Digital Islamic Humanities

Digital Islamic Humanities

Analysis of the Application of Artificial Intelligence in Digital Humanities Research

Document Type : Original Article

Author
Assistant Professor, Department of Psychology and education, Shahid Ashrafi Isfahani University, Isfahan, Iran
Abstract
The primary objective of this study is to analyze the strengths, weaknesses, opportunities, and threats (SWOT) associated with the application of artificial intelligence (AI) in digital humanities research from the perspective of graduate students. This study employed a descriptive–survey research design, and data were collected using two instruments: semi-structured interviews and a researcher-developed questionnaire. The content validity of the instruments was established through expert review, while the reliability of the questionnaire was assessed using Cronbach’s alpha, yielding a coefficient of 0.90.The statistical population consisted of all graduate students at the University of Art, Isfahan. Participants were selected through stratified random sampling for the quantitative phase and purposive sampling for the qualitative phase. The sample size was determined to be 300 using Cochran’s formula.The findings revealed that the principal strengths of AI in digital humanities research include the development of extensive knowledge bases, ease and continuity of use, efficient information storage and retrieval, continuous learning capabilities, effective data management, advanced and personalized user experiences, automation of research tasks and processes, improved decision-making through information analysis, enhancement of researchers’ self-efficacy, and the creation of rich and meaningful research environments.Conversely, the major weaknesses include the absence of adequate security and authentication mechanisms, excessive reliance on existing AI knowledge bases, limitations in information validation, insufficient support for critical thinking, reduced attention to human interaction and personalized support, technical challenges, and the risk of misinterpreting unreliable data.The identified opportunities include the elimination of temporal and geographical constraints, distributed knowledge production and dissemination, preliminary classification of humanities content, advanced search and recommendation systems, machine-based natural language processing, and the development of specialized software for the humanities.Finally, the major threats associated with the application of AI in this field include information security concerns, ethical issues, the dissemination of misinformation, neglect of higher-order cognitive skills, privacy risks, excessive dependence on technology, potential system failures, the high cost of certain AI tools, and the absence of operational and performance standards in digital humanities research.
Keywords
Subjects

