Cross-sectional survey finds chatbot use widespread among Sudanese medical students

A cross-sectional survey of 2,507 medical students in Sudan found that conversational AI chatbot adoption was nearly universal, with 88.8% identifying academic perceived support as the leading reason for engagement. The study also recorded high rates of depressive symptoms and loneliness, while students reporting greater psychological distress were more likely to describe emotional benefits from chatbot interactions. Researchers collected data between November 2025 and March 2026, assessing chatbot use alongside sociodemographic characteristics, usage patterns and perceived psychological benefits.

The sample included 2,507 medical students. Depression-related symptoms were measured with the Patient Health Questionnaire-9, and social isolation was assessed with the Revised UCLA Loneliness Scale-6, allowing the investigators to compare chatbot experiences with two validated measures of psychological distress. Students reported using conversational AI for several purposes.

Academic perceived support was the primary driver of engagement, cited by 88.8% of participants. Smaller proportions reported using chatbots for companionship, at 22.2%, or to cope with stress and loneliness, at 23.1%. Daily use was reported by 41.9% of respondents, indicating especially frequent engagement among a substantial minority of the surveyed students.

The survey found a high prevalence of mental-health and social-well-being difficulties among participants. Mild-to-severe depressive symptoms were present in 70.6% of students according to the PHQ-9, while 65.0% met the survey’s threshold for moderate-to-high loneliness according to the Revised UCLA Loneliness Scale-6. The research therefore characterized chatbot use in a population already carrying a substantial burden of depressive symptoms and social isolation.

Students exhibiting higher levels of psychological distress were significantly more likely to report emotional connectivity, perceived companionship and subjective well-being benefits from their chatbot interactions. However, the authors noted that these correlations remained statistically weak. Because the study was cross-sectional, the measurements represent a snapshot rather than a sequence of events.

The findings nevertheless reveal that these systems occupied both educational and relational roles in the lives of the respondents. Academic support was the dominant reported function, but companionship and coping with stress or loneliness were meaningful uses for subsets of students. The authors described conversational AI as a scalable, low-barrier support mechanism in a conflict-affected setting where access to formal mental health services is severely restricted.

That accessibility does not make the observed benefits equivalent to treatment or establish the long-term safety of chatbot-based support. The study directly measured use, symptoms, loneliness and perceived benefits, but it did not demonstrate that chatbots prevented social isolation, reduced depressive symptoms or worsened either outcome. The authors called for responsible integration and longitudinal research to determine the long-term effects of AI-mediated support on student well-being.

Overall, the survey identifies broad chatbot engagement and substantial psychological distress among Sudanese medical students while highlighting the need for closer examination of how AI tools are used. Its clearest finding is not that chatbots provide a proven mental-health intervention, but that many students already consult them for academic help and some use them in emotionally oriented ways. The weak correlations and cross-sectional design leave the direction and consequences of those relationships unresolved.

Subject of Research: Associations between conversational AI use, depressive symptoms and loneliness among Sudanese medical students

Article Title: Conversational AI chatbots: a double-edged sword for mental health and social well-being among medical students

Article References: Ahmed, A., Mohamed, M. A. E., Ali, W. B. A., Eldouma, M. Y. M., Abdalla MohammedAli, R. M., Barakat, H. O. M., Alapoud, M. M., Ali, A. S. H., Alngar, Y. A. A., Ahmed, S. A. M., Osman, M. M. A., Gassemalseed, T. F. A., Gadallah, A. A., & Eljack, M. M. F. (2026). Conversational AI chatbots: a double-edged sword for mental health and social well-being among medical students. BMC Medical Education. https://doi.org/10.1186/s12909-026-10326-3

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10326-3

Keywords: conversational AI, medical students, Sudan, depressive symptoms, loneliness, cross-sectional survey, digital well-being, academic support, Conversational, chatbots, double-edged, sword