Investigating Research Skills and Artificial Intelligence Attitudes Among Residents in Surgical Medicine Departments

Authors

DOI:

https://doi.org/10.46328/ijtes.7805

Keywords:

Surgical specialties, Medical education, Specialist training, Scientific research skills, Attitudes towards artificial intelligence

Abstract

This research aims to examine the relationship between scientific research skills and attitudes towards artificial intelligence among residents in surgical medicine departments, considering gender and seniority. A quantitative research approach with a comparative correlational survey model was adopted; data was collected via online questionnaires from 127 surgical residents in training and research hospitals and university public hospitals in Ankara, Konya, and İzmir provinces. The findings revealed that participants' scientific research skills were at a moderate level, while their attitudes towards artificial intelligence were at a high level. Male participants showed significantly higher scientific research skills compared to their female peers, while no gender-based difference was found in attitudes towards artificial intelligence. As seniority increased, scientific research skills also increased significantly; however, attitudes towards artificial intelligence did not change with seniority. Regression analysis showed that attitudes towards artificial intelligence did not significantly predict scientific research skills. Based on these results, it is recommended to strengthen research methodology training in the early stages of specialist training, to investigate the causes of gender inequality using qualitative methods, to integrate artificial intelligence tools into the curriculum, and to expand the study to include a wider sample and different disciplines.

References

Abou Hashish, E. A., & Alnajjar, H. (2024). Digital proficiency: Assessing knowledge, attitudes, and skills in digital transformation, health literacy, and artificial intelligence among university nursing students. BMC Medical Education, 24(1), 508. https://doi.org/10.1186/s12909-024-05482-3

Allam, R. M., Abdelfatah, D., Khalil, M. I. M., Elsaieed, M. M., & El Desouky, E. D. (2024). Medical students and house officers' perception, attitude and potential barriers towards artificial intelligence in Egypt, cross sectional survey. BMC Medical Education, 24(1), 1244. https://doi.org/10.1186/s12909-024-06201-8

AlZaabi, A., & Masters, K. (2025). Assessing medical students' readiness for artificial intelligence after pre-clinical training. BMC Medical Education, 25(1), 824. https://doi.org/10.1186/s12909-025-07008-x

AlZaabi, A., AlMaskari, S., & AalAbdulsalam, A. (2023). Are physicians and medical students ready for artificial intelligence applications in healthcare? Digital Health, 9, 20552076231152167. https://doi.org/10.1177/20552076231152167

Andersson, J. (2025). Using Constructivist Grounded Theory to Understand Why Female Secondary Students Engage in Scientific Research. American Journal of Qualitative Research, 9(4), 191-219. https://doi.org/10.29333/ajqr/17134

Aneese, A. M., Nasr, J. A., & Halalau, A. (2019). A prospective mixed-methods study evaluating the integration of an evidence based medicine curriculum into an internal medicine residency program. Advances in Medical Education and Practice, 10, 533-546. https://doi.org/10.2147/AMEP.S203334

Ang, C.-S. (2025). Developing AI literacy in healthcare education: Bridging the gap in competency assessment. Discover Education, 4(1), 372. https://doi.org/10.1007/s44217-025-00812-z

Baumgartner, M., Sauer, C., Blagec, K., & Dorffner, G. (2022). Digital health understanding and preparedness of medical students: A cross-sectional study. Medical Education Online, 27(1), 2114851. https://doi.org/10.1080/10872981.2022.2114851

Carter, A. E., Anderson, T. S., Rodriguez, K. L., Hruska, K. L., Zimmer, S. M., Spagnoletti, C. L., Morris, A., Kapoor, W. N., & Fine, M. J. (2019). A program to support scholarship during internal medicine residency training: Impact on academic productivity and resident experiences. Teaching and Learning in Medicine, 31(5), 552-565. https://doi.org/10.1080/10401334.2019.1604355

Charow, R., Jeyakumar, T., Younus, S., Dolatabadi, E., Salhia, M., Al-Mouaswas, D., Anderson, M., Balakumar, S., Clare, M., Dhalla, A., Gillan, C., Haghzare, S., Jackson, E., Lalani, N., Mattson, J., Peteanu, W., Tripp, T., Waldorf, J., Williams, S., & Wiljer, D. (2021). Artificial intelligence education programs for health care professionals: Scoping review. JMIR Medical Education, 7(4), e31043. https://doi.org/10.2196/31043

Civaner, M. M., Uncu, Y., Bulut, F., Chalil, E. G., & Tatli, A. (2022). Artificial intelligence in medical education: A cross-sectional needs assessment. BMC Medical Education, 22(1), 772. https://doi.org/10.1186/s12909-022-03852-3

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage Publications.

