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Medical students’ knowledge, attitudes, and practices toward generative artificial intelligence in Egypt 2024: a Cross-Sectional study
BMC Medical Education volume 25, Article number: 790 (2025)
Abstract
Background
Generative artificial intelligence (generative AI) has revolutionized the healthcare system for medical students and their educational process worldwide. Several studies assessed its efficacy in improving medical studies learning, and others assessed medical students’ knowledge and attitudes about generative AI. So, we aim to determine the knowledge, attitudes, and practices of generative AI among medical students in Egypt.
Methods
In 2024, a cross-sectional study using a semi-structured questionnaire was performed, targeting 423 students as a convenient sample from 10 selected Egyptian universities by simple random sampling. The questionnaire was distributed via online platforms such as WhatsApp, Facebook, X, Telegram, and Messenger. Data about sociodemographic characteristics, students’ general knowledge about generative AI and its limitations, and their attitudes and practices, respectively, were collected.
Results
About 2\3 (61.5%) of the studied students had satisfactory knowledge about generative AI and (44.7%) had a positive attitude. Males demonstrated significantly higher knowledge than females (69.3% vs. 55%, P-value = 0.003). Knowledge level also differs significantly by university (P-value < 0.001), with Suez-Canal and 6th October university students achieving the highest satisfactory scores. Key significant predictors that affect knowledge level include gender, university and study phase according to binary logistic regression results. Furthermore, there is a positive correlation between satisfactory knowledge and Practice level (r = 0.303, P < 0.001). Finally, most students use generative AI to check Grammar and prepare their homework or assignments.
Conclusion and recommendations
There is moderate knowledge and positive attitude about AI that were reflected in medical students’ practice. However, knowledge about AI influenced by gender, university and study phase. Importantly, better knowledge significantly correlates with greater generative AI practice. Offering specialized courses or faculty modules about AI enhance awareness among Egyptian medical students. Such initiatives will enable them to gain knowledge and apply it effectively and wisely.
Background
Artificial intelligence (AI) is the development of computer systems to perform tasks requiring human intelligence, such as accurate visual perception, speech recognition, timely decision-making, and translation between different languages [1]. Generative artificial intelligence (GAI), a subset of AI, uses enormous amounts of data to generate new information in many formats, such as text, photos, video, audio, and code, making artificial intelligence more powerful and attractive than before [2].
AI is a revolutionary innovation in healthcare that has attracted the attention of clinicians, researchers, physicians, and medical device industry professionals [3]. Many fields have already integrated AI into clinical practice. At first, in education, where AI is beneficial in this process, AI can assess medical students in case-based e-learning [4] or history-taking by using virtual reality [5, 6]. The commonest use of AI in medicine has been in radiology; for example, it can detect several chest X-ray abnormalities with high accuracy [7]. AI applications have expanded to other specialties, such as detecting arrhythmia from ECG or distinguishing between innocent and pathological murmurs in paediatrics [8, 9], also ophthalmology, dermatology, and pathology have benefited from generative AI [10,11,12,13,14,15,16]. It is essential for medical students to understand this significant area and to be aware of its advantages and drawbacks to utilize it effectively.
This rapid rise of generative AI has prompted research into the knowledge, attitudes, and practices of healthcare students regarding AI technologies in general and generative AI specifically. Existing literature from different regions around the world discussed the students’ experiences. Such as the study conducted in Jordan showed moderate knowledge of AI, and different attitudes showing both skepticism about AI replacing human roles and an appreciation for its value, although practical integration into their studies was limited [17]. On the other hand, the study conducted in Egypt showed that nearly half of the dental students have basic knowledge and awareness of AI and its application in their field, also they support the use of AI in dental training, but they are afraid that AI will replace clinicians [18]. Also, in Saudi Arabia on nursing students revealed that personality traits affected their attitudes towards AI, with openness leading to positive attitudes. while neuroticism and agreeableness lead to negative attitudes [19]. Finally, according to a United States study, nursing students showed a cautiously optimistic attitude when they used generative AI, knowing that it can create familiar summaries, but the results from generative AI should be checked carefully [20].
