ARTIFICIAL INTELLIGENCE

Background: Artificial intelligence (AI) chatbots are increasingly being used as source of health information, particularly among undergraduate students who are highly engaged with digital technologies. These tools provide instant, interactive, and personalized responses to healthrelated queries, which may influence health-seeking behaviors. One growing concern is their role in facilitating self-medication, defined as the use of medicines without consultation with qualified healthcare professionals. While AI chatbots may improve access to health information and empower individuals to make decisions, their unregulated use raises concerns about misinformation, inappropriate drug use, delayed diagnosis, and adverse health outcomes. Despite the increasing global use of AI technologies, there is limited evidence on how undergraduate students in Nigeria utilize AI chatbots in relation to self-medication practices. Understanding students’ knowledge, attitudes, and patterns of use is essential for informing public health interventions and policies. Methods: An analytical cross-sectional study was conducted among undergraduate students in Benin City, Nigeria. Data were collected using a structured, self-administered questionnaire adapted from UTAUT and related acceptance models that assessed socio-demographic characteristics, knowledge of AI chatbots, attitudes toward their use in health decision-making, and prevalence of their use in facilitating self-medication. Knowledge and attitude scores were computed and categorized into levels. Data analysis was performed using appropriate statistical software. Descriptive statistics such as frequencies and proportions were used to summarize variables, while inferential statistics, including chi-square tests, were used to examine associations between variables. Statistical significance was set at p < 0.05.Results: The mean age of respondents was 21.50±3.138 years, with females constituting the majority (78.4%). Awareness of AI chatbots was universal, and about four-fifth of respondents demonstrated good knowledge, with Gemini being the most correctly identified tool. Despite this high awareness, only a small proportion had received formal training on AI or chatbots. About seven-tenth of respondents expressed a positive attitude toward AI chatbot use, perceiving these tools as convenient and useful for obtaining quick health information, although concerns regarding reliability and safety remained common. The prevalence of AI chatbot use for self- medication was considerable, with nearly one-third of respondents reporting use for advice on symptoms, possible diagnoses, and treatment options. ChatGPT was the most commonly used chatbot for self-medication, followed by Gemini. Despite the prevalence of use, the frequency of chatbot utilization for self-medication was mostly occasional or rare. Sex and guardians occupation were significant predictors of good knowledge. Attitude toward AI chatbot use was a strong predictor of prevalence. Respondents with a positive attitude were significantly less likely to use AI chatbots for self-medication compared with those with a negative attitude (OR = 0.178, p < 0.001) Conclusion: Despite high awareness and good knowledge of AI chatbots among respondents, concerns about reliability and safety in self-medication persisted. About one-third had used AI chatbots, mainly ChatGPT, for self-medication. Knowledge, attitude, guardians’ occupation, and social media use significantly influenced utilization, highlighting the need for targeted health education, improved digital health literacy, and regulatory frameworks to ensure safe and responsible use of AI chatbots in healthcare decision-making.

Background: The rapid integration of artificial intelligence (AI) tools into higher education has transformed how students access information, complete academic tasks, and engage with learning. While AI offers significant benefits in efficiency and academic support, growing concerns exist regarding excessive student reliance on these tools, with potential implications for critical thinking, cognitive autonomy, and academic integrity. Despite near-universal AI adoption among students globally, empirical data on the nature and extent of AI dependency among Nigerian undergraduates remain limited. Materials and Methods: A descriptive cross-sectional study was conducted among 588 undergraduate students selected from the Ugbowo campus of the University of Benin using a multi-stage sampling technique. Data were collected using a structured self-administered questionnaire that assessed AI tool awareness and usage, patterns of AI use, and level of AI dependency using the validated 22-item Artificial Intelligence Dependence Scale (AIdep-22), which measures four domains: functional dependence, cognitive dependence, emotional dependence, and loss of control. Factors associated with AI dependency were also explored. Data were analysed using IBM SPSS version 29.0; frequencies, proportions, and means were computed for descriptive statistics, while chi-square tests assessed associations between categorical variables at a significance level of p < 0.05. Results: The mean age of respondents was 22.73 ± 4.65 years, with 52.9% being male. Internet access (99.1%) and smartphone ownership (98.6%) were nearly universal. AI tool awareness was reported by 99.3% of respondents, and 98.1% were active users, with ChatGPT being the most recognised (99.0%), most commonly used (83.2%), and most frequently used (83.2%) tool. The primary academic applications were research assistance (72.3%), summarization of materials (69.7%), and writing support (66.7%), with over half reporting daily use. Regarding usage behaviours, 77.9% reported verifying AI outputs relatively more frequently while 58.8% modified AI-generated content before use more frequently, and 15.0% admitted to unethical use including examination malpractice. In terms of dependency, 49.8% of students exhibited low AI dependency, 40.1% moderate dependency, and 10.0% high dependency. Domain-level mean scores were highest for functional dependence (2.86), followed by emotional dependence (2.55), cognitive dependence (2.50), and loss of control (2.27), with an overall AIdep-22 mean of 2.55, corresponding to moderate dependency. Verification behaviour (p = 0.004) and modification of AI outputs (p = 0.05) were significantly associated with dependency level. The key factors associated with AI dependency were heavy academic workload, high performance expectations, ease of AI access, and fear of making errors. Conclusion: AI tools, particularly ChatGPT, have become deeply embedded in undergraduate academic life at the University of Benin, with near-universal awareness and adoption. Most students exhibited moderate AI dependency, with functional dependence being the most prominent domain. Verification behaviour and output modification were significantly associated with dependency level, underscoring the importance of critical engagement with AI-generated content. These findings highlight the urgent need for institutional guidelines, AI literacy programmes, and pedagogical strategies that promote responsible AI use while preserving students' intellectual independence and academic integrity.

AI in healthcare has gained significant momentum in recent years, revolutionizing the delivery of medical services and transforming patient care processes across various specialties. Nursing, as a fundamental pillar of healthcare, is increasingly experiencing the impact of AI technologies, which range from decision support systems, robotics, predictive analytics, to personalized patient care applications. However, the successful adoption and optimal utilization of AI in nursing practice depend largely on nurses' knowledge, perceptions, and the roles they assume in its implementation. The aim of this study is to provide insights into the current knowledge and perceptions of nurses regarding AI, identifying potential gaps that could hinder effective practice. Understanding these gaps will help healthcare administrators and policy-makers design targeted interventions. This study employs a descriptive cross-sectional research design to investigate knowledge and perception of AI among nurses at the University of Benin Teaching Hospital (UBTH). A total of 257 participants were chosen using a stratified random sampling technique. A well self-structured questionnaire was used to access the knowledge, perception and roles of nurses as regards the use of AI among nurses in UBTH. The result shows, 74.3% of the respondents exhibiting correct knowledge of AI in comparison with the McDonald’s scale indicates a moderate level of knowledge regarding AI. Also with a total mean score of 2.8, the study reveals that the respondents have a good perception of AI. Of the 257 respondents, 196 (76.3%) of the respondents strongly agreed that nurses should be involved in the planning and implementation of AI systems, 15(5.8%) disagreed, 31(12.1%) agreed while 15(5.8%) strongly disagreed. The mean response of the respondents is 2.53 which is greater than the average of 2.50 for a 4-point Likert scale, hence indicating the respondents generally agree that Nurses should be involved in the planning and implementation of AI systems. This study thus concluded that there is a fair knowledge, the respondents have good perception of AI and involving in the planning and implementation of AI systems are some of the roles of nurses in the use of AI