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Effect of Artificial Intelligence Mental Health Chatbots on Mental Health Support Awareness among University Students in Nigeria

Abstract

Artificial intelligence (AI)-based mental health chatbots are emerging as digital tools for providing accessible mental health information, basic emotional support, self-help resources, and guidance toward appropriate professional services. University students in Nigeria may experience academic pressure, financial difficulties, relationship challenges, social adjustment problems, and other stressors that can affect their psychological wellbeing. However, limited awareness of available mental health support services, stigma, concerns about confidentiality, financial constraints, and inadequate access to mental health professionals may discourage students from seeking appropriate assistance. AI mental health chatbots may provide students with convenient access to information and support resources, potentially increasing awareness of available mental health support options. However, concerns regarding accuracy, privacy, reliability, inappropriate responses, and students' understanding of the limitations of AI-based tools may affect their usefulness. Against this background, this study investigates the effect of artificial intelligence mental health chatbots on mental health support awareness among university students in Nigeria. The study will be anchored on the Technology Acceptance Model, Social Cognitive Theory, and the Health Belief Model. The Technology Acceptance Model explains how perceived usefulness, perceived ease of use, and users' acceptance of AI mental health chatbots may influence their engagement with digital mental health resources. Social Cognitive Theory emphasizes the roles of observational learning, self-efficacy, behavioural skills, and interaction with digital environments in developing health-related knowledge and awareness. The Health Belief Model explains how exposure to mental health information may influence students' perceptions of mental health needs, perceived benefits of seeking support, perceived barriers, and cues to action. Collectively, these theoretical perspectives provide a suitable framework for explaining how AI mental health chatbots may influence mental health support awareness among university students in Nigeria. The study will adopt a quantitative quasi-experimental or analytical cross-sectional research design. The study population will comprise undergraduate students enrolled in selected public and private universities across Nigeria. A multistage sampling technique will be used to select states, universities, faculties, departments, and eligible students. Exposure to AI mental health chatbots will be measured using indicators such as awareness and use of AI mental health chatbots, frequency of chatbot interaction, duration of use, types of mental health information accessed, use of emotional-support features, use of self-help resources, access to service information, referral guidance, perceived usefulness, ease of use, accessibility, privacy awareness, and perceived credibility of chatbot responses. Mental health support awareness will be assessed using indicators such as knowledge of available counselling services, awareness of university mental health services, knowledge of professional mental health providers, awareness of community-based mental health services, knowledge of referral pathways, awareness of crisis-support resources, understanding of when professional help is required, knowledge of appropriate sources of psychological support, and awareness of the limitations of AI-based mental health tools. Data will be collected using structured questionnaires, validated mental health support awareness instruments, and relevant chatbot-use measures. Where a quasi-experimental design is used, participants' awareness will be assessed before and after exposure to the AI mental health chatbot intervention. Descriptive statistics will be used to summarize students' demographic and academic characteristics, chatbot exposure, and levels of mental health support awareness. Inferential statistical techniques, including paired or independent sample tests, chi-square tests, correlation analysis, and logistic or multiple regression analysis where appropriate, will be used to determine the effect of AI mental health chatbots on mental health support awareness. Diagnostic tests will also be conducted to assess the reliability, validity, and robustness of the findings. The study is expected to find that exposure to appropriately designed AI mental health chatbots has a significant positive effect on mental health support awareness among university students in Nigeria. Students who interact with AI mental health chatbots are expected to demonstrate improved awareness of available counselling services, professional mental health providers, referral pathways, community-based services, and appropriate sources of psychological support compared with their baseline awareness or students without similar exposure. Chatbots may provide accessible and immediate information, explain mental health concepts in simple language, direct students toward appropriate services, and encourage students to consider professional assistance when necessary. However, concerns about misinformation, inaccurate or inappropriate responses, privacy and data security, overreliance on AI, limited personalization, technological barriers, and students' failure to distinguish AI support from professional mental healthcare may reduce the effectiveness of these tools. The study therefore expects appropriately designed, ethically governed, accessible, and professionally informed AI mental health chatbots to contribute significantly to improved mental health support awareness among university students in Nigeria. The study is expected to contribute to the literature on artificial intelligence in mental health, AI mental health chatbots, mental health support awareness, university students, digital mental health, health technology, mental health literacy, digital health education, counselling services, 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 Universities Commission, universities, university counselling centres, mental health professionals, digital health developers, technology companies, data protection stakeholders, development partners, and policymakers regarding the responsible use of AI technologies in mental health promotion. The study will also provide evidence-based recommendations for developing reliable AI mental health chatbots, strengthening privacy and data-protection safeguards, integrating appropriate referral information into chatbot systems, increasing students' digital mental health literacy, ensuring professional oversight, and establishing responsible AI-supported mental health awareness initiatives across universities in Nigeria.

Keywords: Artificial intelligence, mental health chatbots, mental health support awareness, university students, digital mental health, AI in healthcare, mental health literacy, digital health education, counselling services, health technology, Nigeria, public health.