Perceived reliability and use of AI-generated health information among university students in south-west Nigeria
Background: Artificial intelligence (AI) tools are increasingly used for health communication, yet concerns about their reliability and trustworthiness remain, especially in low-resource settings. University students, as early adopters of digital technologies, are an important group for understanding perceptions of AI-generated health information.Objective: This study assessed the perceived reliability and use of AI-generated health information among university students in South-West Nigeria.Methods: A descriptive cross-sectional online survey was conducted among 204 students using a structured questionnaire. Data were analyzed with SPSS version 27, applying descriptive statistics, chi-square tests, and Fisher’s exact tests, with significance set at p < 0.05.Results: The majority of respondents were young adults (mean age 23.3 years) and predominantly female (59.3%). Most students rated AI tools as either moderately reliable (44.1%) or reliable (40.7%), though only 4.4% trusted AI more than health professionals. Age was significantly associated with perceptions of reliability (p = 0.010), while gender and level of study showed no significant association. No significant relationship was found between AI use and perceived reliability (p = 0.515).Conclusion: University students in South-West Nigeria generally perceive AI-generated health information as moderately to highly reliable but continue to place greater trust in human health professionals. Younger students demonstrated greater confidence in AI than older counterparts. These findings highlight the need for digital health literacy interventions, transparent AI systems, and human oversight to ensure safe integration of AI into health communication.
Institutions
- University of Ibadan (NG)
- University of Lagos (NG)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-09-30
- DOI
- https://doi.org/10.5281/zenodo.17528760
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00