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Peer-reviewedCC BY 4.0Frontiers in Public Health

AI chatbots for health information-seeking and self-diagnosis among Saudi university students: patterns of use, predictors, attitudes, and trust

  • Haitham Alzghaibi1
  • Manahil Alfuraydan2
  1. 1Department of Health Informatics, College of Applied Medical Sciences, Qassim University
  2. 2Health Information Management and Technology Department, College of Applied Medical Sciences, King Faisal University
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Abstract

Background: The diffusion of large-language-model (LLM) chatbots such as ChatGPT has introduced a new layer to the digital health information ecosystem, alongside search engines, social media, and national mobile-health (mHealth) platforms. The actual prevalence of AI chatbot-based self-diagnosis among Saudi university students, the ecosystem of tools they choose, and the factors that predict use are not well characterised.

Objectives: To estimate AI chatbot use for self-diagnosis among Saudi university students, map the ecosystem of tools used, characterise attitudes and perceived superiority over traditional search, identify predictors through logistic regression, and surface qualitative themes.

Methods: A cross-sectional online survey was administered to 511 Saudi university students between April and May 2024. Instruments included a binary item on prior self-diagnosis use, a multi-response item on AI tools used, an 8-item AI Chatbot Perceived Superiority scale, a 15-item AI Doctor Chatbot Attitudes and Trust scale, five multi-response items, and three open-ended items analysed thematically. Data were analysed using descriptive statistics, multivariable logistic regression, Mann–Whitney U/Kruskal-Wallis tests, tool co-use analysis, and latent class analysis. The study received IRB approval from Qassim University (No. 23-19-02).

Results: ChatGPT was the dominant tool ( n = 355), followed by Google Gemini ( n = 197), Microsoft Copilot ( n = 166), and the Ministry of Health’s Sehhaty AI chatbot ( n = 154). Multivariable logistic regression identified three independent predictors: gender [female vs. male, adjusted OR = 0.32, 95% CI (0.20, 0.52), p < 0.001], age (per step OR = 1.24, p = 0.080), and specialty (Health vs. Education OR = 6.71, p < 0.001). AI was rated easier and faster than search engines ( M = 3.51) and more comprehensive (M = 3.43). Privacy concerns were prominent ( M = 3.69); 65.1% endorsed institutionally verified AI chatbots. Tool co-use analysis showed the seven AI tools were used largely independently (max phi = 0.12).

Conclusion: AI doctor chatbots have already become mainstream tools for self-diagnosis among Saudi university students, with ChatGPT dominating but the Sehhaty AI chatbot already in active use. Male students and those in Health, Management, Social Science, and Engineering are substantially more likely to use AI. The strong endorsement of institutionally verified chatbots provides a clear policy signal for embedding safe, MOH-aligned LLM tools within Sehhaty under Vision 2030.

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Cite

BibTeXRIS
BibTeX
@article{alzghaibi2026chatbots,
  title = {{AI chatbots for health information-seeking and self-diagnosis among Saudi university students: patterns of use, predictors, attitudes, and trust}},
  author = {Haitham Alzghaibi and Manahil Alfuraydan},
  journal = {Frontiers in Public Health},
  year = {2026},
  month = oct,
  doi = {10.3389/fpubh.2026.1899249},
  url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.1899249/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - AI chatbots for health information-seeking and self-diagnosis among Saudi university students: patterns of use, predictors, attitudes, and trust
AU  - Haitham Alzghaibi
AU  - Manahil Alfuraydan
PY  - 2026
DA  - 2026/10/08
JO  - Frontiers in Public Health
DO  - 10.3389/fpubh.2026.1899249
UR  - https://www.frontiersin.org/articles/10.3389/fpubh.2026.1899249/full
AB  - Background: The diffusion of large-language-model (LLM) chatbots such as ChatGPT has introduced a new layer to the digital health information ecosystem, alongside search engines, social media, and national mobile-health (mHealth) platforms. The actual prevalence of AI chatbot-based self-diagnosis among Saudi university students, the ecosystem of tools they choose, and the factors that predict use are not well characterised.  Objectives: To estimate AI chatbot use for self-diagnosis among Saudi university students, map the ecosystem of tools used, characterise attitudes and perceived superiority over traditional search, identify predictors through logistic regression, and surface qualitative themes.  Methods: A cross-sectional online survey was administered to 511 Saudi university students between April and May 2024. Instruments included a binary item on prior self-diagnosis use, a multi-response item on AI tools used, an 8-item AI Chatbot Perceived Superiority scale, a 15-item AI Doctor Chatbot Attitudes and Trust scale, five multi-response items, and three open-ended items analysed thematically. Data were analysed using descriptive statistics, multivariable logistic regression, Mann–Whitney U/Kruskal-Wallis tests, tool co-use analysis, and latent class analysis. The study received IRB approval from Qassim University (No. 23-19-02).  Results: ChatGPT was the dominant tool ( n = 355), followed by Google Gemini ( n = 197), Microsoft Copilot ( n = 166), and the Ministry of Health’s Sehhaty AI chatbot ( n = 154). Multivariable logistic regression identified three independent predictors: gender [female vs. male, adjusted OR = 0.32, 95% CI (0.20, 0.52), p < 0.001], age (per step OR = 1.24, p = 0.080), and specialty (Health vs. Education OR = 6.71, p < 0.001). AI was rated easier and faster than search engines ( M = 3.51) and more comprehensive (M = 3.43). Privacy concerns were prominent ( M = 3.69); 65.1% endorsed institutionally verified AI chatbots. Tool co-use analysis showed the seven AI tools were used largely independently (max phi = 0.12).  Conclusion: AI doctor chatbots have already become mainstream tools for self-diagnosis among Saudi university students, with ChatGPT dominating but the Sehhaty AI chatbot already in active use. Male students and those in Health, Management, Social Science, and Engineering are substantially more likely to use AI. The strong endorsement of institutionally verified chatbots provides a clear policy signal for embedding safe, MOH-aligned LLM tools within Sehhaty under Vision 2030.
SN  - 2296-2565
ER  -