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Trends in Using Artificial Intelligence in Social and Natural Science Research - Journal of TAM Academy

Trends in Using Artificial Intelligence in Social and Natural Science Research

Authors:
Zahid Zufar At Thaariq Ence Surahman Mohammad Sameer Khader Jaradat Irene Mega Mellyana
DOI: 10.58239/tamde.2025.01.006.x
Pages: 100-132
Published: 31.05.2025
Views: 3
Article Type: Review Article
Article Language: English
Sayı 4/1
Volume 4, Issue 1

2025

View All Articles

Abstract

This article discusses trends in the use of artificial intelligence (AI) in social sciences and natural sciences research. The introduction highlights how AI has evolved into an essential tool in both fields, addressing the limitations of traditional methods in social sciences and accelerating data analysis in natural sciences. The research method used is bibliometric analysis, with data collected from Google Scholar using keywords related to AI in social and natural sciences. Relevant articles were selected through a content evaluation and exclusion process, resulting in 1,000 social science publications and 999 natural science publications, which were further analyzed using VOSviewer with such as being outside the five-year range (published from 2020 to 2025). The study's findings indicate that in social sciences, AI is widely used to enhance research effectiveness through faster data processing, particularly in higher education and social policy analysis. Additionally, AI studies in social sciences are expanding, focusing on ethics, regulation, and human-AI interaction. In natural sciences, AI plays a crucial role in resource management, environmental research, and the healthcare industry, including disease diagnosis and drug development. Recent trends also show an increasing use of large language models (LLMs) and natural language processing (NLP) in scientific research. The study concludes that AI has become a key element in both social and natural science research. Recommendations for social science researchers include further exploration of AI’s impact on psychology, law, and education, as well as the use of bibliometric methods. Meanwhile, natural science researchers are advised to focus on improving AI transparency, developing more accurate technologies, and applying AI in environmental and industrial research. Interdisciplinary collaboration is necessary to ensure AI development remains ethical and inclusive.

Keywords

Artificial Intelligence Social Science Natural Science Bibliometric Review Research

  1. Abdelaal, M. (2024). AI in manufacturing: Market analysis and opportunities (arXiv:2407.05426). arXiv. https://doi.org/10.48550/arXiv.2407.05426
  2. Abrams, A. B. (2022). China and America's tech war from AI to 5G: The struggle to shape the future of world order. Rowman & Littlefield.
  3. Abuhassna, H., Awae, F., Adnan, M. A. B. M., Daud, M., & Almheiri, A. S. B. (2024). The information age for education via artificial intelligence and machine learning: A bibliometric and systematic literature analysis. International Journal of Information and Education Technology, 14(5), 700–711. https://doi.org/10.18178/ijiet.2024.14.5.2095
  4. Ahsan, M. M., Luna, S. A., & Siddique, Z. (2022). Machine-learning-based disease diagnosis: A comprehensive review. Healthcare, 10(3), Article 3. https://doi.org/10.3390/healthcare10030541
  5. Akinrinola, O., Okoye, C., & Ugochukwu, C. (2024). Navigating and reviewing ethical dilemmas in AI development: Strategies for transparency, fairness, and accountability. GSC Advanced Research and Reviews, 18, 050–058. https://doi.org/10.30574/gscarr.2024.18.3.0088
  6. Akour, M., & Alenezi, M. (2022). Higher education future in the era of digital transformation. Education Sciences, 12(11), Article 11. https://doi.org/10.3390/educsci12110784
  7. Alkoud, S., Majeed, I., Zainudin, D., & Mhd Sarif, S. (2024). Future research directions and global research trends of applying artificial intelligence in human resources using bibliometric analysis. International Journal of Academic Research in Accounting, Finance and Management Sciences, 14(4), 1354-1377. https://doi.org/10.6007/IJARAFMS/v14-i4/23963
  8. Al-Zahrani, A. M., & Alasmari, T. M. (2024). Exploring the impact of artificial intelligence on higher education: The dynamics of ethical, social, and educational implications. Humanities and Social Sciences Communications, 11(1), 1–12. https://doi.org/10.1057/s41599-024-03432-4
  9. Armstrong, G. W., & Lorch, A. C. (2020). A(eye): A review of current applications of artificial intelligence and machine learning in ophthalmology. International Ophthalmology Clinics, 60(1), 57–71. https://doi.org/10.1097/IIO.0000000000000298
  10. Ashrafian, H. (2015). Artificial intelligence and robot responsibilities: Innovating beyond rights. Science and Engineering Ethics, 21(2), 317–326. https://doi.org/10.1007/s11948-014-9541-0
  11. Atkinson, R. D., & Atkinson, R. D. (2024). China is rapidly becoming a leading innovator in advanced industries. Information Technology and Innovation Foundation.
