ARTIFICIAL INTELLIGENCE IN EDUCATION: EFFECTS ON DECISION-MAKING, HUMAN EFFORT, AND RISK MANAGEMENT    

Authors : Dr. A.K. SINGH; Prof. B.K. SINGH; Prof. S.P. PANDEY

Publishing Date : 2024

DOI : https://doi.org/10.52458/9788197112492.nsp.2024.eb.ch-02

ISBN : 978-81-977620-7-9

Pages : 5 -11

Chapter id : RBS/NSP/EB/ RAASTTSE/2024/Ch-02

Abstract : Artificial Intelligence (AI) is rapidly transforming educational environments by enhancing decision-making, automating tasks, and improving safety measures. While AI tools offer benefits such as streamlined administrative processes, personalized learning, and real-time risk management, they also present challenges. Increased reliance on AI for decision-making may lead to a reduction in human autonomy and critical thinking, as educators and students may become dependent on automated systems for guidance. Similarly, the ease provided by AI could foster reduced human effort and engagement, raising concerns about student motivation and active participation. However, AI’s ability to monitor and predict potential risks adds a significant layer of safety, from preventing security breaches to ensuring safer learning environments. This study aims to explore the dual impacts of AI in education, examining both the positive advancements in efficiency and safety and the potential drawbacks in decision-making and human effort, to provide a balanced perspective on AI's evolving role in educational settings.

Keywords : Artificial Intelligence, Education, Decision-Making, Human Effort, Risk Management, AI Dependency, Educational Technology, Student Engagement, Safety in Education.

Cite : Singh, A., Singh, B., & Pandey, S. (2024). Artificial Intelligence In Education: Effects On Decision-Making, Human Effort, And Risk Management (1st ed., pp. 5-11). Noble Science Press. https://doi.org/10.52458/9788197112492.nsp.2024.eb.ch-02

References :
  1. Sadeghi K., Ojha D., Kaur  P., Mahto R.V., Dhir A. (2024): “Explainable artificial intelligence and agile decision-making in supply chain cyber resilience”, Decision Support Syatem, 180:114194.
  2.  Thomas S.P., Thomas R.W., Manrodt K.B., Rutner S.M. (2013): "An experimental test of negotiation strategy effects on knowledge sharing intentions in buyer-supplier relationships", Journal of Supply Chain Management, 49(2):96–113.
  3. Toorajipour R., Sohrabpour V., Nazarpour A., Oghazi P., Fischl M. (2021): "Artificial intelligence in supply chain management: A systematic literature review", Journal of Business Research, 122:502–517.
  4.  Topuz K., Zengul F.D., Dag A., Almehmi A., Yildirim M.B. (2018): "Predicting graft survival among kidney transplant recipients: A Bayesian decision support model", Decision Support Systems, 106:97–109.
  5.  Tseng T.-L.B., Huang C.-C. (2016): "Sustainable service and energy provision based on agile rule induction", International Journal of Production Economics, 181:273–288.
  6. Wessling K.S., Huber J., Netzer O. (2017): "MTurk character misrepresentation: Assessment and solutions", Journal of Consumer Research, 44(1):211–230.
  7.  Wooster R.B., Paul D.L. (2016): "Leadership positioning among U.S. firms investing in China", International Business Review, 25(1):319–332.
  8. Yang C., Abedin M.Z., Zhang H., Weng F., Hajek P. (2023): "An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors", Annals of Operations Research, 1–28.
  9. Yu L., Li Y., Fan F. (2023): "Employees’ appraisals and trust of artificial intelligences’ transparency and opacity", Behavioral Sciences, 13(4):344.
  10. Zhdanov D., Bhattacharjee S., Bragin M.A. (2022): "Incorporating FAT and privacy aware AI modeling approaches into business decision making frameworks", Decision Support Systems, 155:113715.