Quantitative results
A total of 236 participants completed the survey, comprising 83 faculty members (35.2%) and 153 medical students (64.8%). Among faculty, majority were aged 31 to 40 years (41%, n = 34), held positions as Assistant Professors (50.6%, n = 42) and had 1 to 5 years of teaching experience (25.3%, n = 21). Majority of the students belonged to the 20 to 21 year-old age group (59.5%, n = 91) and were in their 3rd year of study (47.1%, n = 72) (Table 1).
The internal consistency of both surveys, measured using Cronbach’s alpha, was 0.94 for the 24-item faculty survey and 0.94 for the 22-item student survey, indicating excellent reliability. Responses to the surveys revealed key insights into the respondents’ self-perceived knowledge and current practices related to AI, as well as their attitudes towards AI’s integration into medical education. Faculty members had significantly higher mean composite attitude scores compared to medical students (3.95 ± 0.63 versus 3.81 ± 0.75, p = 0.040). Analysis of mean composite knowledge scores showed that both groups perceived themselves as knowledgeable, with faculty scoring 3.53 ± 0.66 and students 3.55 ± 0.73, though the difference was not statistically significant (p = 0.870). Similarly, mean composite practice scores related to AI use were also similar, with no statistically significant difference between faculty (3.19 ± 0.87) and students (3.23 ± 0.89, p = 0.891) (Table 2).
Evaluation of individual item responses by faculty (Fig. 1) and students (Fig. 2) revealed that while 79.6% (n = 66) of faculty and 79.1% (n = 121) of students believed that they possess a general understanding of AI, only 42.1% (n = 35) of faculty and 41.2% (n = 63) of students were aware of AI subsets, such as machine learning or deep learning. Additionally, approximately only half of the faculty members (50.6%, n = 42) and 42.5% (n = 65) of students were aware of the AI tools currently being utilised in medical education. Similarly, while a vast majority of faculty (84.3%, n = 70) and students (74.5%, n = 114) believed that the integration of AI in medical education could improve the teaching and learning experience, only 45.7% (n = 38) of faculty and 45.1% (n = 69) of students reported to be confident while using AI-based tools in their teaching and learning practices. Less than half of the respondents (45.8% of both faculty members and students, n = 38 and n = 70, respectively) reported to have personally used AI tools or technologies in their medical education practices. However, when asked about their interest in learning more about the utility of AI in medical education, majority of the respondents expressed a positive attitude, with 84.3% (n = 70) of faculty members and 66.0% (n = 101) of students stating that they would like to actively seek out opportunities such as workshops, webinars, or conferences to further enhance their understanding of AI and its utility.
Responses of the faculty members to the administered survey (n = 83). x-axis: percentage, y-axis: survey item
Responses of the students to the administered survey (n = 153). x-axis: percentage, y-axis: survey item
Subgroup analyses of the mean composite scores of knowledge, attitude and practices across the demographic variables revealed that, among medical students, age was significantly associated with mean knowledge and mean attitude levels, with older students reporting greater self-perceived knowledge of AI (p = 0.010), and more positive attitudes towards AI’s utilitiy and integration in medical education (p = 0.016). Additionally, male students were found to have significantly higher mean knowledge scores than their female counterparts (3.69 ± 1.02 versus 3.49 ± 0.57, p = 0.025), however this difference was not significant after adjusting for multiple comparisons. Among faculty respondents, no significant differences in mean knowledge, attitude, or practice scores were observed across age, gender, teaching position, or teaching experience (Table 3).
Qualitative results
In the qualitative component, three FGDs were conducted, with eight medical students per group, and six IDIs were held with faculty members Several themes emerged from these discussions, providing insights into the understanding, opportunities, challenges, and ethical considerations associated with integrating AI into medical education (Table 4).
Commonly emerged themes and subthemes
AI in medical education—understanding of AI
Participants’ understanding of AI varied significantly between faculty and students, highlighting disparities in exposure and familiarity with technology. Faculty members viewed AI as a sophisticated tool that could mimic cognitive abilities and enhance decision-making processes, reflecting their academic and clinical focus. In contrast, students associated AI with basic computer functionalities, indicating a more superficial understanding. Faculty perceived AI as a transformative tool capable of augmenting their teaching and research. Their advanced understanding likely stems from their involvement in curriculum development and exposure to AI-driven research tools.
“It (AI) is the development and application of the algorithm for mimicking human thinking and decision-making.” -IDI_Faculty_5
Students’ limited understanding, often confined to practical tools like ChatGPT, reflects a narrower application-focused perspective. Their emphasis on tangible benefits, such as generating flashcards or mnemonics, highlights the potential of AI for simplifying routine learning tasks but also reveals a gap in understanding its broader implications.
