ICOELT 2021 (pp. 52-57). Atlantis Press.

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., . . . Wright, R. (2023). “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, 71. doi:10.1016/j.ijinfomgt.2023.102642

Haque, M. U., Dharmadasa, I., Sworna, Z. T., Rajapakse, R., & Ahmad, H. (2022). "I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data. doi:10.48550/arXiv.2212.05856

Aydin, Ö., & Karaarslan, E. (2022). OpenAI ChatGPT Generated Literature Review: Digital Twin in Healthcare. Emerging Computer Technologies, 2, 22-31. doi:10.2139/ssrn.4308687

Motamedi, K. K. (1977). Adaptability and Copability: A Study of Social Systems, Their Environment, and Survival. Group & Organization Management, 2(4), 480-490. doi:10.1177/105960117700200409

PricewaterhouseCoopers. (2021). How Organizations Can Mitigate the Risks of AI. Harvard Business Review.

Li, J. C. (2020). Roles of Individual Perception in Technology Adoption at Organization Level: Behavioral Model versus TOE Framework. Journal of System and Management Sciences, 10(3), 97-118. doi:10.33168/JSMS.2020.0308

Microsoft. (2023). 2023 Work Trend Index: Annual Report, Will AI Fix Work? Microsoft.

Beauchamp, G. (1980). The Frankenstein Complex and Asimov's Robots. Mosaic: An Interdisciplinary Critical Journal, 13(3/4), 83-94.

Jung, G. (2018). Our AI Overlord: The Cultural Persistence of Isaac Asimov’s Three Laws of Robotics in Understanding Artificial Intelligence. University of California.

Inoue, M., Fukahori, H., Matsubara, M., Yoshinaga, N., & Tohira, H. (2023). Latent Dirichlet allocation topic modeling of free-text responses exploring the negative impact of the early COVID-19 pandemic on research in nursing. Japan Journal of Nursing Science, 20(2). doi:10.1111/jjns.12520

Salih, B. H., & Abdulazeez, A. (2021). A Review of Principal Component Analysis Algorithm for Dimensionality Reduction. In Journal of Soft Computing and Data Mining (Vol. 2, Issue 1). Penerbit UTHM. https://doi.org/10.30880/jscdm.2021.02.01.003,

Verleysen, M., & François, D. (2005). The Curse of Dimensionality in Data Mining and Time Series Prediction. In Computational Intelligence and Bioinspired System. Springer Berlin Heidelberg. https://doi.org/10.1007/11494669_93, 758–770.

Deng, L. (2018). Artificial Intelligence in the Rising Wave of Deep Learning: The Historical Path and Future Outlook [Perspectives]. IEEE Signal Processing Magazine. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/msp.2017.2762725, Vol. 35, Issue 1, pp. 180–177.

DOI: http://dx.doi.org/10.14203/STIPM.2024.386