Although these studies provide valuable insights into student perspectives toward AI and generative AI, there remains a need for a specific understanding of medical students’ knowledge, attitudes, and practices toward generative AI across multiple Egyptian universities. Although a study conducted at Ain Shams University provided initial insights for medical students from a single university [21]. A more comprehensive assessment is crucial. So, our study aims to assess Egyptian medical students’ knowledge, attitude, and practice about generative AI as a first step toward the possibility of incorporating this important tool into the Egyptian medical curriculum.
Methodology
Study design
This cross-sectional study involved medical students from 10 Egyptian medical faculties. Data collection occurred in March 2024.
Sample size and sampling technique
The sample size was calculated to be 423 participant using open EPI program [22] with a 5% margin of error assuming that the percentage of medical students with knowledge of and attitudes toward generative AI was (57.7%) [21], and the number of medical students in the selected universities is 73,000, according to the Central Agency for Public Mobilization and Statistics (CAPMAS) 2022–2023 [23], with 10% non -response rate.
The medical students were enrolled as a convenience sample and were selected from 10 randomly selected Egyptian universities by a simple random sampling technique (10 out of 53 Egyptian universities). The enrolled universities were: Zagazig, Al-Azhar, Mansura, Minia, 6th October, Alexandria, Bani-Suef, Menoufia, Suez Canal, and Fayoum.
Students aged 18–25 years, of both sexes, and who had access to online platforms were included.
Data collection
A semi-structured online questionnaire in English was designed via Google Forms. It was adapted from previous studies and modified to capture the necessary data on students’ knowledge, attitudes, and practices regarding generative AI [17, 24, 25]. The questionnaire was shared via online platforms such as WhatsApp, Facebook, X, Telegram, and Messenger.
The questionnaire was divided into 5 sections. The first one was for the consent obtained from the students. In the second one, we collected sociodemographic data about these students such as age, sex, university, study year, residency, current living situation, and studying phase (Academic phase: the first two years in the faculty and clinical phase: included the last three years). This division was based on the universities’ categorization in Egypt.
In the third part, we collected their general knowledge and their opinion about limitations of usage of generative AI. The fourth and fifth parts assessed their attitudes and practices, respectively.
Scoring
The knowledge part about Generative AI consists of 8 questions. The total scores were calculated by awarding (1) for yes and (0) for no answers, making the maximum scoring level for each one (8 points) and the minimum (0 points). A cumulative score of (4 points) or more for each is considered a satisfactory knowledge level, and the students’ opinions about AI’s limitation part consists of 7 questions, with (1) score for yes answers and (0) for no answers.
Attitude and practice questions were 13 and 7, respectively. scoring was generated for attitude by awarding (1) for strongly disagree, (2) for disagree, (3) for neutral, (4) for agree, and (5) for strongly agree. Except for the second question “Suppose artificial intelligence makes a diagnosis, what would you prefer?” Scoring was made by awarding (1) for the generative AI misses almost no diagnosis but often gives a false alarm, (2) for the generative AI gives a false alarm about as often as it misses a diagnosis, and (3) for the generative AI rarely gives a false alarm but sometimes misses a diagnosis. This made the maximum score (63 points) and the minimum score (13 points) with people of (44 points) or more considered to have a good attitude toward generative AI, and a lower score considered a negative attitude toward generative AI.
Finally, the practice score was conducted by awarding (1) for Never, (2) for Rarely, (3) for often, (4) for most of the time, and (5) for all the time, so the score ranged from (35 to 7 points) with people of (17 points) or more score considered to have good practice of generative AI and lower score considered bad practice of generative AI.
