Intention to use an interactive Artificial Intelligence (AI) chatbot for learning self-medication consultation among pharmacy students
DOI:
https://doi.org/10.46542/pe.2026.261.3148Keywords:
Artificial intelligence (AI), Chatbot, Pharmacy student, Self-medicationAbstract
Background: Whilst Artificial Intelligence (AI) holds great potential as a significant component in pharmacy education, research on students' intentions as the relevant key stakeholders is lacking. This study describes Indonesian pharmacy students' intention to use a newly developed AI chatbot for Self-Medication Consultation Learning (SMCL-chatbot), and the determinant factors.
Methods: A questionnaire adapted from the Unified Theory of Acceptance and Use of Technology (UTAUT2) model was used to assess the students’ intentions. A tryout for SMCL-chatbot was conducted among pharmacy students at the University of Surabaya’s 2024 “Responding to Symptom” classes (n = 237). After interacting with the SMCL-chatbot, the students filled out the questionnaire.
Results: Of the 237 students, 201 participated from which 90% expressed a positive intention to use the SMCL-chatbot. More than 80% had positive perceptions of the UTAUT2 constructs, including: performance expectancy, effort expectancy, facilitating condition, and hedonistic motivation. Performance expectancy (OR: 16.5, 95% CI: 1.42-192.42, p: 0.025) and hedonistic motivation (OR: 19.4, 95%CI 2.60-144.63, p: 0.004) were significantly related to students’ intentions.
Conclusion:Students showed positive intentions to use the SMCL-chatbot, indicating their readiness to adopt the technology for learning self-medication consultations. Further research is required to demonstrate the effectiveness of improving students’ consultation skills and to perform a cost-benefit analysis.
References
Acosta-Enriquez, B. G., Farroñan, E. V. R, Zapata, L. I. V, Garcia, F. S.M, Rabanal-León, H. C., Angaspilco, J. E. M., & Bocanegra, J. C. S. (2024). Acceptance of artificial intelligence in university contexts: A conceptual analysis based on UTAUT2 theory. Heliyon, 10(19), e38315. https://doi.org/10.1016/j.heliyon.2024.e38315
Adamopoulou, E., & Moussiades, L. (2020). An overview of chatbot technology. In: Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds), Artificial intelligence applications andiInnovations. AIAI 2020. IFIP Advances in Information and Communication Technology, vol 584. Springer, Cham. https://doi.org/10.1007/978-3-030-49186-4_31
Alastalo, N., Siitonen, P., Jyrkkä, J., & Hämeen-Anttila, K. (2023). The quality of non-prescription medicine counselling in Finnish pharmacies – a simulated patient study. Exploratory Research in Clinical and Social Pharmacy, 11, 100304. http://dx.doi.org/10.1016/j.rcsop.2023.100304
Ananyi, S. A., & Somieari-Pepple, E. (2023). Cost-benefit analysis of artificial intelligence integration in education management: Leadership perspectives. International Journal of Economics, Environmental Development and Society, 4(3), 353‒370. https://www.ijeeds.com.ng/assets/vol.%2C-4(3)-ananyi---somieari-pepple.pdf
Asosiasi Pendidikan Tinggi Farmasi Indonesia. (2013). Academic paper of graduate competency standards and pharmacy education curriculum standards. www.aptfi.or.id/dokumen/2016-01-01NAKompetensi&KurAPTFI2013.pdf
Aziz, M. H. A., Rowe, C., Southwood, R., Nogid, A., Berman, S., & Gustafson, K. (2024). A scoping review of artificial intelligence within pharmacy education. American Journal of Pharmaceutical Education, 88, 100615. https://doi.org/10.1016/j.ajpe.2023.100615.