 
1.      بهمن‌آبادی، علیرضا، (۱۴۰۲)، هوش مصنوعی و کاربردهای آن در فعالیت‌های پژوهشی، مجله ترویجی علوم و فناوری اطلاعات کشاورزی، ۶ (۲)، ۳۳۴۳.
2.      تقدمی معصومی، محمدمهدی، (۱۴۰۲)، بررسی استفاده از هوش مصنوعی در آموزش و پژوهش دانش‌آموزان، در نخستین همایش ملی جامعه سالم در عصر دیجیتال: رویکردی تفسیری و انتقادی، جهرم، بازیابی از https: //civilica.com/doc/1966122
3.      حسینی‌مقدم، محمد، (۱۴۰۲)، هوش مصنوعی و آینده آموزش دانشگاهی در ایران، فصلنامه پژوهش و برنامه‌ریزی در آموزش عالی، ۲۹ (۱)، ۱۲۵.
4.      خادم‌علیزاده، امیر، (۱۴۰۲)، ارزیابی کاربست هوش مصنوعی در روش‌های پژوهش علوم انسانی و اجتماعی: مزیت‌ها و چالش‌ها، در نخستین همایش ملی روش‌های پژوهش در علوم انسانی و اجتماعی.
5.      رادفر، حمیدرضا، (۱۴۰۳)، اهمیت نقاط دستیابی به منابع اطلاعاتی دیجیتال از دیدگاه پژوهشگران علوم انسانی: مطالعه موردی اعضای هیئت علمی پژوهشگاه علوم انسانی و مطالعات فرهنگی، نشریه بازیابی دانش و نظام‌های معنایی، (۴۱)، ۳۵۶۱.
6.      میرعمادی، زهرا، صدری، علی‌اکبر، (۱۴۰۲)، هوش مصنوعی: یک بررسی جامع، در هفتمین کنفرانس بین‌المللی پژوهش‌های نوین در مهندسی برق، کامپیوتر، مکانیک و مکاترونیک در ایران و جهان اسلام، تهران. بازیابی از https: //civilica.com/doc/1966607.
7.      وحدانی، محسن، (۱۴۰۲)، هوش مصنوعی، فرصت‌ها و تهدیدها، رشد آموزش و سلامت تربیت بدنی.
8.      AlAfnan, M. A, Dishari, S, Jovic, M, & Lomidze, K. (2023). ChatGPT as an educational tool: Opportunities, challenges, and recommendations for communication, business writing, and composition courses. Journal of Artificial Intelligence and Technology, 3(2), 60–68. https: //doi.org/10.37965/jait.2023.0184.
9.      Berry, D. M, & Fagerjord, A. (2017). Digital humanities: Knowledge and critique in a digital age. Cambridge: Polity.
10.  Crawley, E, Malmqvist, J, Licas, W, & Brodeur, D. (2011). The CDIO syllabus v2.0: An updated statement of goals for engineering education. Paper presented at the 7th International Conceive-Design-Implement-Operate Conference, Copenhagen, Denmark.
11. Creswell, J. W. (2007). Qualitative inquiry and research design: Choosing among five approaches. Thousand Oaks, CA: Sage.
12. Deilomgani, M. (2003). Information technology in educational programs. Journal of Educational Technology, 5(22), 102–112.
13. Dunis, C, Middleton, P, Karathanasopolous, A, & Theofilatos, K. (2016). Artificial intelligence in financial markets. Berlin: Springer.
14.  Eaton, S. E. (2023). Academic integrity and AI. University World News.https: //www.universityworldnews.com/post.php?story=20230228133041549.
15.  Filiz, O, Yurdakul, I. K, & Şahin İzmirli, Ö. (2013). Changes in professional development needs of faculty members according to stages of technology use and field differences. Procedia - Social and Behavioral Sciences, 93, 1224–1228.
16. Forum WE. (2018). Driving the sustainability of production systems with fourth industrial revolution innovation. Geneva: World Economic Forum.
17.  Giray, L. (2023). Prompt engineering with ChatGPT: A guide for academic writers. Annals of Biomedical Engineering, 1–5. https: //doi.org/10.1007/s10439-023-03272-4.
18.  Liu, G, Yang, J, Hao, Y, & Zhang, Y. (2018). Big data-informed energy efficiency assessment of China industry sectors based on k-means clustering. Journal of Cleaner Production, 183, 304–314.
19.  Lu, R, Rausch, C, Bolpagni, M, Brilakis, I, & Haas, C. T. (2020). Geometric accuracy of digital twins for structural health monitoring. In Bridge Engineering. IntechOpen.
20.  Müller, J. M, Buliga, O, & Voigt, K. I. (2018). Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0. Technological Forecasting and Social Change, 132, 2–17.
21. McCarthy, J. (2007). What is artificial intelligence? Stanford University. http: //www-formal.stanford.edu/jmc.
22.      Naik, N, Hameed, B. M. Z, Shetty, D. K, et al. (2022). Legal and ethical consideration in artificial intelligence in healthcare: Who takes responsibility? Frontiers in Surgery, 9. https: //doi.org/10.3389/fsurg.2022.862322.
23.   Palomares, I, Porcel, C, Pizzato, L, Guy, I, & Herrera-Viedma, E. (2021). Reciprocal recommender systems: Analysis of state-of-the-art literature, challenges and opportunities on social recommendation. Information Fusion. https: //doi.org/10.1016/j.inffus.2020.12.001
24. Palomares, I, Martínez-Cámara, E, Montes, R, et al. (2021). A panoramic view and SWOT analysis of artificial intelligence for achieving the sustainable development goals by 2030: Progress and prospects. Applied Intelligence, 51, 6497–6527.
25.       Pandya, M. (2012). Cloud computing for libraries: A SWOT analysis. 8th Convention PLANNER. https: //www.researchgate.net/publication/343280498
26. Plano Clark, V, Creswell, J, O’Neil Green, D, & Shope, R. (2008). Mixing quantitative and qualitative approaches: An introduction to emergent mixed methods research. In S. Hesse-Biber & P. Leavy (Eds.), Handbook of emergent methods (pp. xx–xx). New York: The Guilford Press.
27. Psycharis, S. (2011). Presumptions and actions affecting an e-learning adoption by the educational system: Implementation using virtual private networks. University of the Aegean – Department of Primary Education and Greek Pedagogical Institute. http: //www.eurodl.org/?p=&sp=fullarticle=204
28. Rahman, M, Terano, H. J. R, Rahman, N, Salamzadeh, A, & Rahaman, S. (2023). ChatGPT and academic research: A review and recommendations based on practical examples. Journal of Education, Management and Development Studies, 3(1), 1–12. https: //doi.org/10.52631/jemds.v3i1.175
29.  Raynor, W. (1999). International dictionary of artificial intelligence (1st ed.). Routledge. https: //doi.org/10.4324/9781315074108