Questionnaire validation and piloting
A pilot study was conducted of 40 students, which was (10%) of the expected responses. Also, we assessed the data validation using Cronbach’s Alpha test to check the validity of the questions. Cronbach’s Alpha of (0.708) indicated good consistency all over the questionnaire.
Statistical analysis
Statistical analysis was conducted via IBM SPSS version 27, Data were represented in tables and graphs, Continuous quantitative variables were expressed as mean ± SD & (range), and categorical qualitative variables were expressed as numbers and percentage, suitable statistical tests were used, after normality testing, Mann-Whitney and chi-square tests were conducted, Bonferroni correction for multiple tests was done. Pearson’s correlation coefficient (r) was calculated as a measure of the strength of the association between two numeric variables. Also, a binary logistic regression model was used to assess change in knowledge according to gender, the university, study phase, and residency. The results were considered highly statistically significant when the significance probability was less than (P < 0.001).
Results
Sociodemographic data
Among the 423 studied students, (54.6%) were female, and (45.6%) were male. The mean age was (21.65 ± 1.44) years old. A total of (78.3%) were from the clinical phase, and (21.7%) were from the academic phase. A majority of participants (91.1%) were from governmental universities, while (9.9%) were from private ones, and (66%) were from urban areas, as shown in Table 1.
We found that (45.4%) of the students had solid knowledge, and (63.8%) knew some application of generative AI in their field of interest; however, only (29.8%) knew what deep/machine learning was. Few students had courses to learn generative AI or were taught generative AI in medical curricula (23.9% and 29.6%), respectively. On the other hand, most students were familiar with generative AI in education and various generative AI tools and understood its barriers well (70.2%, 70.4%, and 69.5%), respectively.
Regarding AI usage limitation, (39%) of the studied students reported a lack of knowledge, while (34.8% and 32.9%) of them reported a lack of training centers and access/technical equipment problems, respectively, as shown in Tables 2 and 3.
The total satisfactory level of general knowledge about generative AI was (61.5%), of which males had a statistically significant higher knowledge than females (69.3% versus 55%), respectively and (P-value = 0.003) (Fig. 1). Additionally, we found a statistically significant difference between universities (P-value < 0.001) as Suez-Canal and October 6th university students had the highest satisfactory scores (92.3% and 85.7%) respectively, and Mansoura and Beni Suef university students were the lowest (42.6% and 43.6%) respectively.
Knowledge satisfaction regarding generative AI among medical students participating in this Study, Egypt 2024 (n = 423). Pie diagram representing distribution of the patients studied according to satisfactory knowledge about AI
Additionally, the higher the academic year the higher the satisfactory scores as we found clinical phase students were higher than Academic one in satisfactory scores (74.4% and 58.6% respectively, P-value = 0.01). However, no statistically significant difference was found between urban and rural students (68.8% vs. 56.9% respectively, P-value = 0.17) (see supplementary material).
Finally, the level of general knowledge about generative AI had an impact on opinion about its limitations of usage as we found a statistically significance relationship between levels of knowledge and generative AI ethical and privacy concerns, lack of time due to education burden (P-value = 0.01) for both, The median of knowledge and limitation score was 4 and 2 out of a maximum possible score of 8 and 7 respectively.
Students’ attitudes towards generative AI
This study showed that (44.7%) of students had a positive attitude toward AI in the future, and approximately (75.1%) suggested improvement in the next 10 years. When AI makes a diagnosis, (44.4%) suggested that AI can give false alarms as often as it misses a diagnosis (Fig. 2).
Preferences Regarding Generative AI Diagnosis Among Medical Students Participating in This Study, Egypt 2024 (n = 423). The graph shows the distribution of medical students’ preferences when artificial intelligence is used to make a medical diagnosis, highlighting the varying levels of trust and acceptance of AI-generated diagnostic insights
In general, the most positive attitude among students toward generative AI was in the educational system: “(74.3%) agreed to want medical students to learn generative AI, (64.3%) saw that generative AI will revolutionize education”, and (58.1%) saw that generative AI can help patients who find it difficult to deal with doctors directly. Approximately (70%) agreed that healthcare professionals are only capable of making medical decisions. And (50%) saw that some specialties are prone to be replaced by generative AI, and (74%) were concerned with the transparency of the data used by generative AI; however, only (32.2%) considered generative AI dangerous to the healthcare sector (Fig. 3). (See Supplement material), The median attitude score was 44 out of a maximum possible score of 63.