  12. Babalola, S. S., & Nwanzu, C. L. (2021). The current phase of social sciences research: A thematic overview of the literature. Cogent Social Sciences, 7(1), 1892263. https://doi.org/10.1080/23311886.2021.1892263
  13. Bahoo, S., Cucculelli, M., Goga, X., & Mondolo, J. (2024). Artificial intelligence in finance: A comprehensive review through bibliometric and content analysis. SN Business & Economics, 4(2), 23. https://doi.org/10.1007/s43546-023-00618-x
  14. Bai, A., Wu, C., & Yang, K. (2021). Evolution and features of China's central government funding system for basic research. Frontiers in Research Metrics and Analytics, 6, 751497. https://doi.org/10.3389/frma.2021.751497
  15. Bhatt, P., Sethi, A., Tasgaonkar, V., Shroff, J., Pendharkar, I., Desai, A., Sinha, P., Deshpande, A., Joshi, G., Rahate, A., Jain, P., Walambe, R., Kotecha, K., & Jain, N. K. (2023). Machine learning for cognitive behavioral analysis: Datasets, methods, paradigms, and research directions. Brain Informatics, 10(1), 18. https://doi.org/10.1186/s40708-023-00196-6
  16. Bianchini, S., Müller, M., & Pelletier, P. (2022). Artificial intelligence in science: An emerging general method of invention. Research Policy, 51(10), 104604. https://doi.org/10.1016/j.respol.2022.104604
  17. Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In A. Bohr & K. Memarzadeh (Eds.), Artificial intelligence in healthcare (pp. 25–60). Academic Press. https://doi.org/10.1016/B978-0-12-818438-7.00002-2
  18. Borsboom, D. (2023). Psychological constructs as organizing principles. In L. A. van der Ark, W. H. M. Emons, & R. R. Meijer (Eds.), Essays on contemporary psychometrics (pp. 89–108). Springer International Publishing. https://doi.org/10.1007/978-3-031-10370-4_5
  19. Bouhouita-Guermech, S., Gogognon, P., & Bélisle-Pipon, J.-C. (2023). Specific challenges posed by artificial intelligence in research ethics. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1149082
  20. Bounfour, A. (2016). Digital futures, digital transformation: From lean production to acceluction. Springer International Publishing. https://doi.org/10.1007/978-3-319-23279-9
  21. Braver, T. S., Krug, M. K., Chiew, K. S., Kool, W., Westbrook, J. A., Clement, N. J., Adcock, R. A., Barch, D. M., Botvinick, M. M., Carver, C. S., Cools, R., Custers, R., Dickinson, A., Dweck, C. S., Fishbach, A., Gollwitzer, P. M., Hess, T. M., Isaacowitz, D. M., Mather, M., … for the MOMCAI group. (2014). Mechanisms of motivation–cognition interaction: Challenges and opportunities. Cognitive, Affective, & Behavioral Neuroscience, 14(2), 443–472. https://doi.org/10.3758/s13415-014-0300-0
  22. Bringezu, S., Potočnik, J., Schandl, H., Lu, Y., Ramaswami, A., Swilling, M., & Suh, S. (2016). Multi-scale governance of sustainable natural resource use—challenges and opportunities for monitoring and institutional development at the national and global level. Sustainability, 8(8), Article 8. https://doi.org/10.3390/su8080778
  23. Bulfamante, D. (2023). Generative enterprise search with extensible knowledge base using AI [Yüksek lisans tezi, Politecnico di Torino]. https://webthesis.biblio.polito.it/28491/
  24. Caruso, L. (2018). Digital innovation and the fourth industrial revolution: Epochal social changes? AI & Society, 33(3), 379–392. https://doi.org/10.1007/s00146-017-0736-1
  25. Chen, X., Wu, C.-S., Murakhovs'ka, L., Laban, P., Niu, T., Liu, W., & Xiong, C. (2023). Marvista: Exploring the design of a human-AI collaborative news reading tool (arXiv:2207.08401). arXiv. https://doi.org/10.48550/arXiv.2207.08401
  26. Coulson, R. N., Folse, L. J., & Loh, D. K. (1987). Artificial intelligence and natural resource management. Science, 237(4812), 262–267. https://doi.org/10.1126/science.237.4812.262
  27. Dai, C.-P., Ke, F., Zhang, N., Barrett, A., West, L., Bhowmik, S., Southerland, S. A., & Yuan, X. (2024). Designing conversational agents to support student teacher learning in virtual reality simulation: A case study. Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 1–8. https://doi.org/10.1145/3613905.3637145
