From Data Analysis to Creative Arts: The Ubiquity and Impact of Artificial Intelligence in Academia
DOI:
https://doi.org/10.47747/ijets.v4i4.2244Keywords:
Artificial Intelligence, Academia, Data Analysis, Predictive Modelling, Cybersecurity, Personalized LearningAbstract
Integrating Artificial Intelligence in academia has revolutionized various fields with new opportunities for innovation, research, and learning. The capability of AI to analyze enormous amounts of data at such incredibly short times contributes to research advancement across natural sciences, humanities, social sciences, engineering, and healthcare sciences. For instance, in natural sciences, AI algorithms support various types of data analysis and simulation, helping to make new discoveries and provide methods and new approaches to look at existing research methods. AI advances in social sciences employ prediction modeling and machine learning to enhance economic models and other behavioral analyses. AI has presented humanities advancements in text analysis and interpretation of history work, augmenting the research based on historical data with data analysis. In engineering and technology, AI's role is twofold: enhancing physical security and, at the same time, posing new threats in the form of complex cyber threats. In a related context, AI’s application for diagnosis and treatment planning has been observed in the healthcare sector. It has shown the potential capability of improving the care of patients far beyond any imagined capabilities. Nevertheless, the application of AI in academia comes with some challenges. Privacy, protection, ethical views, and prejudice enhancement are some of the most significant issues that should be considered. Despite these challenges, AI creates multi-professional collaboration and advances in knowledge and performance in various scientific disciplines. AI continues to thrive in the future of academia, as future advancement holds possible new research horizons, educational improvement, and world problem-solving. With the rapid evolution of AI, its incorporation into academia and its abuses, biases, and risks need to be constantly reviewed
References
Aiello, K. D., & Simeone, M. (2019). Triangulation of history using textual data. Isis, 110(3), 522-537.
Alhussain, G., Kelly, A., O'Flaherty, E. I., Quinn, D. P., & Flaherty, G. T. (2022). Emerging role of artificial intelligence in global health care. Health Policy and Technology, 11(3), 100661.
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., ... & Albekairy, A. M. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education, 23(1), 689.
Athey, S. (2018). The impact of machine learning on economics. In The economics of artificial intelligence: An agenda (pp. 507-547). University of Chicago Press.
Bhardwaj, A., Kishore, S., & Pandey, D. K. (2022). Artificial intelligence in biological sciences. Life, 12(9), 1430.
Bhatt, P., Sethi, A., Tasgaonkar, V., Shroff, J., Pendharkar, I., Desai, A., ... & Jain, N. K. (2023). Machine learning for cognitive behavioral analysis: datasets, methods, paradigms, and research directions. Brain informatics, 10(1), 18.
Chatterjee, A. (2022). Art in an age of artificial intelligence. Frontiers in Psychology, 13, 1024449.
Checco, A., Bracciale, L., Loreti, P., Pinfield, S., & Bianchi, G. (2021). AI-assisted peer review. Humanities and Social Sciences Communications, 8(1), 1-11.
Currie, G. M. (2023, May). Academic integrity and artificial intelligence: is ChatGPT hype, hero or heresy?. In Seminars in Nuclear Medicine. WB Saunders.
Data analytics network. (2022). How Is AI, Data & Analytics Beneficial to Industry? Implementation of AI, Data & Analytics.
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), 94.
Dehghan, P., Alashwal, H., & Moustafa, A. A. (2022). Applications of machine learning to behavioral sciences: focus on categorical data. Discover Psychology, 2(1), 22.
Essel, H. B., Vlachopoulos, D., Tachie-Menson, A., Johnson, E. E., & Baah, P. K. (2022). The impact of a virtual teaching assistant (chatbot) on students' learning in Ghanaian higher
education. International Journal of Educational Technology in Higher Education, 19(1), 1-19. Ferrara, E. (2023). Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies. arXiv preprint arXiv:2304.07683.
Gangwani, D., & Zhu, X. (2024). Modeling and prediction of business success: a survey. Artificial Intelligence Review, 57(2), 1-51.
George, B., & Wooden, O. (2023). Managing the strategic transformation of higher education through artificial intelligence. Administrative Sciences, 13(9), 196.
Gries, T., & Naudé, W. (2022). Modelling artificial intelligence in economics. Journal for labour market research, 56(1), 12.
Hajkowicz, S., Sanderson, C., Karimi, S., Bratanova, A., & Naughtin, C. (2023). Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960- 2021. Technology in Society, 74, 102260.
Hancock, R., East, M., Mezei, S., Aas, M. B., & Gijsbertsen, B. (2024). Reconsidering Education policy in the era of Generative AI.
Hartwig, B. (2019). Benefits of Artificial Intelligence. Hackr. io.
Holstein, K., & Doroudi, S. (2021). Equity and Artificial Intelligence in Education: Will" AIEd" Amplify or Alleviate Inequities in Education?. arXiv preprint arXiv:2104.12920.
Jain, H., Dhupper, R., Shrivastava, A., Kumar, D., & Kumari, M. (2023). AI-enabled strategies for climate change adaptation: protecting communities, infrastructure, and businesses from the impacts of climate change. Computational Urban Science, 3(1), 25.
Järvelä, S., Nguyen, A., & Hadwin, A. (2023). Human and artificial intelligence collaboration for socially shared regulation in learning. British Journal of Educational Technology.
Johnson, K. B., Wei, W. Q., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., ... & Snowdon, J. L. (2021). Precision medicine, AI, and the future of personalized health care. Clinical and translational science, 14(1), 86-93.
