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Impact of AI Health Chatbot Education on Appropriate Health Information-Seeking among Young Adults in Nigeria

Abstract

Artificial intelligence health chatbots are increasingly being used as digital tools for obtaining health-related information, answering health questions, providing general health guidance, and directing users toward appropriate health resources. Among young adults in Nigeria, the growing use of smartphones, internet services, social media platforms, and artificial intelligence applications may increase access to AI-powered health information. However, inappropriate reliance on AI-generated health information may expose users to inaccurate responses, misinterpretation of symptoms, delayed professional consultation, self-diagnosis, or inappropriate self-treatment. AI health chatbot education provides an opportunity to improve young adults' understanding of the appropriate use and limitations of AI-powered health information tools, including the importance of verifying AI-generated information, recognizing situations that require professional medical attention, protecting personal health information, and avoiding the use of chatbots as substitutes for qualified healthcare professionals. Against this background, this study investigates the impact of AI health chatbot education on appropriate health information-seeking among young adults in Nigeria. The study will be anchored on the Health Belief Model, Technology Acceptance Model, and Information Literacy Theory. The Health Belief Model explains how young adults' perceptions of health risks, perceived benefits of seeking reliable health information, perceived barriers, and cues to action may influence their health information-seeking behaviours. The Technology Acceptance Model explains how perceived usefulness, perceived ease of use, attitudes, and acceptance of AI health chatbots may influence their willingness to use such technologies for health-information purposes. Information Literacy Theory emphasizes the ability to identify information needs, locate relevant information, critically evaluate information sources, verify information, and use reliable information appropriately. Collectively, these theoretical perspectives provide a suitable framework for explaining how AI health chatbot education may influence appropriate health information-seeking among young adults in Nigeria. The study will adopt a quantitative quasi-experimental or analytical cross-sectional research design. The study population will comprise young adults aged approximately 18–35 years residing in selected urban, semi-urban, and rural communities across Nigeria. A multistage sampling technique will be used to select geopolitical zones, states, local government areas, communities, institutions, workplaces, and eligible young adults. AI health chatbot education will be assessed using indicators such as exposure to educational sessions, frequency and duration of education, knowledge of AI health chatbots, understanding of appropriate chatbot use, awareness of limitations of AI-generated health information, ability to formulate appropriate health questions, verification of chatbot responses, recognition of inaccurate or unsafe information, understanding of when professional medical consultation is necessary, privacy and personal-health-information protection, and appropriate use of emergency health services. Appropriate health information-seeking will be assessed using indicators such as use of credible health-information sources, verification of AI-generated health information, consultation with healthcare professionals when necessary, use of official health websites and health agencies, avoidance of self-diagnosis based solely on chatbot responses, avoidance of inappropriate self-medication, recognition of urgent symptoms requiring professional attention, responsible sharing of health information, and appropriate use of AI chatbots as supplementary rather than substitute sources of health information. Data will be collected using structured questionnaires, digital health literacy assessment tools, AI health chatbot-use scenarios, health information-seeking assessment instruments, and relevant educational programme records. Descriptive statistics will be used to summarize participants' characteristics, digital-health experience, AI chatbot use, health-information sources, and information-seeking practices. Inferential statistical techniques, including chi-square tests, t-tests, correlation analysis, and logistic or multiple regression analysis where appropriate, will be used to determine the impact of AI health chatbot education on appropriate health information-seeking. Where a quasi-experimental design is adopted, appropriate health information-seeking scores before and after the educational intervention may be compared with those of a comparison group to determine changes associated with the intervention. Diagnostic tests will also be conducted to assess the reliability, validity, and robustness of the findings. The study is expected to find that AI health chatbot education has a significant positive impact on appropriate health information-seeking among young adults in Nigeria. Young adults exposed to structured AI health chatbot education are expected to demonstrate better understanding of the appropriate use and limitations of AI-generated health information than those without comparable education. Education may improve users' ability to formulate relevant health questions, evaluate chatbot responses, verify information through credible sources, recognize potentially inaccurate or misleading information, and seek professional healthcare when necessary. It may also reduce inappropriate reliance on AI chatbots for self-diagnosis, emergency decisions, or medication-related decisions without professional guidance. Practical education may further improve awareness of privacy risks and encourage users to avoid unnecessarily entering sensitive personal health information into AI systems. However, limited digital literacy, high trust in technology, accessibility of professional healthcare services, cost of healthcare, misinformation, inadequate internet access, and overreliance on AI-generated responses may influence the effectiveness of education. The study therefore expects practical, continuous, digitally oriented, and health-focused AI chatbot education to contribute significantly to improved and more appropriate health information-seeking among young adults in Nigeria. The study is expected to contribute to the literature on artificial intelligence in healthcare, AI health chatbots, digital health literacy, health information-seeking, health education, health communication, health technology, health promotion, and public health in Nigeria. The findings will provide useful information to the Federal Ministry of Health and Social Welfare, Federal Ministry of Communications, Innovation and Digital Economy, National Primary Health Care Development Agency, universities, healthcare professionals, digital health organizations, technology developers, public health practitioners, development partners, and policymakers regarding responsible use of AI-powered health-information technologies. The study will also provide evidence-based recommendations for developing AI health chatbot literacy programmes, promoting verification of AI-generated health information, strengthening digital health education, improving awareness of AI limitations, protecting users' health information and privacy, encouraging appropriate professional healthcare consultation, and integrating responsible AI-health education into broader digital health and health-promotion programmes for young adults across Nigeria.

Keywords: AI health chatbot education, artificial intelligence, health information-seeking, young adults, digital health literacy, health chatbots, health education, health information, health technology, Nigeria, public health.