Use of Artificial Intelligence among Undergraduate Medical Students at Dhaka National Medical College: A Descriptive Cross-Sectional Study

Background: The use of artificial intelligence (AI) is rapidly increasing among university students worldwide. However, little is known about how undergraduate medical students in Bangladesh engage with AI tools in their education. Objective: To assess the pattern of using AI and the self-reported knowledge and attitude regarding AI among undergraduate medical students of Dhaka National Medical College (DNMC). Methods: A descriptive cross-sectional study was conducted among 1st, 2nd, 3rd and 5th-year MBBS students of DNMC from December 2025 to July 2026 (4th-year students were excluded). By convenience type of non-probability sampling, 125 medical students completed a structured, self-administered questionnaire distributed as a Google Form through students' WhatsApp number. The questionnaire included sociodemographic data, patterns of AI use, and a 10-item, 5-point Likert scale for assessment of self-reported knowledge and attitudes (range 10 to 50). Data were analysed using descriptive statistics, the chi-square test, independent t-test, and ANOVA; p<0.05 was considered significant. Results: Mean age of 125 students was 21.7±1.7 years, among them 56.0% were female. Almost all respondents (97.6%) were familiar with AI. Most of them (89.6%) were current users, most commonly of ChatGPT (81.2%) and Gemini (32.1%). Majority of them (81.3%) thought that AI would be beneficial to their future career and 74.2% felt it should be taught to all medical students, 41.1% felt confident about replacement of medical specialities. Half of respondents (50.0%) were concerned about AI-generated misinformation, and 79.3% reported at least occasional difficulty verifying AI-generated academic content. The composite knowledge–attitude score (mean 37.2±6.0 of 50) rose significantly with academic year (p=0.023, driven by a 1st- versus 5th-year difference). Gender and current AI-use status were not significantly associated with the score. Conclusion: Most of the undergraduate medical students of DNMC have familiarity with use AIs and positive attitudes of it use for their learning. However, some of them are concerned about the misinformation may be produced by the AIs. Hence, structured AI-literacy training, from the first year of the MBBS curriculum, is a demand of the time. J Dhaka National Med. Coll. Hos. 2026; 32 (02): 60-64

Authors

Institutions

Publication Details

Journal
Journal of Dhaka National Medical College & Hospital
Published
2026-10-28
DOI
https://doi.org/10.3329/jdnmch.v32i2.94228
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Use of Artificial Intelligence among Undergraduate Medical Students at Dhaka National Medical College: A Descriptive Cross-Sectional Study

Afrin Islam, Sharmin Jahan, Md Afzal Hossain, Hadi Ashfaq Kitaba et al.
5 links
Journal of Dhaka National Medical College & Hospital
Artificial Intelligence in Healthcare and Education
article

Use of Artificial Intelligence among Undergraduate Medical Students at Dhaka National Medical College: A Descriptive Cross-Sectional Study

Afrin Islam, Sharmin Jahan, Md Afzal Hossain, Hadi Ashfaq Kitaba, Satadal Dey, Arieya Afreen
article en

Abstract

Background: The use of artificial intelligence (AI) is rapidly increasing among university students worldwide. However, little is known about how undergraduate medical students in Bangladesh engage with AI tools in their education. Objective: To assess the pattern of using AI and the self-reported knowledge and attitude regarding AI among undergraduate medical students of Dhaka National Medical College (DNMC). Methods: A descriptive cross-sectional study was conducted among 1st, 2nd, 3rd and 5th-year MBBS students of DNMC from December 2025 to July 2026 (4th-year students were excluded). By convenience type of non-probability sampling, 125 medical students completed a structured, self-administered questionnaire distributed as a Google Form through students' WhatsApp number. The questionnaire included sociodemographic data, patterns of AI use, and a 10-item, 5-point Likert scale for assessment of self-reported knowledge and attitudes (range 10 to 50). Data were analysed using descriptive statistics, the chi-square test, independent t-test, and ANOVA; p<0.05 was considered significant. Results: Mean age of 125 students was 21.7±1.7 years, among them 56.0% were female. Almost all respondents (97.6%) were familiar with AI. Most of them (89.6%) were current users, most commonly of ChatGPT (81.2%) and Gemini (32.1%). Majority of them (81.3%) thought that AI would be beneficial to their future career and 74.2% felt it should be taught to all medical students, 41.1% felt confident about replacement of medical specialities. Half of respondents (50.0%) were concerned about AI-generated misinformation, and 79.3% reported at least occasional difficulty verifying AI-generated academic content. The composite knowledge–attitude score (mean 37.2±6.0 of 50) rose significantly with academic year (p=0.023, driven by a 1st- versus 5th-year difference). Gender and current AI-use status were not significantly associated with the score. Conclusion: Most of the undergraduate medical students of DNMC have familiarity with use AIs and positive attitudes of it use for their learning. However, some of them are concerned about the misinformation may be produced by the AIs. Hence, structured AI-literacy training, from the first year of the MBBS curriculum, is a demand of the time. J Dhaka National Med. Coll. Hos. 2026; 32 (02): 60-64

Journal of Dhaka National Medical College & HospitalVol. 32(2)
Communications Consortium Media Center (US), Dhaka National Medical College (BD)
Good health and well-being, Quality education
Openalex Percentile: Top 20%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon