Author Affiliations
Abstract
Background: Artificial intelligence (AI)-based simulation is an emerging innovation in surgical education, offering a safe, interactive, and data-driven environment for skill development. Traditional apprenticeship models are increasingly limited by reduced operative exposure, patient safety concerns, and variability in clinical training. Understanding residents’ acceptance of AI technologies is essential for effective integration into surgical education.
Aim: This study aimed to assess surgical residents’ acceptance and perceived usefulness of AI-based simulation in surgical training at Saidu Teaching Hospital, Swat.
Methodology: An analytical cross-sectional study was conducted among 80 surgical residents from various specialties using non-probability convenience sampling. Data were collected through a structured, self-administered questionnaire based on the Technology Acceptance Model, including demographic variables and Likert-scale items assessing perceived usefulness and acceptance. Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 27. Descriptive statistics, Pearson correlation, independent t-tests, and one-way ANOVA were applied, with p ≤ 0.05 considered statistically significant.
Results: Residents reported high perceived usefulness (mean 3.87 ± 0.58) and acceptance (mean 3.74 ± 0.62) of AI-based simulation. A strong positive correlation was found between perceived usefulness and acceptance (r = 0.68, p < 0.001). No significant gender differences were observed, while acceptance varied significantly across training years (p = 0.041).
Conclusion: AI-based simulation is well accepted among surgical residents. Early integration, faculty training, and blended learning approaches are recommended to enhance surgical skill acquisition.
Keywords
Artificial intelligence, Surgical training, Simulation, Resident acceptance, Perceived usefulness.
Introduction
Artificial intelligence (AI) in healthcare education is an eye-opening change in the training of surgical workers, especially in the improvements of technical skills, decision-making, and patient safety. In the past surgical training has been based on the models of apprenticeship; however, the rising procedural intricacy, patient safety issues, and inadequate clinical experience have called into question the suitability of traditional approaches.[1] The simulation provided by AI presents a new approach to a learning process through its immersive, data-driven, and feedback-based learning setting, which enables the simulation of complex surgical situations without posing a threat to patients. This development is especially topical in the age of competency-based medical learning, where objective evaluation and the standardization of skills are required.[2,3]
Across the world, gaps in surgical education are increasing, such as decreased time spent in the operating rooms, more work, and limitation of supervision. Research shows that surgical residents usually have restricted practical exposure to medico-legal factors and resource constraints, which can undermine the acquisition of skills and confidence.[4] The use of AI-based simulation platforms is believed to overcome these challenges by offering repetitive practice, real-time feedback, and performance analytics, which enhance learning efficiency and clinical preparedness. In spite of these benefits, there is a difference in the kind of acceptance and usage of such technologies by trainees.[5]
Considering the latest high-impact evidence, AI-driven simulation has shown to considerably improve surgical competence, decrease rates of errors, and positively influence the outcome of the procedure.[6] The acceptance of these technologies, however, depends on the perceived usefulness, ease of use, institutional support and technological literacy. There are theoretical models to understand the behavioral intentions of users in regard to the integration of AI in learning, and they are theoretical frameworks, namely, the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT).[7] These models emphasize the fact that acceptance is not based on technical availability alone but on the perceptions and attitudes of the users as well.[8]
In the South Asian context and more specifically Pakistan, there is a low adoption of advanced simulation technologies in surgical education because of the infrastructural nature, financial constraints, and untrained faculty.[9] The teaching approach in surgical training programs is usually traditional, with little to no use of digital or AI-driven solutions. This gap raises the issue of a significant gap between international progress and local practice, which may influence the level of competency and preparedness of surgical residents in the area.[10]
In theory, this research is concerned with such constructs as AI-based simulation, which is the application of intelligent systems to develop interactive and adaptive learning environments; perceived usefulness, which is the degree to which individuals believe that the usage of a certain system improves their performance; and acceptance, which can be defined as the desire to implement and use new technology. These constructions are connected and significant in identifying whether AI implementation in surgical training will be successful or not.[11] These dimensions are crucial in understanding so that effective educational interventions and policies can be developed.[12]
There are several controversies and debates despite the potential that AI has in surgical education. The cost-efficiency of AI technologies, ethical aspects, data privacy, and the possibility of excessive reliance on simulation instead of actual clinical practice have been raised.[13] Besides, educators and learners might resist the introduction of innovative tools. These issues support the necessity of context-oriented studies that could assess the advantages and shortcomings of AI-based approaches to training.[14]
Since there is little empirical data in Pakistan and other low- and middle-income countries, the acceptance and perceived usefulness of AI-based simulation in surgical training among residents are limited. This research is thus warranted in investigating these dimensions at a local level with the aim of producing evidence useful in informing policy, curriculum, and resource allocation. The research aims to fill these gaps and, therefore, help in the future development of surgical education and, consequently, patient care outcomes by ensuring that the healthcare professionals are better trained.
Methodology
The purpose of the analytical cross-sectional study based on quantitative data was carried out at the Saidu Teaching Hospital to determine the acceptance and perceived usefulness of AI-based simulation in surgical training among surgical residents. The sample size used in the study was 80 surgical residents of various specialties. Sampling size was determined with the help of the Raosoft sample size calculator with the confidence level of 95% and the margin of error of 5%, and thus an adequate representative sample was obtained. Participants were recruited through a non-probability convenience sampling approach in terms of availability and willingness. The inclusion criteria were the residents who had a minimum of six months of clinical experience, and those who did not want to participate were excluded. The institutional review board granted ethical approval, and informed consent was also obtained before the data collection.
Data collection: Data was gathered in three months through completion of a self-administered and structured (adopted) questionnaire, which was a version of tools that were validated (in a previous study) and which was based on the TAM. The tool was divided into two parts, the demographic items (age, gender, specialty, and year of training) and the questions that determined the perceived usefulness and acceptance of AI-based simulation with a five-point Likert scale (strongly disagree to strongly agree). A pilot test took place on a small sample of the residents before the main study to facilitate clarity, reliability, and relevance to the context of questions. Feedback was used to make necessary changes. The questionnaires were distributed at duty hours through coordination with the heads of the departments, and the participants were provided with enough time to fill them. The process was conducted with respect to confidentiality and anonymity.
Data analysis procedure: Data were entered and analyzed using SPSS version 27. The summary of demographic variables and the key constructs were summarized by means of descriptive statistics, including frequencies, percentages, means, and standard deviations. Associations among variables were tested using inferential statistics. A Pearson correlation test was employed to determine the relationship between the concepts of perceived usefulness and acceptance of AI-based simulation. Independent t-tests and one-way ANOVA were used to compare the mean scores in the groups of demographics. The p-value of less than or equal to 0.05 was statistically significant. The findings were indicated in tables and charts to be read and interpreted.
Results
The demographic information shows that most of the participants were aged between 25-30 years (52.5%) and mostly male (60%), which represents the gender composition of the surgical residency programs in Saidu Teaching Hospital. The highest number of participants was represented by general surgery residents (37.5%), and the majority of the participants were in their early years of training (Years 1 and 2 combined: 60%). This distribution implies that the sample study is representative of the resident population and will also have sufficient variability to analyze perceptions of AI-based simulation by demographics.
| Variable | Category | Frequency (n) | Percentage (%) |
| Age (years) | 25-30 | 42 | 52.5 |
| 31-35 | 28 | 35.0 | |
| >35 | 10 | 12.5 | |
| Gender | Male | 48 | 60.0 |
| Female | 32 | 40.0 | |
| Specialty | General Surgery | 30 | 37.5 |
| Orthopedics | 18 | 22.5 | |
| Gynecology | 16 | 20.0 | |
| Others | 16 | 20.0 | |
| Year of Training | Year 1 | 22 | 27.5 |
| Year 2 | 26 | 32.5 | |
| Year 3 | 20 | 25.0 | |
| Year 4 | 12 | 15.0 |
Table 1: Demographic characteristics of participants (n = 80)
The average values of perceived usefulness (3.87 ± 0.58) and acceptance (3.74 ± 0.62) show that the residents tend to believe that AI-based simulation is beneficial and that they are ready to use it in their training. The standard deviations are relatively small, which has been an indicator of uniformity in responses, whereas the scale of the scores has been used as an indicator of individual variability in perception. These findings represent a preliminary quantitative evaluation of the positive attitudes of residents to the implementation of AI in surgical education.
| Variable | Mean ± standard deviation (SD) | Minimum | Maximum |
| Perceived Usefulness | 3.87 ± 0.58 | 2.40 | 4.90 |
| Acceptance of AI Simulation | 3.74 ± 0.62 | 2.20 | 4.80 |
Table 2: Descriptive statistics of perceived usefulness and acceptance
On an item-by-item basis, residents especially appreciated AI simulation as a means of a safe learning environment (mean 4.10 ± 0.55) and enhancement of surgical skills (mean 4.02 ± 0.65). The low confidence in procedures (mean 3.75 ± 0.68) indicates that, though the residents see the advantages, some might still want to use real-life clinical practice to complement simulation training. Altogether, these results point to certain strengths of AI simulation as well as reveal areas that could be improved in terms of transferring skills to clinical practice.
