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Impact of AI-Generated Health Content Education on Recognition of Unreliable Health Information among Young Adults in Nigeria

Abstract

The increasing use of artificial intelligence (AI) in generating health-related content has created new opportunities for accessing health information while also introducing challenges related to misinformation, inaccurate medical claims, fabricated references, outdated information, and misleading health advice. Young adults in Nigeria increasingly obtain health information through social media, search engines, chatbots, and other digital platforms where AI-generated content may be encountered without clear identification of its source or level of reliability. Limited awareness of how AI-generated health content is produced, combined with inadequate digital health literacy and difficulty evaluating health claims, may increase the risk of accepting unreliable information. AI-generated health content education provides an opportunity to improve young adults' understanding of AI-generated health information and strengthen their ability to identify potentially unreliable health content. Against this background, this study investigates the impact of AI-generated health content education on recognition of unreliable health information among young adults in Nigeria. The study will be anchored on the Health Belief Model, Technology Acceptance Model, and Health Literacy Theory. The Health Belief Model explains how young adults' perceptions of susceptibility to health misinformation, perceived severity of potential consequences, perceived benefits of verifying health information, perceived barriers, and cues to action may influence their information-evaluation practices. The Technology Acceptance Model explains how perceived usefulness and perceived ease of use of AI-based health-information technologies may influence engagement with and evaluation of AI-generated health content. Health Literacy Theory emphasizes the ability of individuals to obtain, understand, critically evaluate, and appropriately use health information when making health-related decisions. Collectively, these theoretical perspectives provide a suitable framework for explaining how AI-generated health content education may influence recognition of unreliable health information 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 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, and eligible participants. AI-generated health content education will be assessed using indicators such as exposure to AI-health-content education sessions, frequency and duration of education, knowledge of AI-generated content, awareness of common limitations of AI-generated health information, understanding of AI hallucinations, awareness of fabricated or unverifiable references, knowledge of source verification, awareness of outdated health information, understanding of appropriate use of AI tools for health information, and knowledge of when to consult qualified healthcare professionals. Recognition of unreliable health information will be assessed using indicators such as ability to identify unsupported health claims, recognition of exaggerated treatment promises, identification of fabricated or unverifiable sources, detection of outdated information, recognition of contradictory medical claims, ability to distinguish evidence-based information from opinion, verification of information against credible health sources, recognition of inappropriate AI-generated medical advice, and willingness to seek professional confirmation before acting on uncertain health information. Data will be collected using structured questionnaires, AI-generated health-content knowledge assessment tools, scenario-based health-information evaluation tests, digital health-literacy instruments, and pre-test and post-test assessments where a quasi-experimental intervention is adopted. Descriptive statistics will be used to summarize participants' demographic and digital-use characteristics, sources of health information, frequency of AI use, previous exposure to AI-health content, and ability to recognize unreliable information. Inferential statistical techniques, including chi-square tests, paired and independent t-tests, correlation analysis, and logistic or multiple regression analysis where appropriate, will be used to determine the impact of AI-generated health content education on recognition of unreliable health information. Where a quasi-experimental design is adopted, recognition 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-generated health content education has a significant positive impact on recognition of unreliable health information among young adults in Nigeria. Young adults exposed to structured and practical AI-health-content education are expected to demonstrate greater ability to identify unreliable health claims than those without comparable exposure. Education may improve participants' understanding of the limitations of AI-generated health content, ability to verify claims using credible sources, recognition of fabricated references and unsupported medical recommendations, and awareness of situations requiring consultation with qualified healthcare professionals. Practical exercises involving realistic AI-generated health messages may strengthen participants' ability to critically evaluate information rather than accepting content solely because it appears professionally written or technologically generated. However, rapidly changing AI technologies, sophisticated misinformation, high volumes of online health content, limited access to authoritative health information, confirmation bias, and overreliance on AI tools may reduce the effectiveness of education alone. The study therefore expects continuous, practical, accessible, and culturally appropriate AI-generated health content education, supported by improved digital health literacy and access to credible health-information sources, to contribute significantly to improved recognition of unreliable health information among young adults in Nigeria. The study is expected to contribute to the literature on artificial intelligence and health, digital health literacy, health misinformation, health-information evaluation, health education, health communication, technology use, and public health in Nigeria. The findings will provide useful information to the Federal Ministry of Health and Social Welfare, Federal Ministry of Education, National Information Technology Development Agency, Nigeria Data Protection Commission, universities, youth organizations, healthcare professionals, digital health providers, public health practitioners, technology developers, development partners, and policymakers regarding strategies for improving the safe use of AI-generated health information. The study will also provide evidence-based recommendations for integrating AI-health-content literacy into health education programmes, strengthening young adults' ability to verify digital health information, promoting responsible use of AI for health information, improving access to credible health resources, and developing sustainable digital health literacy interventions for young adults across Nigeria.

Keywords: AI-generated health content education, unreliable health information, young adults, artificial intelligence, health misinformation, digital health literacy, health-information evaluation, health education, health communication, Nigeria, public health.