  28. Davenport, T. H. (2018). The AI advantage: How to put the artificial intelligence revolution to work. MIT Press.
  29. Díaz-Rodríguez, N., Ser, J. D., Coeckelbergh, M., López de Prado, M., Herrera-Viedma, E., & Herrera, F. (2023). Connecting the dots in trustworthy artificial intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation. Information Fusion, 99, 101896. https://doi.org/10.1016/j.inffus.2023.101896
  30. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
  31. English, N., Zhao, C., Brown, K. L., Catlett, C., & Cagney, K. (2022). Making sense of sensor data: How local environmental conditions add value to social science research. Social Science Computer Review, 40(1), 179–194. https://doi.org/10.1177/0894439320920601
  32. Farina, M., Zhdanov, P., Karimov, A., & Lavazza, A. (2024). AI and society: A virtue ethics approach. AI & Society, 39(3), 1127–1140. https://doi.org/10.1007/s00146-022-01545-5
  33. Feng, T., Xiong, R., & Huan, P. (2023). Productive use of natural resources in agriculture: The main policy lessons. Resources Policy, 85, 103793. https://doi.org/10.1016/j.resourpol.2023.103793
  34. Fischer, G., Giaccardi, E., Eden, H., Sugimoto, M., & Ye, Y. (2005). Beyond binary choices: Integrating individual and social creativity. International Journal of Human-Computer Studies, 63(4), 482–512. https://doi.org/10.1016/j.ijhcs.2005.04.014
  35. Forrester, C. (2025). Rethinking cheating in the age of AI. In Teaching and learning in the age of generative AI: Evidence-based approaches to pedagogy, ethics, and beyond. Routledge.
  36. Franco, G. D., & Santurro, M. (2021). Machine learning, artificial neural networks and social research. Quality & Quantity, 55(3), 1007–1025. https://doi.org/10.1007/s11135-020-01037-y
  37. Gao, F. (2018). 全球知名智库对中国《新一代人工智能发展规划》发布与实施情况的评价及启示. 情报工程, 4(2), 026–035.
  38. Garfield, E. (2006). The history and meaning of the journal impact factor. JAMA, 295(1), 90–93.
  39. Gignac, G. E., & Szodorai, E. T. (2024). Defining intelligence: Bridging the gap between human and artificial perspectives. Intelligence, 104, 101832. https://doi.org/10.1016/j.intell.2024.101832
  40. González, A. L., Moreno, M., Román, A. C. M., Fernández, Y. H., & Pérez, N. C. (2024). Ethics in artificial intelligence: An approach to cybersecurity. Inteligencia Artificial, 27(73), Article 73. https://doi.org/10.4114/intartif.vol27iss73pp38-54
  41. Graesser, A. C., Fiore, S. M., Greiff, S., Andrews-Todd, J., Foltz, P. W., & Hesse, F. W. (2018). Advancing the science of collaborative problem solving. Psychological Science in the Public Interest, 19(2), 59–92. https://doi.org/10.1177/1529100618808244
  42. Grossmann, I. (2023). AI surrogates and the transformation of social science research. OSF Preprints. https://osf.io/h4e2a/
  43. Grossmann, I., Feinberg, M., Parker, D. C., Christakis, N. A., Tetlock, P. E., & Cunningham, W. A. (2023). AI and the transformation of social science research. Science, 380(6650), 1108–1109. https://doi.org/10.1126/science.adi1778
  44. Guleria, A., Krishan, K., Sharma, V., & Kanchan, T. (2023). ChatGPT: Ethical concerns and challenges in academics and research. The Journal of Infection in Developing Countries, 17(09), Article 09. https://doi.org/10.3855/jidc.18738
  45. Haleem, A., Javaid, M., Pratap Singh, R., & Suman, R. (2022). Medical 4.0 technologies for healthcare: Features, capabilities, and applications. Internet of Things and Cyber-Physical Systems, 2, 12–30. https://doi.org/10.1016/j.iotcps.2022.04.001
  46. Haque, Md. A., & Li, S. (2024). Exploring ChatGPT and its impact on society. AI and Ethics. https://doi.org/10.1007/s43681-024-00435-4
  47. Harlow, H. (2018). Ethical concerns of artificial intelligence, big data and data analytics. European Conference on Knowledge Management, 316–323.