| Item description | Mean ± SD |
| AI simulation improves surgical skills | 4.02 ± 0.65 |
| Enhances clinical decision-making | 3.89 ± 0.60 |
| Provides safe learning environment | 4.10 ± 0.55 |
| Improves confidence in procedures | 3.75 ± 0.68 |
| Facilitates repetitive practice | 3.88 ± 0.63 |
Table 3: Item-wise distribution of perceived usefulness
A positive relation between the perceived usefulness and acceptance of AI-based simulation was found to have a strong positive correlation (r = 0.68, p < 0.001). It means that people who are aware that AI is educational have higher chances to use it, which proves theoretical frameworks like TAM. The small confidence interval (0.54-0.79) supports the validity of the relationship and indicates the relevance of the relationship between perception and behavioral intention.
| Variables | r-value | p-value | 95% confidence interval (CI) |
| Perceived Usefulness vs Acceptance | 0.68 | <0.001 | 0.54-0.79 |
Table 4: Pearson correlation between perceived usefulness and acceptance
The independent t-tests were found not to have any statistically significant differences between male and female residents in perceived usefulness (p = 0.438) or acceptance (p = 0.517). This means that the gender factor will not affect the attitudes to AI-based simulation among this cohort. The fact that the mean scores of both sexes are similar indicates that both sexes have an equal opportunity and equal perception of the educational value of AI among both male and female residents.
| Variable | Male (Mean ± SD) | Female (Mean ± SD) | t-value | p-value |
| Perceived Usefulness | 3.91 ± 0.55 | 3.81 ± 0.62 | 0.78 | 0.438 |
| Acceptance | 3.78 ± 0.60 | 3.69 ± 0.65 | 0.65 | 0.517 |
Table 5: Comparison of mean scores by gender
One-way ANOVA revealed that there was a significant difference in acceptance by the levels of training years (p = 0.041) but not by the levels of perceived usefulness (p = 0.074). This idea implies that the readiness of residents to embrace AI-based simulation can grow over time and exposure, but their attitude towards its utility is always positive during all the years. The effect sizes (p = 0.10 to accept acceptance) would suggest that the annual training year has a small to moderate effect on adoption.
| Variable | F-value | p-value | Effect Size (η²) |
| Perceived Usefulness | 2.41 | 0.074 | 0.08 |
| Acceptance | 2.88 | 0.041 | 0.10 |
Table 6: ANOVA for differences by year of training
Discussion
These research findings show that surgical residents in Saidu Teaching Hospital spontaneously view AI-based simulation as helpful and that they have a positive acceptance rate. This is in line with international evidence that simulation-based technologies not only improve technical abilities of surgical trainees but also clinical decision-making and surgical trainee confidence.[12] The high level of positive correlation between perceived usefulness and acceptance confirms the assumption of the TAM, which assumes that the intentions of people to behave in a certain way are majorly determined by their perceived advantages of technology.[13] Those residents that recognize the educational worth of AI will thus be more likely to implement it in their training.
International studies are recording comparable trends comparatively. In a study in the United States, surgical residents exposed to AI-based simulators demonstrated an increased rate of skills acquisition and were open to using these tools during regular training.[14] Likewise, junior residents in Europe who were exposed to structured simulation curricula had a higher level of acceptance.[15] This is in line with our observation that residents in their initial years of training demonstrated higher adoption, which means that younger or less experienced trainees might be more responsive to new educational interventions because of less established clinical practices.
South Asian and Pakistani regional studies, however, point out differences in context. The use of AI in surgical education is frequently constrained by a lack of access to sophisticated simulation resources, a high patient workload, and faculty.[16,17] Irrespective of these difficulties, residents in this study indicated moderate to high acceptance, which could be attributed to greater exposure to digital learning tools when adapting to Coronavirus Disease (COVID-19) and the institutional decision to modernize training. These local contextual differences might be seen as the reason why the perceived usefulness was continually high, but adoption differed across years of training.