Badan Pusat Statistik. (2023). Statistical yearbook of Indonesia 2023. https://www.bps.go.id/publication/2023/02/28/18018f9896f09f03580a614b/statistik-indonesia-2023.html
Baigi, S. F. M., 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, e1138. https://doi.org/10.1002/hsr2.1138
Baker, C., Bowers, R., & Ghassemi, E. (2024). Student perceptions of generative artificial intelligence in didactic patient presentations. Pharmacy Education, 24(1), 590‒597. https://doi.org/10.46542/pe.2024.241.590597
Beshir, S. A., Mohamed, A. P., Soorya, A., Sir Loon Goh, S., Moussa El-Labadd, E., Hussain, N., & Said, A.S. (2022). Virtual patient simulation in pharmacy education: A systematic review. Pharmacy Education, 22(1). https://doi.org/10.46542/pe.2022.221.954970
Boillat, T., Nawaz, F. A., & Rivas, H. (2022). Readiness to embrace artificial intelligence among medical doctors and students: Questionnaire-based study. JMIR Medical Education, 12(8(2)), e34973. https://doi.org/10.2196/34973.
Brandes, G. I. G., D’Ippolito, G., Azzolini, A. G., & Meirelles, G. (2020). Impact of artificial intelligence on the choice of radiology as a specialty by medical students from the city of São Paulo. Radiologia Brasileira, 53(3), 167‒170. http://dx.doi.org/10.1590/0100-3984.2019.0101
Brata, C., Fisher, C,, Marjadi, B., Schneider, C. R., & Clifford, R.M. (2016). Factors influencing the current practice of self-medication consultations in Eastern Indonesian community pharmacies: A qualitative study. BMC Health Service Research, 16, 179. https://doi.org/10.1186/s12913-016-1425-3
Brata, C., Halim, S. V., Setiawan, E., Presley, B., Wibowo, Y. I., & Schneider, C.R. (2021). The competency of Indonesian pharmacy students in handling a self-medication request for a cough: A simulated patient study. Pharmacy Practice, 19(2), 2269. https://doi.org/10.18549/PharmPract.2021.2.2269
Brata, C., Schneider, C. R., Marjadi, B., & Clifford, R. M. (2019). The provision of advice by pharmacy staff in eastern Indonesian community pharmacies. Pharmacy Practice, 17(2), 1452. https://doi.org/10.18549/PharmPract.2019.2.1452
Busch, F., Hoffmann, L., Truhn, D., Palaian, S., Alomar, M., Shpati, K., Makowski, M. R., Bressem, K. K., Adams, L. C. (2024). International pharmacy students' perceptions towards artificial intelligence in medicine-A multinational, multicentre cross-sectional study. British Journal of Clinical Pharmacology, 90(3), 649‒661. https://doi.org/10.1111/bcp.15911
Butow, P., & Hoque, E. (2020). Using artificial intelligence to analyse and teach communication in healthcare. The Breast, 50, 49‒55. https://doi.org/10.1016/j.breast.2020.01.008
Chakraborty, C., Pal, S., Bhattacharya, M., Dash, S., & Lee, S.S. (2023). Overview of Chatbots with special emphasis on artificial intelligence-enabled ChatGPT in medical science. Frontiers in Artificial Intelligence, 6, 1237704. https://doi.org/10.3389/frai.2023.1237704
Cokro, F., Atmanda, P. F. K., Sagala, R. J., Arrang, S. T., Notario , D., Rukmini, E., & Aparasu, R. (2021). Pharmacy education in Indonesia. Pharmacy Education, 21, 432–442. https://doi.org/10.46542/pe.2021.211.432442
Elanjeran, R., Ramkumar, A., & Mahmood, L. S. (2024). Digitalisation of the simulation landscape – Novel solutions for simulation in low‑resource settings. Indian Journal of Anaesthesia, 68, 71‒77. https://doi.org/10.4103/ija.ija_1246_23
Farahani, I., Farahani, S., Deters, M. A., Schwender, H., & Laeer, S. (2021). Training pharmacy students in self-medication counseling using an Objective Structured Clinical Examination–based approach. Journal of Medical Education and Curricular Development, 8, 1‒9. https://doi.org/10.1177/23821205211016484
Field, A. (2009). Discovering statistics using IBM SPSS statistics (3rd ed.). SAGE Publications.
