The Influence of Artificial Intelligence on Human Decision-Making, Productivity, and Safety in the Field of Education at Pangasinan State University
DOI:
https://doi.org/10.29284/fxyb4r18Keywords:
Artificial Intelligence, Higher Education, Human Decision-Making, Productivity, AI Governance, Data Privacy, Human–AI Interaction.Abstract
The integration of Artificial Intelligence (AI) in higher education has increasingly influenced academic and administrative practices, reshaping how decisions are made, tasks are performed, and institutional risks are managed. This study examined the perceived influence of AI on human decision-making, productivity, and safety within a public higher education institution in the Philippines. A quantitative descriptive research design was employed, using a structured survey questionnaire administered to 300 university personnel with direct exposure to AI-enabled academic and administrative tools. Descriptive statistical techniques, including mean and standard deviation, were used to analyze respondents’ perceptions across the three domains.
The findings indicate that AI is perceived to have a strong positive influence on productivity (composite mean = 4.29), particularly in automating routine tasks, improving efficiency, and supporting workload management. AI was also viewed as a valuable decision-support tool (composite mean = 4.17), enhancing access to information and enabling data-informed academic and administrative decisions. In contrast, perceptions related to AI safety and governance were comparatively moderate (composite mean = 3.95), reflecting cautious confidence in institutional safeguards, data privacy measures, and ethical oversight mechanisms.
Overall, the results suggest an uneven trajectory of AI adoption in higher education, where functional benefits related to productivity and decision support advance more rapidly than governance and safety readiness. The study highlights the importance of balanced AI integration strategies that align technological innovation with ethical governance, policy development, and capacity-building initiatives. By providing empirical evidence from a public university in a developing-country context, this study contributes to ongoing discussions on responsible AI adoption in higher education and offers insights for institutional leaders and policymakers seeking to harness AI while safeguarding human judgment and institutional accountability.
References
1. Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2020). Technology acceptance model in m-learning context: A systematic review. Computers & Education, 125, 389–412.
2. Alon-Barkat, S., & Busuioc, M. (2022). Human–AI interaction in public sector decision-making: Automation bias and selective adherence to algorithmic advice. Journal of Public Administration Research and Theory, 32(1), 96–111. https://doi.org/10.1093/jopart/muab009
3. Boateng, R., Hinson, R., Heeks, R., & Molla, A. (2022). Digital transformation in public sector organizations: Evidence from developing countries. Government Information Quarterly, 39(3), 101686. https://doi.org/10.1016/j.giq.2022.101686
4. Bond, M., Zawacki-Richter, O., & Marín, V. I. (2021). Elements of artificial intelligence in higher education: A systematic review of empirical research. British Journal of Educational Technology, 52(6), 2251–2270. https://doi.org/10.1111/bjet.13196
5. Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence: What it can—and cannot—do for your organization. Harvard Business Review. https://hbr.org/2017/07/the-business-of-artificial-intelligence
6. Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). Adoption of artificial intelligence in higher education: A conceptual framework. International Journal of Information Management, 58, 102207. https://doi.org/10.1016/j.ijinfomgt.2020.102207
7. Crompton, H., Gregory, K., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 1–22. https://doi.org/10.1186/s41239-023-00392-8
8. Dede, C., & Richards, J. (2021). The role of artificial intelligence in advancing teaching and learning. Educational Leadership, 79(3), 18–25.
9. Faraj, S., Pachidi, S., & Sayegh, K. (2024). Working and organizing in the age of artificial intelligence. Organization Science, 35(1), 1–18. https://doi.org/10.1287/orsc.2022.1635
10. Floridi, L., Cowls, J., Beltrametti, M., et al. (2020). AI ethics: From principles to practices. Minds and Machines, 30, 1–28. https://doi.org/10.1007/s11023-020-09539-1
11. Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Educational Technology & Society, 25(1), 1–15. https://doi.org/10.30191/ETS.202201_25(1).0001
12. Ifenthaler, D., & Schumacher, C. (2023). Governance of learning analytics and artificial intelligence in education. Educational Technology Research and Development, 71(2), 589–610. https://doi.org/10.1007/s11423-022-10134-6
13. Isaac, O., Abdullah, Z., Ramayah, T., & Mutahar, A. M. (2020). Internet usage within organizations: A task–technology fit perspective. Journal of Enterprise Information Management, 33(1), 161–179. https://doi.org/10.1108/JEIM-04-2018-0088
14. Jarrahi, M. H. (2021). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision-making. Human–Computer Interaction, 36(5–6), 1–31. https://doi.org/10.1080/07370024.2020.1868991
15. Kasneci, E., Sessler, K., Küchemann, S., et al. (2023). ChatGPT for education: Opportunities and challenges. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
16. Mhlanga, D. (2023). Artificial intelligence in higher education: A risk–benefit analysis. Education and Information Technologies, 28, 7211–7234. https://doi.org/10.1007/s10639-022-11407-2
17. OECD. (2024). AI governance in education systems (OECD Education Working Paper No. 324). OECD Publishing. https://doi.org/10.1787/5f3b2b9f-en
18. Qazi, A., Naseer, K., Qazi, J., et al. (2021). Students’ perception of e-learning in developing countries. Computers & Education, 179, 104990. https://doi.org/10.1016/j.compedu.2021.104990
19. Selwyn, N. (2021). Should robots replace teachers? Artificial intelligence and the future of education. Learning, Media and Technology, 46(1), 1–3. https://doi.org/10.1080/17439884.2020.1863642
20. Tian, L., & Fu, Y. (2023). Ethical challenges in educational AI deployment: Addressing data transparency and bias. Computers and Education: Artificial Intelligence, 4, 100089. https://doi.org/10.1016/j.caeai.2023.100089
21. UNESCO. (2023). Guidance for generative AI in education and research. United Nations Educational, Scientific and Cultural Organization. https://doi.org/10.54675/EWZM9535
22. Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995
23. Winkler, R., & Söllner, M. (2021). Unleashing the potential of artificial intelligence for educational purposes: A review. Information Systems Frontiers, 23, 1615–1630. https://doi.org/10.1007/s10796-020-10085-z
24. Zhai, X., Zhang, M., Yu, X., & Chen, Z. (2024). AI literacy and readiness in higher education: A systematic review. Computers & Education, 196, 104713. https://doi.org/10.1016/j.compedu.2023.104713
25. Zhou, M., Chen, S., & Wang, Y. (2022). Exploring how artificial intelligence affects university teaching: A meta-analysis of empirical studies. Educational Technology Research and Development, 70(4), 895–912. https://doi.org/10.1007/s11423-021-10045-2
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
