Utilisation of ChatGPT and other Artificial Intelligence tools among medical faculty in Uganda: a cross-sectional study
<ns3:p>Background ChatGPT is an open-source large language model that uses deep learning techniques to generate human-like texts. ChatGPT has the potential to revolutionize medical education as it acts as an interactive virtual tutor and personalized learning assistant. We assessed the use of ChatGPT and other Artificial Intelligence (AI) tools among medical faculty in Uganda. Methods We conducted a descriptive cross-sectional study among medical faculty at four public universities in Uganda from November to December 2023. Participants were recruited consecutively. We used a semi-structured questionnaire to collect data on participants’ socio-demographics and the use of AI tools such as ChatGPT. Our outcome variable was the use of ChatGPT and other AI tools. Data were analyzed in Stata version 17.0. Results We recruited 224 medical faculty, majority [75% (167/224)] were male. The median age (interquartile range) was 41 years (34–50). Almost all medical faculty [90% (202/224)] had ever heard of AI tools such as ChatGPT. Over 63% (120/224) of faculty had ever used AI tools. The most commonly used AI tools were ChatGPT (56.3%) and Quill Bot (7.1%). Fifty-six faculty use AI tools for research writing, 37 for summarizing information, 28 for proofreading work, and 28 for setting exams or assignments. Forty faculty use AI tools for non-academic purposes like recreation and learning new skills. Faculty older than 50 years were 40% less likely to use AI tools compared to those aged 24 to 35 years (Adjusted Prevalence Ratio (aPR):0.60; 95% Confidence Interval (CI): [0.45, 0.80]). Conclusion The use of ChatGPT and other AI tools was high among medical faculty in Uganda. Older faculty (>50 years) were less likely to use AI tools compared to younger faculty. Training on AI use in education, formal policies, and guidelines are needed to adequately prepare medical faculty for the integration of AI in medical education.</ns3:p>
Authors
- Sarah Kiguli (ORCID: https://orcid.org/0000-0002-8764-4161)
- Ritah Nantale (ORCID: https://orcid.org/0000-0001-8433-7054)
- Ian Guyton Munabi (ORCID: https://orcid.org/0000-0001-6028-7355)
- Joseph Mpagi
- Milton Wamboko Musaba (ORCID: https://orcid.org/0000-0003-4145-4044)
- David Mukunya (ORCID: https://orcid.org/0000-0002-3892-9777)
- Aloysius Gonzaga Mubuuke (ORCID: https://orcid.org/0000-0001-9930-9443)
- Job Kuteesa
- Faith Oguttu (ORCID: https://orcid.org/0009-0006-7481-3321)
- Jonathan Babuya (ORCID: https://orcid.org/0000-0001-7586-3386)
- Kennedy Pangholi
- Elizabeth Ajalo
- Brenda Nakitto (ORCID: https://orcid.org/0009-0009-8712-3137)
- Steven Ekak (ORCID: https://orcid.org/0009-0003-5927-3038)
- Kirsten Nantongo
- Frank Kayemba
- Suzan Langoya Akuu (ORCID: https://orcid.org/0009-0002-2509-0076)
- Amelia Margaret Namiiro
- Ronald Tweheyo (ORCID: https://orcid.org/0009-0001-4529-9574)
Publication Details
- Journal
- MedEdPublish
- Published
- 2024-10-23
- DOI
- https://doi.org/10.12688/mep.20554.1
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.12