Ghasemyani, S., Benis, M. R., Hosseinifard, H., Jahangiri, R., Aryankhesal, A., Shabaninejad, H., Rafiei, S., Ghashghaee, A. (2022). Global, WHO Regional, and continental prevalence of self-medication from 2000 to 2018: A systematic review and meta-analysis. Annals of Public Health, 1(637). https://doi.org/10.55085/aph.2022.585
Grassini, S., Aasen, M. L., & Møgelvang, A. (2024). Understanding university students’ acceptance of ChatGPT: Insights from the UTAUT2 model. Applied Artificial Intelligence, 38(1), 2371168. https://doi.org/10.1080/08839514.2024.2371168
Hastings, J. K., Flowers, S. K., Pace, A. C., & Spadaro, D. (2010). An Objective Standardised Clinical Examination (OSCE) in an advanced nonprescription medicines course. American Journal of Pharmaceutical Education, 74, Article 98. https://doi.org/10.5688/aj740698
Holderried, F., Stegemann-Philipps, C., Herschbach, L., Moldt, J. A., Nevins, A., Griewatz, J., Holderried, M., Herrman-Werner, A., Festl-Wietek, T., Mahling, M. (2024a). A Generative Pretrained Transformer (GPT)-Powered Chatbot as a simulated patient to practice history taking: Prospective, mixed methods study. JMIR Medical Education, 16(10), e53961 https://doi.org/10.2196/53961
Holderried, F., Stegemann-Philipps, C., Herrmann-Werner, A., Festl-Wietek, T., Holderried, M., Eickhoff, C., & Mahling, M. (2024b). A language model-powered simulated patient with automated feedback for history taking: Prospective study. JMIR Medical Education, 16(10), e59213. https://doi.org/10.2196/59213.
Huynh, C. B. D., Nguyen, A. H., Nguyen, T. H. T., & Le, A. H. (2023). Factors affecting students’ intention to use mobile learning at universities: An empirical study. Journal of System and Management Sciences, 13(1), 281‒304. https://doi.org/10.33168/JSMS.2023.0116
Indriani, N. (2023). The ability of pharmacy students in providing self-medication services in simulated cases of cough due to asthma exacerbation and acute diarrhoea in children. [Thesis, University of Surabaya].
International Pharmaceutical Federation. (2020). FIP pharmacy education in sub-Saharan Africa. The FIP-UNESCO UNITWIN Programme: A decade of education partnership across Africa. https://www.fip.org/file/4812
International Pharmaceutical Federation and World Self Medication Industry. (1999). Joint statement by The International Pharmaceutical Federation and The World Self-Medication Industry: Responsible self-medication. https://www.fip.org/file/1484#:~:text=DEFINITION,medicines%20available%20for%20self%2Dmedication.