  48. Hasas, A., Hakimi, M., Shahidzay, A. K., & Fazil, A. W. (2024). AI for social good: Leveraging artificial intelligence for community development. Journal of Community Service and Society Empowerment, 2(02), 196–210. https://doi.org/10.59653/jcsse.v2i02.592
  49. He, W.-B., Ma, Y.-G., Pang, L.-G., Song, H.-C., & Zhou, K. (2023). High-energy nuclear physics meets machine learning. Nuclear Science and Techniques, 34(6), 88. https://doi.org/10.1007/s41365-023-01233-z
  50. Hisham, A. B., Yusof, N. A. M., Salleh, S. H., & Abas, H. (2024). Transforming governance: A systematic review of AI applications in policymaking. Journal of Science, Technology and Innovation Policy, 10(1), 7–15. https://doi.org/10.11113/jostip.v10n1.148
  51. Hodges, A., & Hofstadter, D. (2014). Alan Turing: The enigma: The book that inspired the film the imitation game (Updated ed.). Princeton University Press.
  52. Hulland, J. (2024). Bibliometric reviews—some guidelines. Journal of the Academy of Marketing Science, 52(4), 935–938. https://doi.org/10.1007/s11747-024-01016-x
  53. Ibrahim, L., Huang, S., Ahmad, L., & Anderljung, M. (2024). Beyond static AI evaluations: Advancing human interaction evaluations for LLM harms and risks (arXiv:2405.10632). arXiv. https://doi.org/10.48550/arXiv.2405.10632
  54. Izard, C. E. (2013). Human emotions. Springer Science & Business Media.
  55. Jiang, Y., Li, X., Luo, H., Yin, S., & Kaynak, O. (2022). Quo vadis artificial intelligence? Discover Artificial Intelligence, 2(1), 4. https://doi.org/10.1007/s44163-022-00022-8
  56. Jiao, L., Song, X., You, C., Liu, X., Li, L., Chen, P., Tang, X., Feng, Z., Liu, F., Guo, Y., Yang, S., Li, Y., Zhang, X., Ma, W., Wang, S., Bai, J., & Hou, B. (2024). AI meets physics: A comprehensive survey. Artificial Intelligence Review, 57(9), 256. https://doi.org/10.1007/s10462-024-10874-4
  57. Jinnuo, Z., Goyal, S. B., Rajawat, A. S., Nassar Waked, H., Ahmad, S., Randhawa, P., Suresh, S., & Naik, N. (2025). Analysis of existing techniques in human emotion and behavioral analysis using deep learning and machine learning models. Engineering Research Express, 7(1), 012201. https://doi.org/10.1088/2631-8695/ada68b
  58. Kang, Y., Gao, S., & Roth, R. E. (2024). Artificial intelligence studies in cartography: A review and synthesis of methods, applications, and ethics. Cartography and Geographic Information Science, 51(4), 599–630. https://doi.org/10.1080/15230406.2023.2295943
  59. Khan, A. (2024). The intersection of artificial intelligence and international trade laws: Challenges and opportunities. IIUM Law Journal, 32, 103.