The item-level analysis showed that residents appreciated the safe learning setting and the possibility of repetitive practice offered by AI simulation. This aligns with the above literature that risk-free, standardized training settings help boost procedural confidence and minimize operational errors.[18] These reduced confidence ratings in the actual practice process indicate that AI simulation is a useful tool but is viewed not as a substitute but as an addition to real-life surgical practice. It is consistent with the concept of blended learning used in surgical education, suggesting the integration of simulation and clinical exposure.[19]
The insignificant gender variations in acceptance and perceived usefulness are consistent with the world literature, indicating that gender is not a major factor influencing attitudes toward technology in surgical education.[20] This observation indicates a fair interaction with AI tools and contributes to the wider applicability of AI-based simulation to a wide range of trainees. It further highlights the significance of institutional support and training culture as opposed to demographic factors in adoption.=
The research also found a strong correlation between the acceptance of AI-based simulation and the year of training. Older residents seemed to be less likely to embrace AI tools, which might be explained by their trust in the role of apprenticeship or the lack of time to use new technologies. These findings are in line with previous research showing that prior exposure to simulation improves the transfer to practice, but delayed introduction can constrain perceived utility.[21] This supports the argument that AI-based simulation should be incorporated into the surgical curriculum at the earliest stage to maximize the learning process.
Altogether, the results emphasize the transformative capability of AI-based simulation in surgical education but also touch upon the contextual and experience-related issues that determine acceptance. By combining TAM and adult learning theories, there is a framework within which these behaviors can be comprehended and curriculum design guided. Raising awareness of AI by mitigating challenges, including infrastructure, faculty development, and curriculum integration, can maximize AI utilization, promote skills gain, and eventually elevate patient safety and surgical outcomes in local and regional settings.[22]
Recommendations:
The given work shows that surgical residents in Saidu Teaching Hospital see AI-based simulation as functional, and there is a good attitude toward its implementation into surgical training. The perceived usefulness is a strong determinant of acceptance that has been found to support theoretical models like the Technology Acceptance Model. Although residents appreciated the safe, routine, and standardized learning setting offered by AI, the adoption did not have any relationship with training year, thus indicating the significance of early exposure. There was no significant difference in gender in terms of attitudes, implying that other influencing factors of technology integration are more powerful in terms of institutions and their curriculum. Such results suggest that AI-based simulation may be used as a support service to conventional surgical training, which will improve skills acquisition, confidence, and clinical decision-making, as well as reduce patient risk.
Conclusion
The given work shows that surgical residents in Saidu Teaching Hospital see AI-based simulation as functional, and there is a good attitude toward its implementation into surgical training. The perceived usefulness is a strong determinant of acceptance that has been found to support theoretical models like the Technology Acceptance Model. Although residents appreciated the safe, routine, and standardized learning setting offered by AI, the adoption did not have any relationship with training year, thus indicating the significance of early exposure. There was no significant difference in gender in terms of attitudes, implying that other influencing factors of technology integration are more powerful in terms of institutions and their curriculum. Such results suggest that AI-based simulation may be used as a support service to conventional surgical training, which will improve skills acquisition, confidence, and clinical decision-making, as well as reduce patient risk.
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Acknowledgments
The authors would like to express their sincere gratitude to Dr. Shah Hussain, Principal/Associate Professor, Janbar College of Nursing, Swat, for his invaluable supervision, guidance, and support throughout the course of this study.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author Information
Corresponding Author:
Sami Ur Rahman
Department of Surgery
Saidu Group of Teaching Hospitals, Swat, Pakistan
Email: [email protected]
Co-Authors:
Kulsoom Nadir
Department of Gynecology and Obstetrics
Saidu Group of Teaching Hospitals, Swat, Pakistan
Muhammad Ilyas, Mehar Nigar, Anwar Khan
Department of Surgery
Saidu Group of Teaching Hospitals, Swat, Pakistan
Abdur Rahman
Department of Dental Surgeon
Smile Dental Care Centre, Charbagh, Pakistan
Shah Hussain
Department of Nursing
Janbar College of Nursing, Swat, Pakistan
Authors Contributions
Dr. Sami Ur Rahman contributed to data collection and data analysis. Dr. Kulsoom Nadir and Dr. Muhammad Ilyas were involved in data collection and literature review. Dr. Mehar Nigar, Dr. Anwar Khan, and Dr. Abdur Rahman contributed to data collection and data organization. Shah Hussain was responsible for data analysis and interpretation.
Ethical Approval
Ethical Approval was obtained from Saidu Teaching Hospital, Swat, Ref No SGTH/IRB/2026/09.
Conflict of Interest Statement
The authors declare that there is no conflict of interest regarding the publication of this paper.
Guarantor
Dr. Sami Ur Rahman is the guarantor of this study and takes full responsibility for the integrity of the data and the accuracy of the data analysis.
DOI
Cite this Article
Rahman SU, Nadir K, Ilyas M, et al. Empowering Surgical Training through Artificial Intelligence: A Cross-Sectional Study on Residents’ Acceptance and Perceived Usefulness of AI-Based Simulation. medtigo J Med. 2026;4(2):e3062424. doi:10.63096/medtigo3062424 Crossref