Iwasawa, M., Kobayashi, M., & Otori, K. (2023). Knowledge and attitudes of pharmacy students towards artificial intelligence and the ChatGPT. Pharmacy Education, 23(1), 665‒675. https://doi.org/10.46542/pe.2023.231.665675
Kementrian Kesehatan Republik Indonesia. (2023). Decree of the Minister of Health of the Republic of Indonesia Number HK.01.07/MENKES/13/2023 concerning Professional Standards for Pharmacists. Indonesia Ministry of Health. https://farmalkes.kemkes.go.id/unduh/kepmenkes-13-2023/
Koduah, A., Kretchy, I., Sekyi-Brown, R., Asiedu-Danso, M., Ohene-Agyei, T., & Duwiejua, M. (2020). Education of pharmacists in Ghana: evolving curriculum, context and practice in the journey from dispensing certificate to doctor of pharmacy certificate. BMC Medical Education, 20, 475. https://doi.org/10.1186/s12909-020-02393-x
Labrague, L. J., & Sabei, S. A. (2025). Integration of AI-Powered chatbots in nursing education: A scoping review of Their utilisation, outcomes, and challenges. Teaching and Learning in Nursing, 20, e285‒e293. https://doi.org/10.1016/j.teln.2024.11.010
Loh, P., Lee, J.W., Karuppannan, M., & Chua, S.S. (2023). Practice of pharmaceutical care by community pharmacists in response to self-medication request for a cough: A simulated client study. BMC Health Service Research, 23, 657. https://doi.org/10.1186/s12913-023-09642-x
Lou, C., Kang, H., & Tse, C. H. (2021). Bots vs. humans: how schema congruity, contingency-based interactivity, and sympathy influence consumer perceptions and patronage intentions. International Journal of Advertising, 41(4), 655‒684. https://doi.org/10.1080/02650487.2021.1951510
Lupa-Wojcik, I. (2024). Students' willingness to pay for access to ChatGPT. European Research Studies Journal, 27(3), 730‒745. https://doi.org/10.35808/ersj/3462
Makhlouf, E., Alenezi, A., & Shokr, E. A. (2024). Effectiveness of designing a knowledge-based artificial intelligence chatbot system into a nursing training program: A quasi-experimental design. Nurse Education Today, 137, 106159. https://doi.org/10.1016/j.nedt.2024.106159.
Mizranita, V., Hughes, J. D., Sunderland, B., & Sim, T. F. (2023). Pharmacists and pharmacy technicians’ perceptions of scopes of practice employing agency theory in the management of minor ailments in central Indonesian community pharmacies: A qualitative study. Pharmacy, 11, 132. https://doi.org/10.3390/pharmacy11050132
Montemayor, C., Halpern, J., & Fairweather, A. (2022). In principle obstacles for empathic AI: why we can't replace human empathy in healthcare. AI & Society, 37(4), 1353‒1359. https://doi.org/10.1007/s00146-021-01230-z.
Mortlock, R., & Lucas, C. (2024). Generative artificial intelligence (Gen-AI) in pharmacy education: Utilisation and implications for academic integrity: A scoping review. Exploratory Research in Clinical and Social Pharmacy, 15, 100481. https://doi.org/10.1016/j.rcsop.2024.100481.
Musfiroh, I., Holik, H. A., Indradi, R. B., & Sriwidodo. (2021). Active and non-active stations as an adaptive method for Objective Structured and Clinical Examination during the COVID-19 pandemic. Pharmacy Education, 21(1), 306‒309. https://doi.org/10.46542/pe.2021.211.306309
Nonyel, N., & Ogbonna, B. O. (2022). Capacity-building and collaborative curriculum development: A transition from BPharm to PharmD degree at Nnamdi Azikiwe university in Nigeria. Pharmacy Education, 22(4), 131–142. https://doi.org/10.46542/pe.2022.224.131142
Oritsegbemi, O. (2023). Human Intelligence versus AI: Implications for Emotional Aspects of Human Communication. Journal of Advanced Research in Social Sciences, 6(2), 76‒85. https://doi.org/10.33422/jarss.v6i2.1005
Paraidathathu, T., Ploylearmsang C., & Olson, P. S. (2022). The development and trends in pharmacy education across ASEAN countries. Isan Journal of Pharmaceutical Sciences, 18(1), 1‒20. https://doi.org/10.14456/ijps.2022.1
Peraman, R., Palaian, S., & Izham, M. I. M. (2017). Are doctor of pharmacy curricula in developing countries adequate to train graduates to provide pharmaceutical care? Archives of Pharmacy Practice, 8, 35‒38. https://doi.org/10.4103/2045-080X.199620