  60. Khanal, S., Hongzhou, Z., & Taeihagh, A. (2025). Development of new generation of artificial intelligence in China: When Beijing's global ambitions meet local realities. Journal of Contemporary China, 34(151), 19–42. https://doi.org/10.1080/10670564.2024.2333492
  61. Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M. D., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), Article 18. https://doi.org/10.3390/su131810424
  62. Lawal, Y. A., Ayanleke, A. O., & Oshin, I. I. (2024). The impact of AI techniques on human-AI interaction quality in project management: A mixed-methods study. Organization and Human Capital Development, 3(2), 1–17. https://doi.org/10.31098/orcadev.v3i2.2307
  63. Lee, D., & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1), Article 1. https://doi.org/10.3390/ijerph18010271
  64. Lescrauwaet, L., Wagner, H., Yoon, C., & Shukla, S. (2022). Adaptive legal frameworks and economic dynamics in emerging technologies: Navigating the intersection for responsible innovation. Law and Economics, 16(3), Article 3. https://doi.org/10.35335/laweco.v16i3.61
  65. Li, R. (2020). Artificial intelligence revolution: How AI will change our society, economy, and culture. Simon and Schuster.
  66. Liu, Y., & Quan, Q. (2022). AI recognition method of pronunciation errors in oral English speech with the help of big data for personalized learning. Journal of Information & Knowledge Management, 21(Supp02), 2240028. https://doi.org/10.1142/S0219649222400287
  67. Luong, N., & Fedasiuk, R. (2022). State plans, research, and funding. In Chinese power and artificial intelligence. Routledge.
  68. Ma, D., Akram, H., & Chen, I.-H. (2024). Artificial intelligence in higher education: A cross-cultural examination of students' behavioral intentions and attitudes. The International Review of Research in Open and Distributed Learning, 25(3), 134–157. https://doi.org/10.19173/irrodl.v25i3.7703
  69. Madumal, P., Miller, T., Sonenberg, L., & Vetere, F. (2019). A grounded interaction protocol for explainable artificial intelligence (arXiv:1903.02409). arXiv. https://doi.org/10.48550/arXiv.1903.02409
  70. Maghsoudi, M., Shahri, M. K., Kermani, M. A. M. A., & Khanizad, R. (2025). Mapping the landscape of AI-driven human resource management: A social network analysis of research collaboration. IEEE Access, 13, 3090–3114. https://doi.org/10.1109/ACCESS.2024.3523437
  71. Mandavilli, S. R. (2024). Propounding "structured innovative thinking techniques for social sciences research": Why this can be a game changer in social sciences research. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4889628
  72. Marwala, T., & Mpedi, L. G. (2024). Artificial intelligence and the law. In T. Marwala & L. G. Mpedi (Eds.), Artificial intelligence and the law (pp. 1–25). Springer Nature. https://doi.org/10.1007/978-981-97-2827-5_1
  73. McPhee, S. J., & Papadakis, M. (2009). Current medical diagnosis and treatment 2010 (49th ed.). McGraw-Hill Medical.
  74. Meskó, B., Drobni, Z., Bényei, É., Gergely, B., & Győrffy, Z. (2017). Digital health is a cultural transformation of traditional healthcare. mHealth, 3(9), Article 9. https://doi.org/10.21037/mhealth.2017.08.07
  75. Messeri, L., & Crockett, M. J. (2024). Artificial intelligence and illusions of understanding in scientific research. Nature, 627(8002), 49–58. https://doi.org/10.1038/s41586-024-07146-0
  76. Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38. https://doi.org/10.1016/j.artint.2018.07.007
  77. Modiba, M. (2024). Application of conversational generative pre-trained transformer for improvement of information services in academic libraries. South African Journal of Libraries and Information Science, 90(1), Article 1. https://doi.org/10.7553/90-1-2384
  78. Mondal, S., Das, S., Golder, S. S., Bose, R., Sutradhar, S., & Mondal, H. (2024). AI-driven big data analytics for personalized medicine in healthcare: Integrating federated learning, blockchain, and quantum computing. In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA) (pp. 1–6). IEEE. https://doi.org/10.1109/ICAIQSA64000.2024.10882330
  79. Mondal, S., & Palit, D. (2022). Challenges in natural resource management for ecological sustainability. In M. K. Jhariya, R. S. Meena, A. Banerjee, & S. N. Meena (Eds.), Natural resources conservation and advances for sustainability (pp. 29–59). Elsevier. https://doi.org/10.1016/B978-0-12-822976-7.00004-1
  80. Morande, S., Tewari, V., & Kukreja, J. (2025). Decoding the consumer mimic: Influencers, algorithms and the future of marketing. In A. Kumar, M. D. Ciddikie, A. K. Kashyap, & H. W. Akram (Eds.), Marketing 5.0 (pp. 43–56). Emerald Publishing Limited. https://doi.org/10.1108/978-1-83797-815-120251004
  81. Mottaghi-Dastjerdi, N., & Soltany-Rezaee-Rad, M. (2024). Advancements and applications of artificial intelligence in pharmaceutical sciences: A comprehensive review. Iranian Journal of Pharmaceutical Research, 23(1), e150510. https://doi.org/10.5812/ijpr-150510
  82. Muwani, T. S., Ranganai, N., Zivanai, L., & Munyoro, B. (2022). The global digital divide and digital transformation: The benefits and drawbacks of living in a digital society. In Digital transformation for promoting inclusiveness in marginalized communities (pp. 217–236). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-6684-3901-2.ch011
  83. Naamati-Schneider, L. (2024). Enhancing AI competence in health management: Students' experiences with ChatGPT as a learning tool. BMC Medical Education, 24(1), 598. https://doi.org/10.1186/s12909-024-05595-9
  84. Nadjia, M. (2024). The impact of artificial intelligence on legal systems: Challenges and opportunities. Проблеми Законності, 164, 285–303.