Çalışkan, S. A., Demir, K., & Karaca, O. (2022). Artificial intelligence in medical education curriculum: An e-Delphi study for competencies. PLOS One, 17(7), e0271872. https://doi.org/10.1371/journal.pone.0271872

Fischer, A., Rietveld, A., Teunissen, P., Hoogendoorn, M., & Bakker, P. (2023). What is the future of artificial intelligence in obstetrics? A qualitative study among healthcare professionals. BMJ Open, 13(10), e076017. https://doi.org/10.1136/bmjopen-2023-076017

George, D., & Mallery, P. (2010). SPSS for Windows step by step: A simple guide and reference (10th ed.). Pearson.

Heinrichs, H., Kies, A., Nagel, S. K., & Kiessling, F. (2025). Physicians' attitudes toward artificial intelligence in medicine: Mixed methods survey and interview study. Journal of Medical Internet Research, 27, e74187. https://doi.org/10.2196/74187

Johnson, B. & Christensen, L. (2008). Educational Research: Quantitative, Qualitative, and Mixed Approaches. New York: Sage.

Karasar, N. (2005). Scientific Research Method: Concepts, Principles, Techniques. Ankara: Nobel Publishing.

Kaya, F., Akça, A., & Yıldız, M. (2022). Development and validation of the General Attitude Toward Artificial Intelligence Scale. Journal of Educational Technology & Online Learning, 5(2), 101-114. https://doi.org/10.52163/jetol.1082582

Khairat, S., Marc, D., Crosby, W., & Al Sanousi, A. (2018). Reasons for physicians not adopting clinical decision support systems: Critical analysis. JMIR Medical Informatics, 6(2), e24. https://doi.org/10.2196/medinform.8912

Kouri, A., Yamada, J., Lam Shin Cheung, J., Van De Velde, S., & Gupta, S. (2022). Do providers use computerized clinical decision support systems? A systematic review and meta-regression of clinical decision support uptake. Implementation Science, 17(1), 21. https://doi.org/10.1186/s13012-022-01199-3

Lambert, S. I., Madi, M., Sopka, S., Lenes, A., Stange, H., Buszello, C.-P., & Stephan, A. (2023). An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. npj Digital Medicine, 6(1), 111. https://doi.org/10.1038/s41746-023-00852-5

Lee, J., Wu, A. S., Li, D., & Kulasegaram, K. (Mahan). (2021). Artificial intelligence in undergraduate medical education: A scoping review. Academic Medicine, 96(11S), S62-S70. https://doi.org/10.1097/ACM.0000000000004291

Livinți, R., Gunnesch-Luca, G., & Iliescu, D. (2021). Research self-efficacy: A meta-analysis. Educational Psychologist, 56(3), 215-242. https://doi.org/10.1080/00461520.2021.1886103

Michel, J. J., Flores, E. J., Dutcher, L., Mull, N. K., & Tsou, A. Y. (2021). Translating an evidence-based clinical pathway into shareable CDS: Developing a systematic process using publicly available tools. Journal of the American Medical Informatics Association, 28(1), 52-61. https://doi.org/10.1093/jamia/ocaa257

Mohr, N. M., Stoltze, A. J., Harland, K. K., Van Heukelom, J. N., Hogrefe, C. P., & Ahmed, A. (2015). An evidence-based medicine curriculum implemented in journal club improves resident performance on the Fresno Test. The Journal of Emergency Medicine, 48(2), 222-229. https://doi.org/10.1016/j.jemermed.2014.09.011

Mokhtari, B., Badalzadeh, R., & Ghaffarifar, S. (2024). The next generation of physician-researchers: Undergraduate medical students' and residents' attitudes, challenges, and approaches towards addressing them. BMC Medical Education, 24(1), 1313. https://doi.org/10.1186/s12909-024-06166-8

Mousavi Baigi, S. F., Sarbaz, M., Ghaddaripouri, K., Ghaddaripouri, M., Mousavi, A. S., & Kimiafar, K. (2023). Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review. Health Science Reports, 6(3), e1138. https://doi.org/10.1002/hsr2.1138

Nyinge, B., Matete, R., & William, F. K. (2024). Exploring the relationship between authentic assessment and teaching professional competence acquisition among undergraduate science student-teachers in higher education institutions in Tanzania. International Journal of Current Educational Studies, 3(1), 14-27. https://doi.org/10.46328/ijces.95

Özcan, A., & Polat, S. (2023). Artificial Intelligence and Chat Bots in Academic Research. Journal of Research in Social Sciences and Language, 3(2), 81–90. https://doi.org/10.71514/jssal/2023.111

Paranjape, K., Schinkel, M., Nannan Panday, R., Car, J., & Nanayakkara, P. (2019). Introducing artificial intelligence training in medical education. JMIR Medical Education, 5(2), e16048. https://doi.org/10.2196/16048