“We use ChatGPT to get our answers within seconds. We can use it for generating mnemonics or flash cards.” -FGD_1_Student_6
This gap underscores the need for differentiated AI education, tailored to the prior knowledge and professional context of each group. It also underscores the importance of foundational AI education for both students and faculty, with tailored content to address their specific needs.
AI in medical education -use of AI in education and research
Participants acknowledged AI’s potential to revolutionize both education and research, albeit in different ways. Faculty emphasized AI’s role in enhancing conceptual understanding and enabling more effective lesson planning. For instance, they viewed AI as a tool to distill complex information into accessible formats. Faculty’s reliance on AI for planning reflects its potential to streamline their workload, allowing them to focus on higher-order teaching and mentoring activities. Students, on the other hand, highlighted AI’s utility for self-directed learning and research, particularly in finding resources and generating study aids. Their emphasis on efficiency and accessibility suggests that AI can democratize learning, particularly in resource-constrained settings. This dichotomy reveals an important opportunity for institutions to guide both groups in using AI responsibly, balancing efficiency with critical thinking and conceptual depth.
Ethical and moral considerations
Participants raised significant concerns about ethical issues, including data privacy and the potential misuse of AI. Faculty, in particular, emphasized the importance of accountability frameworks to safeguard against these risks.
“Making someone accountable is the way to deal with things in a better way… clear guidelines given to the learners about the legalities, the penalties they’re going to face because of use of this AI or irrational use of the AI tool will make them accountable for their acts…” -IDI_Faculty_3.
Their suggestions for legal education and clear guidelines reflect a proactive approach to addressing these challenges, underscoring the importance of ethical literacy in AI education.
Role of institutional support
Both faculty and students stressed the critical role of institutional support in facilitating AI integration. This includes not only providing access to resources but also fostering an environment that encourages innovation and collaboration. Faculty emphasized the importance of validation and supervision, reflecting their concerns about maintaining academic standards and integrity. Students’ call for free institutional access to paid AI tools highlights the potential for institutions to bridge resource gaps and democratize access to technology.
Theme-specific insights: faculty perspectives
Limitations of AI in education
Faculty members raised significant concerns about over-reliance on AI, cautioning that excessive dependence could undermine students’ critical thinking and problem-solving skills. This reflects a broader global concern about AI potentially reducing the role of human judgment in education. Faculty framed AI as a supplementary tool rather than a replacement for traditional methods, emphasizing the irreplaceable value of human insight in teaching and learning. Their apprehension underscores the need for frameworks that integrate AI thoughtfully into medical education, ensuring that it complements rather than replaces critical pedagogical practices.
Future recommendations
Faculty stressed the importance of a phased approach to AI integration, beginning with foundational training for educators. This highlights their awareness of the challenges associated with incorporating new technologies into entrenched curricula. Their preference for integrating AI into existing modules rather than creating standalone courses suggests a practical approach to curriculum development, aiming to minimize disruptions while maximizing impact. Faculty also emphasized the need for institutional readiness, reflecting their recognition of systemic barriers that could hinder AI adoption, such as limited resources and resistance to change.
“To start with, in our context- we should educate the faculty first… then we must pursue it accordingly.” -IDI_Faculty_5
Theme-specific insights: student perspectives
Barriers to AI integration
Students identified several barriers, including technological constraints, financial challenges, and limited accessibility to AI tools. Their concerns reflect the broader socio-economic challenges faced by students in resource-limited settings. The financial burden of accessing advanced AI tools, combined with the lack of institutional support, emerged as a significant obstacle. This highlights the need for institutions to invest in infrastructure and provide equitable access to resources.
So there are paid websites of AI tools which an individual cannot buy easily. So an institute can provide free institutional access to the students.” – FGD_3_Student_3
Future recommendations
Students advocated for a practical, application-focused approach to AI education, emphasizing the importance of hands-on training. Their preference for basics reflects their current knowledge gaps and underscores the need for incremental learning pathways. This theme highlights the potential of AI to level the playing field for students, particularly in under-resourced settings. However, it also raises questions about the depth of engagement required to foster a nuanced understanding of AI’s capabilities and limitations.
“Our undergraduate programs are already very extensive and if you will incorporate AI extensively in our curriculum students will not be able to grasp it. So I think sticking to the basics of AI and how to simply use it will be enough.” -FGD_1_Student_2.