Attitude toward Generative AI Among Medical Students Participating in This Study, Egypt 2024 (n = 423). The graph illustrates the nuanced attitudes of medical students towards artificial intelligence, including perspectives on generative AI’s potential impact on medical diagnosis, treatment decisions, healthcare professionals’ roles, and others
Students’ practice using generative AI tools
The highest percentage of the students often used generative AI for spelling and grammar checking (28.1%), preparing for homework or assignments (29.1%), conducting their research (30.7%), and idea generation and brainstorming (29.8%) in comparison to other responses.
However, the studied students rarely used generative AI to prepare for their exams (32.4%) and never used generative AI for personality development and other skills, such as courses (32.4%), while (43.3%) never used Al for personal choices or career guidance as shown in (Table 4).
Concerning the generative AI tools used by the studied students, (37.1%) used Chat-GPT 3.5, about 1\3 of them (35.2%) used Chat-GPT 4, and fewer than (20%) used Microsoft Copilot, Grammarly, Gemini, Quill bot, and Perplexity AI; however, (12.5%) of the students used other generative AI tools, and (10.2%) of them did not use any generative AI tool. (see supplementary material). The median practice score was 17 (range 17–18) out of a maximum possible score of 35.
The current study found that there was statistically significant correlations between satisfactory knowledge and Practice level as the higher the knowledge the greater the practice using generative AI tools (coefficient r = 0.303, P-value < 0.001).
Binary logistic regression
Logistic regression analysis shows that Male (OR = 1.871, CI (1.191, 2.941), P-value = 0.007), some universities like Al-Azhar University (P-value = 0.003), Beni Suef university (OR = 0.381, CI (0.150, 0.966), P-value = 0.042), October 6 university (OR = 3.551, CI (1. 135, 11.111), P-value = 0.029), and clinical phase (OR = 0.539, CI (0.296, 0.981), P-value = 0.043) were statistically significant independent predictors of knowledge about generative AI. A logistic regression was performed to ascertain the effects of sex, university, Academic or clinical phase, residency, and living situation on the participants of the study in relation to knowledge. The logistic regression model was highly statistically significant, (χ2 = 47.209, P-value < 0.001). The model explained (Nagelkerke R2 = 14.3%) of the variance in knowledge changes and correctly classified (67%) of cases. Male participants were (1.87 times) more likely to have more knowledge about generative AI than females and Beni Suef was (0.38 times) more likely to have less knowledge about generative AI than Al-Azhar University.
While October 6th students were (3.5 times) more likely to have more knowledge than Al-Azhar University students. Finally, the clinical phase was (0.54 times) more likely to have less knowledge than the academic phase (see supplementary material).
Discussion
Artificial intelligence has gained considerable importance in the medical field in recent years [26, 27]Therefore, it is important to assess the knowledge, attitudes, and practices of healthcare professionals in different specialties, especially the coming generation of medical students, regarding generative AI in Egypt.
Knowledge about AI
Our study revealed a moderate level of knowledge among medical students about understanding generative AI regarding its meaning, usage, application in the medical field, and its limitations and ethical concerns.
Our findings are similar to those of several cross-sectional studies in several countries. Baigi, 2023 [28] was the first systematic review to assess the knowledge, attitude, and skills among medical, dental, and pharmacist students. It summarized 38 cross-sectional studies conducted from 2019 to 2022 around the world. This study was conducted when generative AI was first introduced to the public. They revealed that most students had limited knowledge about generative AI with a positive attitude.