30.  Shrestha, D. (2019). How artificial intelligence will impact scientific research. Fusemachines Blog. https: //fusemachines.medium.com/how-artificial-intelligence-will-impact-scientific-research-4e6f4face1ae
31.  Slattery, P. (2010). Curriculum development in the postmodern era. New York: Routledge.
32.  Sobir, R. (2020). Micro-, small and medium-sized enterprises (MSMEs) and their role in achieving the sustainable development goals. Department of Economic and Social Affairs. https: //sustainabledevelopment.un.org/content/documents/26073MSMEsandSDGs.pdf
33.  AlAfnan, M. A, Dishari, S, Jovic, M, & Lomidze, K. (2023). ChatGPT as an educational tool: Opportunities, challenges, and recommendations for communication, business writing, and composition courses. Journal of Artificial Intelligence and Technology, 3(2), 60–68. https: //doi.org/10.37965/jait.2023.0184
34.  Berry, D. M, & Fagerjord, A. (2017). Digital humanities: Knowledge and critique in a digital age. Cambridge: Polity.
35.  Crawley, E, Malmqvist, J, Licas, W, & Brodeur, D. (2011). The CDIO syllabus v2.0: An updated statement of goals for engineering education. Paper presented at the 7th International Conceive-Design-Implement-Operate Conference, Copenhagen, Denmark.
36.  Creswell, J. W. (2007). Qualitative inquiry and research design: Choosing among five approaches. Thousand Oaks, CA: Sage.
37.  Deilomgani, M. (2003). Information technology in educational programs. Journal of Educational Technology, 5(22), 102–112.
38.  Dunis, C, Middleton, P, Karathanasopolous, A, & Theofilatos, K. (2016). Artificial intelligence in financial markets. Berlin: Springer.
39.  Eaton, S. E. (2023). Academic integrity and AI. University World News.https: //www.universityworldnews.com/post.php?story=20230228133041549
40.  Filiz, O, Yurdakul, I. K, & Şahin İzmirli, Ö. (2013). Changes in professional development needs of faculty members according to stages of technology use and field differences. Procedia - Social and Behavioral Sciences, 93, 1224–1228.
41.  Forum WE. (2018). Driving the sustainability of production systems with fourth industrial revolution innovation. Geneva: World Economic Forum.
42.  Giray, L. (2023). Prompt engineering with ChatGPT: A guide for academic writers. Annals of Biomedical Engineering, 1–5. https: //doi.org/10.1007/s10439-023-03272-4
43.  Liu, G, Yang, J, Hao, Y, & Zhang, Y. (2018). Big data-informed energy efficiency assessment of China industry sectors based on k-means clustering. Journal of Cleaner Production, 183, 304–314.
44.  Lu, R, Rausch, C, Bolpagni, M, Brilakis, I, & Haas, C. T. (2020). Geometric accuracy of digital twins for structural health monitoring. In Bridge Engineering. IntechOpen.
45.  Müller, J. M, Buliga, O, & Voigt, K. I. (2018). Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0. Technological Forecasting and Social Change, 132, 2–17.
46.  McCarthy, J. (2007). What is artificial intelligence? Stanford University. http: //www-formal.stanford.edu/jmc/
47.    Naik, N, Hameed, B. M. Z, Shetty, D. K, et al. (2022). Legal and ethical consideration in artificial intelligence in healthcare: Who takes responsibility? Frontiers in Surgery, 9. https: //doi.org/10.3389/fsurg.2022.862322
48.   Palomares, I, Porcel, C, Pizzato, L, Guy, I, & Herrera-Viedma, E. (2021). Reciprocal recommender systems: Analysis of state-of-the-art literature, challenges and opportunities on social recommendation. Information Fusion. https: //doi.org/10.1016/j.inffus.2020.12.001
49.  Palomares, I, Martínez-Cámara, E, Montes, R, et al. (2021). A panoramic view and SWOT analysis of artificial intelligence for achieving the sustainable development goals by 2030: Progress and prospects. Applied Intelligence, 51, 6497–6527.
50.        Pandya, M. (2012). Cloud computing for libraries: A SWOT analysis. 8th Convention PLANNER. https: //www.researchgate.net/publication/343280498
51.  Plano Clark, V, Creswell, J, O’Neil Green, D, & Shope, R. (2008). Mixing quantitative and qualitative approaches: An introduction to emergent mixed methods research. In S. Hesse-Biber & P. Leavy (Eds.), Handbook of emergent methods (pp. xx–xx). New York: The Guilford Press.
52.  Psycharis, S. (2011). Presumptions and actions affecting an e-learning adoption by the educational system: Implementation using virtual private networks. University of the Aegean – Department of Primary Education and Greek Pedagogical Institute. http: //www.eurodl.org/?p=&sp=fullarticle=204
53.  Rahman, M, Terano, H. J. R, Rahman, N, Salamzadeh, A, & Rahaman, S. (2023). ChatGPT and academic research: A review and recommendations based on practical examples. Journal of Education, Management and Development Studies, 3(1), 1–12. https: //doi.org/10.52631/jemds.v3i1.175
54.   Raynor, W. (1999). International dictionary of artificial intelligence (1st ed.). Routledge. https: //doi.org/10.4324/9781315074108
55.  Shrestha, D. (2019). How artificial intelligence will impact scientific research. Fusemachines Blog. https: //fusemachines.medium.com/how-artificial-intelligence-will-impact-scientific-research-4e6f4face1ae
56.  Slattery, P. (2010). Curriculum development in the postmodern era. New York: Routledge.
57.         Sobir, R. (2020). Micro-, small and medium-sized enterprises (MSMEs) and their role in achieving the sustainable development goals. Department of Economic and Social Affairs. https: //sustainabledevelopment.un.org/content/documents/26073MSMEsandSDGs.pdf.