Juan, Y., Dai, Y., Yang, Y., & Zhang, J. (2021). Accelerating materials discovery using machine learning. Journal of Materials Science & Technology, 79, 178-190.
Krause, D. (2023). Mitigating Risks for Financial Firms Using Generative AI Tools. Available at SSRN 4452600.
Kusters, R., Misevic, D., Berry, H., Cully, A., Le Cunff, Y., Dandoy, L., ... & Wehbi, F. (2020).
Interdisciplinary research in artificial intelligence: challenges and opportunities. Frontiers in big data, 3, 577974.
Larriva-Novo, X. A., Vega-Barbas, M., Villagrá, V. A., & Rodrigo, M. S. (2020). Evaluation of cybersecurity data set characteristics for their applicability to neural networks algorithms detecting cybersecurity anomalies. IEEE Access, 8, 9005-9014.
Maharjan, J., Garikipati, A., Dinenno, F. A., Ciobanu, M., Barnes, G., Browning, E., ... & Das, R. (2023).
Machine learning determination of applied behavioral analysis treatment plan type. Brain Informatics, 10(1), 7.
Murdoch, B. (2021). Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Medical Ethics, 22(1), 1-5.
Na, L., Yang, C., Lo, C. C., Zhao, F., Fukuoka, Y., & Aswani, A. (2018). Feasibility of reidentifying individuals in large national physical activity data sets from which protected health.
information has been removed with use of machine learning. JAMA network open, 1(8), e186040-e186040.
Najjar, R. (2023). Redefining radiology: a review of artificial intelligence integration in medical imaging. Diagnostics, 13(17), 2760.
Ojokoh, B. A., Samuel, O. W., Omisore, O. M., Sarumi, O. A., Idowu, P. A., Chimusa, E. R., ... & Katsriku, F. A. (2020). Big data, analytics and artificial intelligence for sustainability. Scientific African, 9, e00551.
Olawade, D. B., Wada, O. J., & Ling, J. (2023). Using artificial intelligence to improve public health: a narrative review. Frontiers in Public Health, 11, 1196397.
Park, J., Teo, T. W., Teo, A., Chang, J., Huang, J. S., & Koo, S. (2023). Integrating artificial intelligence into science lessons: teachers’ experiences and views. International Journal of STEM Education, 10(1), 61.
Pérez, J. Q., Daradoumis, T., & Puig, J. M. M. (2020). Rediscovering the use of chatbots in education: A systematic literature review. Computer Applications in Engineering Education, 28(6), 1549- 1565.
Pyzer-Knapp, E. O., Pitera, J. W., Staar, P. W., Takeda, S., Laino, T., Sanders, D. P., ... & Curioni, A. (2022). Accelerating materials discovery using artificial intelligence, high performance computing and robotics. npj Computational Materials, 8(1), 84.
Roser, M. (2023). The brief history of artificial intelligence: The world has changed fast–what might be next?. Our World in Data.
Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(3), 160.
Sheikh, H., Prins, C., & Schrijvers, E. (2023). Artificial Intelligence: Definition and Background. In Mission AI: The New System Technology (pp. 15-41). Cham: Springer International Publishing.
Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, A review. Cognitive Robotics.
Tallberg, J., Erman, E., Furendal, M., Geith, J., Klamberg, M., & Lundgren, M. (2023). The Global Governance of Artificial Intelligence: Next Steps for Empirical and Normative Research. arXiv preprint arXiv:2305.11528.
Van Dijk, S. H., Brusse-Keizer, M. G., Bucsán, C. C., van der Palen, J., Doggen, C. J., & Lenferink, A. (2023). Artificial intelligence in systematic reviews: promising when appropriately used. BMJ open, 13(7), e072254.
Villamor Martin, M., Kirsch, D. A., & Prieto-Nañez, F. (2023). The promise of machine-learning-driven text analysis techniques for historical research: topic modeling and word
embedding. Management & Organizational History, 18(1), 81-96.
Vishwanatha, J. K., Christian, A., Sambamoorthi, U., Thompson, E. L., Stinson, K., & Syed, T. A. (2023).
Community perspectives on AI/ML and health equity: AIM-AHEAD nationwide stakeholder listening sessions. PLOS Digital Health, 2(6), e0000288.
Wandelt, S., Sun, X., & Zhang, A. (2023). AI-driven assistants for education and research? A case study on ChatGPT for air transport management. Journal of Air Transport Management, 113, 102483.
Wang, K. (2019). Opportunities in open science with AI. Frontiers in big Data, 2, 26.
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., ... & Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4).
Yao, X., Kumar, M. V., Su, E., Miranda, A. F., Saha, A., & Sussman, J. (2024). Evaluating the efficacy of artificial intelligence tools for the automation of systematic reviews in cancer research: A systematic review. Cancer Epidemiology, 88, 102511.
Zhang, Y., Liang, S., Feng, Y., Wang, Q., Sun, F., Chen, S., ... & Pan, H. (2022). Automation of literature screening using machine learning in medical evidence synthesis: a diagnostic test accuracy systematic review protocol. Systematic reviews, 11(1), 1-7.
Zhang, Z., Ning, H., Shi, F., Farha, F., Xu, Y., Xu, J., ... & Choo, K. K. R. (2022). Artificial intelligence in cyber security: research advances, challenges, and opportunities. Artificial Intelligence Review, 1-25.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Bongs Lainjo
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright for this article is retained by the author(s), with first publication rights granted to the journal.
This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/)