This could be explained as generative AI was a new trend with little understanding of its meaning, lack of access to free paid apps or internet, and lack of teaching centers for new trends about AI, also it could be expected with the medical field studying overwhelming.
Similar findings were presented in other studies among medical students in different Arabic countries such as Saudi Arabia [29] and Kuwait [30].and the study which was conducted by Al-Qerem, 2023 [17] among Jordan medical, dental, and pharmacy students, Al-Qerem, 2023 stated that there was a moderate level of knowledge with a positive attitude and little practice. They interestingly found a positive association between the type of college and both knowledge and attitude, as medical students had more knowledge and attitude scores than dental and pharmacy students.
This could be explained as medical students might be more prone to use generative AI tools in their future work such as in surgical specialties or radiology with much interest in learning AI.
In the current study, there was a statistically significant difference between males and females in knowledge scores where males had a higher knowledge than females (69.3% versus 55%) respectively, in the contrary to the study conducted by Elchaghaby, 2025 [18] in Egypt to assess the knowledge, attitude, and perception of dental students.
This difference in knowledge and usage of AI tools between men and women could be attributed to that men generally have higher engagement and interest in AI, while some women may find interacting with AI tools less appealing due to perceived utility and the specific way of thinking required.
In the current study, a positive association was found between the university and knowledge scores, Students from the 6th October University, which is a private university, had the highest knowledge score followed by Al Azhar University, this can be explained as 6th October students had access to more technology in their educational process than other governmental universities.
Additionally, the Clinical phase students had more knowledge than the academic phase. This could be explained as the clinical phase students learn about internal medicine, surgery, and other specialties making it easier for them to understand the impact of generative AI on the medical field.
Attitude towards AI
In the current study, despite moderate knowledge, most of the studied medical students had a positive attitude towards generative AI, as they consider it an effective tool for medical education and helping patients communicate with doctors. While they still had some concerns about replacing some medical specialties. Most students (67.2%) had some concerns about ethical considerations of using generative AI.
These findings are similar to the study conducted by Elchaghaby, 2025 [18] to assess the knowledge, attitude, and perception of dental students from Cario and Russian dental schools in Egypt, they found a moderate knowledge level with a positive attitude toward generative AI.
Khater, 2023 [21] conducted a study on medical students at Ain Shams University to assess the knowledge and attitude of medical students in Egypt. Khater, stated the same findings where (41.2%) had good knowledge and (57.7%) had a positive attitude toward generative AI.
Hasan, 2024 [31] conducted a cross-sectional study on pharmacy students from different countries around the world including Egypt and also reported that there was moderate knowledge with a positive attitude towards generative AI.
Practice of generative AI
In the current study, moderate knowledge and positive attitude were reflected in medical students’ practice, as most of them use generative AI tools in grammar checking, doing tasks, and homework, and conducting research.
In the contrary, in another cross-sectional study in two different medical schools in Germany, students were asked about AI literacy knowledge and attitudes, which revealed slightly different findings than the current study, as the findings revealed that students had positive attitudes, the ability to critically appraise AI and their use in practice was high; however, their technical understanding was limited [32].
This could be explained as German students and developed countries use technology in their education more frequently than those in the Arab world do and being exposed to incorporation of generative AI learning in medical their curricula.
For generative AI’s increasing importance, several trials have been conducted to incorporate generative AI learning in medical curricula. For example, Moldt, 2023 [25] was an elective course for 12 medical students in Germany, that tried to increase medical awareness. The course succeeds in increasing students’ awareness and attitude paving the way for German medical schools to teach generative AI in their curricula.
Another course was conducted on nurse students in Portland. Hawk, 2024 [20] assessed the influence of applying generative AI in research compared with ordinary research with guidance on the way it is used. They reported positive outcomes as students found it easy to use generative AI tools and find the right information easily.
Finally, male sex, private universities and clinical phase were statistically significant independent predictors of knowledge about generative AI. This study also highlights the need to incorporate AI knowledge into medical curricula.