  85. Nikolinakos, N. Th. (2023). Ethical principles for trustworthy AI. In N. Th. Nikolinakos (Ed.), EU policy and legal framework for artificial intelligence, robotics and related technologies—The AI Act (pp. 101–166). Springer International Publishing. https://doi.org/10.1007/978-3-031-27953-9_3
  86. Ogilvie, A. D. (2024). Antisocial analagous behavior, alignment and human impact of Google AI systems: Evaluating through the lens of modified antisocial behavior criteria by human interaction, independent LLM analysis, and AI self-reflection. Computer & Society. https://doi.org/10.48550/arXiv.2403.15479
  87. Okon-Singer, H., Hendler, T., Pessoa, L., & Shackman, A. J. (2015). The neurobiology of emotion–cognition interactions: Fundamental questions and strategies for future research. Frontiers in Human Neuroscience, 9. https://doi.org/10.3389/fnhum.2015.00058
  88. Ortony, A. (2022). Are all "basic emotions" emotions? A problem for the (basic) emotions construct. Perspectives on Psychological Science, 17(1), 41–61. https://doi.org/10.1177/1745691620985415
  89. Öztürk, O., Kocaman, R., & Kanbach, D. K. (2024). How to design bibliometric research: An overview and a framework proposal. Review of Managerial Science. https://doi.org/10.1007/s11846-024-00738-0
  90. Pal, S. (2023). A paradigm shift in research: Exploring the intersection of artificial intelligence and research methodology. International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 11(3), 1–7.
  91. Pandey, D. K., Hunjra, A. I., Bhaskar, R., & Al-Faryan, M. A. S. (2023). Artificial intelligence, machine learning and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022. Resources Policy, 86, 104250. https://doi.org/10.1016/j.resourpol.2023.104250
  92. Pang, L.-G. (2024). Studying high-energy nuclear physics with machine learning. International Journal of Modern Physics E, 33(06), 2430009. https://doi.org/10.1142/S0218301324300091
  93. Pattanayak, S. K. (2022). Generative AI for market analysis in business consulting: Revolutionizing data insights and competitive intelligence. International Journal of Enhanced Research in Management & Computer Applications, 11, 74–86.
  94. Payadnya, I. P. A. A., Putri, G. A. M. A., Suwija, I. K., Saelee, S., & Jayantika, I. G. A. N. T. (2025). Cultural integration in AI-enhanced mathematics education: Insights from Southeast Asian educators. Journal for Multicultural Education, 19(1), 58–72. https://doi.org/10.1108/JME-09-2024-0119
  95. Phillips, O. R., Harries, C., Leonardi-Bee, J., Knight, H., Sherar, L. B., Varela-Mato, V., & Morling, J. R. (2024). What are the strengths and limitations to utilising creative methods in public and patient involvement in health and social care research? A qualitative systematic review. Research Involvement and Engagement, 10, 48. https://doi.org/10.1186/s40900-024-00580-4
  96. Prasad, A., Nagda, G., Syed, N., & Kumar, A. (2023). A detailed survey on awareness, knowledge and practice of pesticides used against various vegetables, fruits and cereal crops grown in and around Udaipur region of south Rajasthan, India. Bulletin of Pure & Applied Sciences- Zoology, 42(1), Article 1. https://doi.org/10.48165/bpas.2023.42A.1.6
  97. Qayyum, J., Siddiqui, H. A., Al Prince, A., Ahmad, S., & Raza, M. (2025). Revolutionizing market insights through AI and data analytics: The next era of competitive intelligence. The Critical Review of Social Sciences Studies, 3(1), 3285–3302.
