Empowerment Through Information: How AI-Driven Education Impacts Decision-Making Autonomy in Fertility Treatment

Dyah Juliastuti; Royani Royani; Ida Farida; Ihsan Nuril Hikam; Ava Garcia

doi:10.34306/itsdi.v6i2.694

Authors

Dyah Juliastuti Ichsan Satya University https://orcid.org/0000-0002-4159-9166
Royani Royani Ichsan Satya University https://orcid.org/0000-0002-2746-5364
Ida Farida University of Yarsi Pratama
Ihsan Nuril Hikam University of Raharja https://orcid.org/0009-0000-0345-9763
Ava Garcia ADI Incorporation https://orcid.org/0000-0001-9578-5848

DOI:

https://doi.org/10.34306/itsdi.v6i2.694

Keywords:

AI Driven Education, Decision Making Autonomy, Patient Understanding, Fertility Treatment

Abstract

Artificial intelligence (AI) is transforming healthcare by enhancing patient education and supporting decision making autonomy. While AI application in various healthcare contexts is well documented, its role in empowering decision making autonomy in fertility treatments remains underexplored. This study introduces a novel perspective by evaluating the impact of AI driven education on patient understanding and autonomy in fertility treatments, incorporating the moderating roles of trust in AI and prior patient experience. Using Partial Least Squares Structural Equation Modeling (PLS SEM), data was analyzed from individuals undergoing or considering fertility treatments. The results show that AI driven education significantly enhances both understanding and autonomy, with understanding acting as a critical mediator. Notably, trust in AI and prior experience primarily influence understanding but do not directly affect autonomy, highlighting the complex relationship between knowledge and decision making independence. This research emphasizes the need to tailor AI tools to address both informational and psychological needs of patients, marking a novel contribution to AI driven patient centered care, especially in sensitive medical contexts. The studys findings underscore the importance of expanding the sample to diverse populations and examining psychological and cultural variables, areas that have been minimally addressed in previous AI healthcare research.

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