Pawelczyk, J., Kraus, M., Eckl, L., Nehrer, S., Aurich, M., Izadpanah, K., Siebenlist, S., & Rupp, M.-C. (2024). Attitude of aspiring orthopaedic surgeons towards artificial intelligence: A multinational cross-sectional survey study. Archives of Orthopaedic and Trauma Surgery, 144(8), 3541-3552. https://doi.org/10.1007/s00402-024-05408-0

Pinto Dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., Maintz, D., & Baeßler, B. (2019). Medical students' attitude towards artificial intelligence: A multicentre survey. European Radiology, 29(4), 1640-1646. https://doi.org/10.1007/s00330-018-5601-1

Pumplun, L., Fecho, M., Wahl, N., Peters, F., & Buxmann, P. (2021). Adoption of machine learning systems for medical diagnostics in clinics: Qualitative interview study. Journal of Medical Internet Research, 23(10), e29301. https://doi.org/10.2196/29301

Rjoop, A., Al-Qudah, M., Alkhasawneh, R., Bataineh, N., Abdaljaleel, M., Rjoub, M. A., Alkhateeb, M., Abdelraheem, M., Al-Omari, S., Bani-Mari, O., Alkabalan, A., Altulaih, S., Rjoub, I., & Alshimi, R. (2025). Awareness and attitude toward artificial intelligence among medical students and pathology trainees: Survey study. JMIR Medical Education, 11, e62669. https://doi.org/10.2196/62669

Schepman, A., & Rodway, P. (2020). Initial teacher education students' attitudes towards artificial intelligence: A mixed-methods study. Journal of Education for Teaching, 46(2), 169-183. https://doi.org/10.1080/02607476.2020.1714699

Sit, C., Srinivasan, R., Amlani, A., Muthuswamy, K., Azam, A., Monzon, L., & Poon, D. S. (2020). Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: A multicentre survey. Insights into Imaging, 11(1), 14. https://doi.org/10.1186/s13244-019-0830-7

St John, A., Cooper, L., & Kavic, S. M. (2024). The role of artificial intelligence in surgery: What do general surgery residents think? The American Surgeon, 90(4), 541-549. https://doi.org/10.1177/00031348231209524

Stewart, J., Lu, J., Gahungu, N., Goudie, A., Fegan, P. G., Bennamoun, M., Sprivulis, P., & Dwivedi, G. (2023). Western Australian medical students' attitudes towards artificial intelligence in healthcare. PLOS ONE, 18(8), e0290642. https://doi.org/10.1371/journal.pone.0290642

Tolentino, R., Baradaran, A., Gore, G., Pluye, P., & Abbasgholizadeh-Rahimi, S. (2024). Curriculum frameworks and educational programs in AI for medical students, residents, and practicing physicians: Scoping review. JMIR Medical Education, 10, e54793. https://doi.org/10.2196/54793

Velioğlu, E., & Özdemir, A. (2023). Levels of Teacher’s Scientific Research Skills: Mixed Method Research. Marmara University Atatürk Education Faculty Journal of Educational Sciences, 58(58), 186-215. https://doi.org/10.15285/maruaebd.1125661

Vosoughmatin, M. (2025). Investigation of Digital Competencies and Artificial Intelligence Literacy of Special Education Students. International Journal of Modern Education Studies, 9(2), 501-525. https://doi.org/10.51383/ijonmes.2025.430

Wang, H., Ye, Z., Zhang, P., Cui, X., Chen, M., Wu, A., Riggs, S. L., Xue, P., & Qiao, Y. (2024). Chinese colposcopists' attitudes toward the colposcopic artificial intelligence auxiliary diagnostic system (CAIADS): A nation-wide, multi-center survey. Digital Health, 10, 20552076241279952. https://doi.org/10.1177/20552076241279952

Woo, H., Kim, N., Lee, J., Chae, K., & Mathew, A. (2024). Research self-efficacy and research productivity of doctoral students in counselling programmes: Research training environment as a moderator. British Journal of Guidance & Counselling, 52(6), 1071-1080. https://doi.org/10.1080/03069885.2023.2297892

Wood, W., McCollum, J., Kukreja, P., Vetter, I. L., Morgan, C. J., Hossein Zadeh Maleki, A., & Riesenberg, L. A. (2018). Graduate medical education scholarly activities initiatives: A systematic review and meta-analysis. BMC Medical Education, 18(1), 318. https://doi.org/10.1186/s12909-018-1407-8

Ziapour, A., Darabi, F., Janjani, P., Amani, M. A., Yıldırım, M., & Motevaseli, S. (2025). Factors affecting medical artificial intelligence (AI) readiness among medical students: Taking stock and looking forward. BMC Medical Education, 25(1), 264. https://doi.org/10.1186/s12909-025-06852-1

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Published

2026-03-13

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Investigating Research Skills and Artificial Intelligence Attitudes Among Residents in Surgical Medicine Departments . (2026). International Journal of Technology in Education and Science, 10(2), 398-416. https://doi.org/10.46328/ijtes.7805