  98. Radanliev, P. (2025). AI ethics: Integrating transparency, fairness, and privacy in AI development. Applied Artificial Intelligence, 39(1), 2463722. https://doi.org/10.1080/08839514.2025.2463722
  99. Rahiman, H. U., & Kodikal, R. (2024). Revolutionizing education: Artificial intelligence empowered learning in higher education. Cogent Education, 11(1), 2293431. https://doi.org/10.1080/2331186X.2023.2293431
  100. Raman, P. (2023). The transformative role of AI in social science research. Uniathena. https://uniathena.com/role-of-AI-in-social-science-research
  101. Saheb, T., & Saheb, T. (2024). Mapping ethical artificial intelligence policy landscape: A mixed method analysis. Science and Engineering Ethics, 30(2), 9. https://doi.org/10.1007/s11948-024-00472-6
  102. Santos, M. F. de L., & Jamil, S. (2024). Bridging the AI divide: Human and responsible AI in news and media industries. Emerging Media, 2(3), 335–346. https://doi.org/10.1177/27523543241291229
  103. Sarker, I. H. (2022). AI-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3(2), 158. https://doi.org/10.1007/s42979-022-01043-x
  104. Scherer, M. U. (2015). Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies. Harvard Journal of Law & Technology, 29, 353.
  105. Schoser, B. (2023). Editorial: Framing artificial intelligence to neuromuscular disorders. Current Opinion in Neurology, 36(5), 424. https://doi.org/10.1097/WCO.0000000000001190
  106. Sebastian, R., Kottekkadan, N. N., Thomas, T. K., & Niyas Kk, M. (2025). Generative AI tools (ChatGPT*) in social science research. Journal of Information, Communication and Ethics in Society, 23(2), 284–290. https://doi.org/10.1108/JICES-10-2024-0145
  107. Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21(1), 125. https://doi.org/10.1186/s12911-021-01488-9
  108. Senyapar, H. N. D. (2024). Artificial intelligence in marketing communication: A comprehensive exploration of the integration and impact of AI. Technium Social Sciences Journal, 55, 64–81. https://doi.org/10.47577/tssj.v55i1.10651
  109. Shah, S. A. R., Zhang, Q., Abbas, J., Tang, H., & Al-Sulaiti, K. I. (2023). Waste management, quality of life and natural resources utilization matter for renewable electricity generation: The main and moderate role of environmental policy. Utilities Policy, 82, 101584. https://doi.org/10.1016/j.jup.2023.101584
  110. Shao, Z., Yuan, S., Wang, Y., & Xu, J. (2021). Evolutions and trends of artificial intelligence (AI): Research, output, influence and competition. Library Hi Tech, 40(3), 704–724. https://doi.org/10.1108/LHT-01-2021-0018
  111. Shin, D., Grover, S., Holstein, K., & Perer, A. (2021). Characterizing human explanation strategies to inform the design of explainable AI for building damage assessment (arXiv:2111.02626). arXiv. https://doi.org/10.48550/arXiv.2111.02626
  112. Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., Hirschberg, J., Kalyanakrishnan, S., Kamar, E., Kraus, S., Leyton-Brown, K., Parkes, D., Press, W., Saxenian, A., Shah, J., Tambe, M., & Teller, A. (2022). Artificial intelligence and life in 2030: The one hundred year study on artificial intelligence (arXiv:2211.06318). arXiv. https://doi.org/10.48550/arXiv.2211.06318
  113. Strauss, M. E., & Smith, G. T. (2009). Construct validity: Advances in theory and methodology. Annual Review of Clinical Psychology, 5, 1–25. https://doi.org/10.1146/annurev.clinpsy.032408.153639
  114. Sun, T., Zhao, K., & Chen, M. (2024). Human-AI interaction: Human behavior routineness shapes AI performance. IEEE Transactions on Knowledge and Data Engineering, 36(12), 8476–8487. https://doi.org/10.1109/TKDE.2024.3480317