Petrovi´c, A. T., Pavlovi´c, N., Stilinovi´c, N., Lalovi´c, N., Kusturica, M. P., Dugandžija, T., Zaklan, D., Horvat, O. (2022). Self-medication perceptions and practice of medical and pharmacy students in Serbia. International Journal of Environmental Research and Public Health, 19, 1193. https://doi.org/10.3390/ijerph19031193
Risana, V. U., Shirin, A., Purayil, R. N., Mathew, S. R., Soman, S., Chandran, C. S., & Kiron, S. S. (2024). Artificial intelligence and pharmacy education: A survey to assess the knowledge, application, and perspective of B. Pharm. students from India. Discover Education, 3, 213. https://doi.org/10.1007/s44217-024-00297-2
Roseno, M., & Widyastiwi. (2023). Assessing quality of self-medication services in pharmacies in Bandung, West Java, Indonesia using a mystery customer approach. Indonesian Journal of Pharmacy, 34(2), 312‒323. https://doi.org/10.22146/ijp.4145
Safitri, I. (2024). Should likert data be transformed using summated rating scale? A confirmatory factor analysis study on the continuous learning. Jurnal EduScience, 11(3), 790‒801. https://jurnal.ulb.ac.id/index.php/eduscience/article/viewFile/6412/4520
Sinopoulou, V., & Rutter, P. (2019). Approaches to over-the-counter medications teaching in pharmacy education: A global perspective. Pharmacy Education, 19(1), 34‒39. https://pharmacyeducation.fip.org/pharmacyeducation/article/view/654
Srinivasan, M., Venugopal, A., Venkatesan, L., & Kumar, R. (2024). Navigating the pedagogical landscape: Exploring the implications of AI and Chatbots in nursing education. JMIR Nursing, 13(7), e52105. https://doi.org/10.2196/52105
Sullivan, G. M., & Artino, A. R. (2013). Analyzing and interpreting data from Likert-type scales. Journal of Graduate Medical Education, 5(4), 541‒542. https://doi.org/10.4300/JGME-5-4-18
Thang, D. X. (2013). An investigation of non-prescription medicine supply in community pharmacies in Hanoi, Vietnam. [PhD Thesis, University of Nottingham]. https://eprints.nottingham.ac.uk/13804/1/Thang%2C_THESIS_FINAL_version.pdf
Ursachi, G., Horodnic, I. A., & Zait, A. (2015). How reliable are measurement scales? External factors with indirect influence on reliability estimators. Procedia Economics and Finance, 20, 679‒686. https://doi.org/10.1016/S2212-5671(15)00123-9
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425.org/278. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36, 157‒178. https://doi.org/10.2307/41410412
World Health Organisation. (2000). Guidelines for the regulatory assessment of medicinal products for use in self-medication. https://iris.who.int/bitstream/handle/10665/66154/WHO_EDM_QSM_00.1_eng.pdf
Wu, H., & Leung, S. O. (2017). Can Likert scales be treated as interval scales?—A simulation study. Journal of Social Service Research, 43(4), 527–532. https://doi.org/10.1080/01488376.2017.1329775
Xue, L., Rashid, A. M., & Ouyang, S. (2024). The Unified Theory of Acceptance and Use of Technology (UTAUT) in higher education: A systematic review. Sage Open, 14(1). https://doi.org/10.1177/21582440241229570
Yee, M. L. S., & Abdullah, M. S. (2021). A review of UTAUT and extended model as a conceptual framework in education research. Jurnal Pendidikan Sains Dan Matematik Malaysia, 11, 1‒20. https://doi.org/10.37134/jpsmm.vol11.sp.1.2021
Yusoff, M. S. B. (2019). ABC of content validation and content validity index calculation. Education in Medicine Journal, 11(2), 49‒54. https://doi.org/10.21315/eimj2019.11.2.6
Zacharis, G., & Nikolopoulou, K. (2022). Factors predicting University students' behavioral intention to use eLearning platforms in the post-pandemic normal: a UTAUT2 approach with 'Learning Value'. Education and Information Technologies (Dordr), 27(9), 12065‒12082. https://doi.org/10.1007/s10639-022-11116-2.
Zidoun, Y., & Mardi, A. E. (2024). Artificial Intelligence (AI)-Based simulators versus simulated patients in undergraduate programs: A protocol for a randomised controlled trial. BMC Medical Education, 24, 1260. https://doi.org/10.1186/s12909-024-06236-x