Study limitations
Our study has several limitations. Our sample was a convenient sample, which can lead to potential selection bias, potential recall bias, and responder bias as the questionnaire was self-reported online. The Nagelkerke R² value (14.3%) indicates modest predictive power of satisfactory knowledge. The size of our sample is too small to extend our conclusions to all medical universities in Egypt. Another potential limitation of our study is the length of the questionnaire, which may discourage some students from completing it. Additionally, since the questionnaire was administered online, it led to exclusion of students with limited digital access. Finally, as the questionnaire is about a new field, “Generative AI,” most of the students are already interested in this field, which results in selection bias. To solve these problems, several studies should be performed in medical schools in Egypt with larger sample sizes and using diverse sample methods for better interpretation.
Although that several strengths exist as the current study assessed the knowledge, attitudes, and practices of generative AI in several medical schools in Egypt not only one medical school discussing this important field among a wide range of medical students in different Egyptian universities, providing a view of Egyptian medical students’ thinking and interpretation of generative AI.
Conclusion
Our study explored the knowledge, attitudes, and practices of Egyptian medical students towards generative AI and revealed that it is crucial for students to learn the basics of generative AI and to use it in their daily practice, as they will use it in their professional work in the future. It also sheds light on the importance of medical schools in Egypt incorporating teaching generative AI and generative AI tools into their medical curriculum to prepare their students for future challenges.
We recommend conducting specific modules to teach medical students the meaning of generative AI, its usage, and its ethical considerations. Also to help them incorporate generative AI tools into their learning process such as helping with single-based answer questions, finally faculties should provide technical support to students wishing to learn AI and guide them to solve any problems regarding the usage of these tools.
Data availability
The dataset was collected from the answer of participants to our questionnaire and here is the google sheet of these answers results: https://docs.google.com/spreadsheets/d/1r025zczOALKjjBXk8ZsgIe7tla9jSSm1/edit?usp=sharing_ouid=110869071363678691856_rtpof=true_sd=true.
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Acknowledgements
We would like to thank our study collaborators who helped us collect responses for the online questionnaires: Rafik Fouad Fayek Khalik, Abdul Basit Zargar, Jihad Farrag, Abdelrahman Hamdy Mahmoud, Lobna H. Mahran, and OLA. M. ELsayed.
Funding
Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).
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Contributions
Omar A. Ghanem contributed to design of the work and analysis of the data collected from university student and interpretation of the data and revising the work.Abdelmonam M Hagag contributed to make questionnaire and analysis of the collected data from university student and interpretation of the data and revising the work.Muhammed E. Kormod contributed to analysis of the data and writing introduction section.Mennatullah A. El-Refaay help in writing Discussion and conclusion section.Alaa Mahmoud Khedr contributed to writing methodology section.Omnia M. Abozaid contributed to writing methodology section.Karim M. Abdelmoaty contributed to writing methodology section.Mona Sami Hamed was the supervisor of this paper and revising the work.All authors reviewed the manuscript.
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This study was conducted in accordance with the principles of the Declaration of Helsinki. The study received ethical approval from the Institutional Review Board of Zagazig University (ZU-IRB# 245/26 March 2024).
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Prior to the commencement of the study, informed consent was obtained from each participant. They were informed about the aim of the study, the procedures involved, and any risks or benefits of the study. Also, they were informed that they could withdraw at any time from the study. The results were used for further statistical evaluation and scientific publication. Respondent anonymity was guaranteed by design.
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Ghanem, O.A., Hagag, A.M., Kormod, M.E. et al. Medical students’ knowledge, attitudes, and practices toward generative artificial intelligence in Egypt 2024: a Cross-Sectional study. BMC Med Educ 25, 790 (2025). https://doi.org/10.1186/s12909-025-07329-x
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DOI: https://doi.org/10.1186/s12909-025-07329-x