  115. Sunarti, S., Rahman, F. F., Naufal, M., Risky, M., Febriyanto, K., & Masnina, R. (2021). Artificial intelligence in healthcare: Opportunities and risk for future. Gaceta Sanitaria, 35, S67–S70. https://doi.org/10.1016/j.gaceta.2020.12.019
  116. Tao, Q., Chao, H., Fang, D., & Dou, D. (2024). Progress in neurorehabilitation research and the support by the National Natural Science Foundation of China from 2010 to 2022. Neural Regeneration Research, 19(1), 226. https://doi.org/10.4103/1673-5374.375342
  117. Tapia, E. B. (2024). Artificial intelligence based on resilient leadership in the health sector. Revista Cientifica Global Negotium, 7(1), Article 1. https://doi.org/10.0833/rgn.v7i1.421
  118. Thacharodi, A., Singh, P., Meenatchi, R., Tawfeeq Ahmed, Z. H., Kumar, R. R. S., V, N., Kavish, S., Maqbool, M., & Hassan, S. (2024). Revolutionizing healthcare and medicine: The impact of modern technologies for a healthier future—A comprehensive review. Health Care Science, 3(5), 329–349. https://doi.org/10.1002/hcs2.115
  119. Tripathi, M. K., Nath, A., Singh, T. P., Ethayathulla, A. S., & Kaur, P. (2021). Evolving scenario of big data and artificial intelligence (AI) in drug discovery. Molecular Diversity, 25(3), 1439–1460. https://doi.org/10.1007/s11030-021-10256-w
  120. Turing, A. M. (1950). Computing machinery and intelligence. Mind, LIX(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433
  121. Wallin, J. A. (2005). Bibliometric methods: Pitfalls and possibilities. Basic & Clinical Pharmacology & Toxicology, 97(5), 261–275. https://doi.org/10.1111/j.1742-7843.2005.pto_139.x
  122. Wan, Q., Miao, X., & Afshan, S. (2022). Dynamic effects of natural resource abundance, green financing, and government environmental concerns toward the sustainable environment in China. Resources Policy, 79, 102954. https://doi.org/10.1016/j.resourpol.2022.102954
  123. Wang, C., Chen, X., Yu, T., Liu, Y., & Jing, Y. (2024). Education reform and change driven by digital technology: A bibliometric study from a global perspective. Humanities and Social Sciences Communications, 11(1), 1–17. https://doi.org/10.1057/s41599-024-02717-y
  124. Wang, F., Guo, W., Xue, R., Baron, C., & Jia, C. (2025). Exploring the subject heterogeneity of scientific research projects funding-example of the Chinese natural science foundation. Information Processing & Management, 62(4), 104098. https://doi.org/10.1016/j.ipm.2025.104098
  125. Wang, H. (2020). Corporate social responsibility in China. In S. Seifi (Ed.), The Palgrave handbook of corporate social responsibility (pp. 1–24). Springer International Publishing. https://doi.org/10.1007/978-3-030-22438-7_71-1
  126. Wiederhold, B. K. (2025). The rise of synthetic societies: Is there a role for humans? Cyberpsychology, Behavior, and Social Networking. https://doi.org/10.1089/cyber.2025.0067
  127. Wu, L., Kim, M., & Markauskaite, L. (2020). Developing young children's empathic perception through digitally mediated interpersonal experience: Principles for a hybrid design of empathy games. British Journal of Educational Technology, 51(4), 1168–1187. https://doi.org/10.1111/bjet.12918
  128. Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4). https://doi.org/10.1016/j.xinn.2021.100179
  129. Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21(1), 21. https://doi.org/10.1186/s41239-024-00453-6
  130. Zhu, J.-J., Yang, M., & Ren, Z. J. (2023). Machine learning in environmental research: Common pitfalls and best practices. Environmental Science & Technology, 57(46), 17671–17689. https://doi.org/10.1021/acs.est.3c00026
  131. 高芳. (2018). 全球知名智库对中国《新一代人工智能发展规划》发布与实施情况的评价及启示. 情报工程, 4(2), 026–035.

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