This study investigates the primary technological and socio-environmental factors influencing the adoption intentions of AI-powered technology at the corporate level within higher education institutions. A conceptual model based on the Diffusion of Innovation Theory (DOI), the Technology–Organization–Environment (TOE), and the Technology Acceptance Model (TAM) combined framework were proposed and tested using data collected from 367 higher education students, faculty members, and employees. SPSS Amos 24 was used for CB-SEM to choose the best-fitting model, which proved more efficient than traditional multiple regression analysis to examine the relationships among the proposed constructs, ensuring model fit and statistical robustness. The findings reveal that Compatibility “C”, Complexity “CX”, User Interface “UX”, Perceived Ease of Use “PEOU”, User Satisfaction “US”, Performance Expectation “PE”, Artificial intelligence “AI” introducing new tools “AINT”, AI Strategic Alignment “AIS”, Availability of Resources “AVR”, Technological Support “TS”, and Facilitating Conditions “FC” significantly impact AI adoption intentions. At the same time, Competitive Pressure “COP” and Government Regulations “GOR” do not. Demographic factors, including major and years of experience, moderated these associations, and there were large differences across educational backgrounds and experience.

This study investigates the primary technological and socio-environmental factors influencing the adoption intentions of AI-powered technology at the corporate level within higher education institutions. A conceptual model based on the Diffusion of Innovation Theory (DOI) and the Technology-Organization-Environment (TOE) framework was proposed and tested using data collected from 367 higher education students, faculty members, and employees. The findings reveal that Compatibility, Complexity, User Interface, Perceived Ease of Use, User Satisfaction, Performance Expectation, AI introducing new tools, AI Strategic development, Availability of Resources, Technological Support, and Facilitating Conditions significantly impact AI adoption intentions. At the same time, Competitive Pressure and Government Regulations do not. Demographic factors, including major and years of experience, moderated these associations, and there were large differences across educational backgrounds and experience. The SPSS Amos 24 was used for SEM to choose the best-fitting model that proved to be more efficient than traditional multiple regression analysis.

Healthcare systems have increasingly relied on intelligent computational tools to manage the rising complexity of diagnosing human infectious diseases, especially in environments where early clinical intervention is limited or delayed. Since many infections spread through close physical contact and exhibit overlapping physiological symptoms, rapid and reliable identification of infected individuals is essential to prevent transmission, isolate high-risk cases, and protect healthy populations. With the expansion of internet-enabled health monitoring, smart devices now serve as continuous data sources, providing real-time physiological indicators that support large-scale diagnostic analytics. This study proposes a secure machine learning framework to detect human infections using two curated datasets: the first includes physiological parameters of 941 infected individuals, such as body temperature, blood oxygen saturation, and respiratory rate, while the second contains similar records from 940 non-infected individuals who share overlapping symptoms. Multiple classification models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree, were trained and evaluated using the KNIME analytics platform. Additionally, Logistic Regression and Random Forest models were tested to benchmark performance. Although Logistic Regression and Random Forest achieved the highest accuracy, Random Forest was selected as the optimal model due to its superior sensitivity score of 0.8275, outperforming SVM and Logistic Regression. The findings highlight the potential of secure, internet-driven machine learning systems to enhance early detection of human infections and support real-time clinical decision-making in distributed healthcare environments.