Modupe Aduke Aina, Mary Ayodeji Gbenga-Epebinu, Rebecca Oluwafunke Olofinbiyi, Oluwakemi Christie Ogidan, Tosin O. Ayedun
{"title":"尼日利亚Ekiti州立大学大学生对医疗聊天机器人的认知和接受度","authors":"Modupe Aduke Aina, Mary Ayodeji Gbenga-Epebinu, Rebecca Oluwafunke Olofinbiyi, Oluwakemi Christie Ogidan, Tosin O. Ayedun","doi":"10.37745/bje.2013/vol11n11114","DOIUrl":null,"url":null,"abstract":"This study explores the perceptions and acceptance of medical chatbots among undergraduate students at Ekiti State University, Ado-Ekiti, Nigeria. A medical chatbot is an artificially intelligent conversational agent that simulates human-like communication, catering to user inquiries and generating logical responses. These chatbots leverage natural language processing and machine learning to engage in dynamic interactions, retrieve relevant information, and adapt to new data. This research investigates two primary aspects: the perception of undergraduate students towards the use of medical chatbots and the level of acceptance of these chatbots among the same demographic. The study employs a descriptive cross-sectional survey design, involving a sample size of 300 undergraduate students, determined using Taro Yamane's method. The data collection process includes a semi-structured questionnaire, validated by experts in Tests and Measurement and Public Health. The collected data are analyzed using SPSS version 28. The findings reveal an equitable gender distribution among participants, with a slightly higher representation of females. Additionally, a substantial proportion of respondents fall within the 18-25 age bracket, with a significant presence of undergraduates below 18 years old. The study indicates positive perceptions of medical chatbots among undergraduate students, suggesting a favorable view towards their adoption. While the majority of participants exhibit acceptance of medical chatbots, there is skepticism about the precision and reliability of healthcare suggestions provided by these platforms. In conclusion, this study sheds light on the positive perceptions and acceptance of medical chatbots among undergraduate students in Ekiti State University. The findings suggest a potential for integrating this technology into healthcare, education, and research endeavors, while acknowledging the need for further investigation into the underlying factors influencing these perceptions. As the healthcare landscape evolves, chatbots can potentially offer valuable contributions to enhancing health services, especially in scenarios where in-person visits are unnecessary. However, continuous research is essential to ensure their accuracy, trustworthiness, and effectiveness across diverse demographic groups","PeriodicalId":46054,"journal":{"name":"British Journal of Special Education","volume":"23 1","pages":"0"},"PeriodicalIF":0.9000,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Perception and Acceptance of Medical Chatbot Among Undergraduates in Ekiti State University, Nigeria\",\"authors\":\"Modupe Aduke Aina, Mary Ayodeji Gbenga-Epebinu, Rebecca Oluwafunke Olofinbiyi, Oluwakemi Christie Ogidan, Tosin O. Ayedun\",\"doi\":\"10.37745/bje.2013/vol11n11114\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study explores the perceptions and acceptance of medical chatbots among undergraduate students at Ekiti State University, Ado-Ekiti, Nigeria. A medical chatbot is an artificially intelligent conversational agent that simulates human-like communication, catering to user inquiries and generating logical responses. These chatbots leverage natural language processing and machine learning to engage in dynamic interactions, retrieve relevant information, and adapt to new data. This research investigates two primary aspects: the perception of undergraduate students towards the use of medical chatbots and the level of acceptance of these chatbots among the same demographic. The study employs a descriptive cross-sectional survey design, involving a sample size of 300 undergraduate students, determined using Taro Yamane's method. The data collection process includes a semi-structured questionnaire, validated by experts in Tests and Measurement and Public Health. The collected data are analyzed using SPSS version 28. The findings reveal an equitable gender distribution among participants, with a slightly higher representation of females. Additionally, a substantial proportion of respondents fall within the 18-25 age bracket, with a significant presence of undergraduates below 18 years old. The study indicates positive perceptions of medical chatbots among undergraduate students, suggesting a favorable view towards their adoption. While the majority of participants exhibit acceptance of medical chatbots, there is skepticism about the precision and reliability of healthcare suggestions provided by these platforms. In conclusion, this study sheds light on the positive perceptions and acceptance of medical chatbots among undergraduate students in Ekiti State University. The findings suggest a potential for integrating this technology into healthcare, education, and research endeavors, while acknowledging the need for further investigation into the underlying factors influencing these perceptions. 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Perception and Acceptance of Medical Chatbot Among Undergraduates in Ekiti State University, Nigeria
This study explores the perceptions and acceptance of medical chatbots among undergraduate students at Ekiti State University, Ado-Ekiti, Nigeria. A medical chatbot is an artificially intelligent conversational agent that simulates human-like communication, catering to user inquiries and generating logical responses. These chatbots leverage natural language processing and machine learning to engage in dynamic interactions, retrieve relevant information, and adapt to new data. This research investigates two primary aspects: the perception of undergraduate students towards the use of medical chatbots and the level of acceptance of these chatbots among the same demographic. The study employs a descriptive cross-sectional survey design, involving a sample size of 300 undergraduate students, determined using Taro Yamane's method. The data collection process includes a semi-structured questionnaire, validated by experts in Tests and Measurement and Public Health. The collected data are analyzed using SPSS version 28. The findings reveal an equitable gender distribution among participants, with a slightly higher representation of females. Additionally, a substantial proportion of respondents fall within the 18-25 age bracket, with a significant presence of undergraduates below 18 years old. The study indicates positive perceptions of medical chatbots among undergraduate students, suggesting a favorable view towards their adoption. While the majority of participants exhibit acceptance of medical chatbots, there is skepticism about the precision and reliability of healthcare suggestions provided by these platforms. In conclusion, this study sheds light on the positive perceptions and acceptance of medical chatbots among undergraduate students in Ekiti State University. The findings suggest a potential for integrating this technology into healthcare, education, and research endeavors, while acknowledging the need for further investigation into the underlying factors influencing these perceptions. As the healthcare landscape evolves, chatbots can potentially offer valuable contributions to enhancing health services, especially in scenarios where in-person visits are unnecessary. However, continuous research is essential to ensure their accuracy, trustworthiness, and effectiveness across diverse demographic groups
期刊介绍:
This well-established and respected journal covers the whole range of learning difficulties relating to children in mainstream and special schools. It is widely read by nasen members as well as other practitioners, administrators advisers, teacher educators and researchers in the UK and overseas. The British Journal of Special Education is concerned with a wide range of special educational needs, and covers all levels of education pre-school, school, and post-school.