Enrollment in courses is a key performance indicator in educational systems for maintaining academic and financial viability. Today, a lot of factors, comprising demographic and individual features like age, gender, academic background, financial capabilities, and academic degree of choice, contribute to the attrition rates of students at various higher education institutions. In this study, we developed prediction models for students' attrition rate in pursuing a computer science degree as well as those who have a high chance of dropping out before graduation using machine learning methodologies. This approach can assist higher education institutions in creating effective interventions to lower attrition rates and raise the likelihood that students will succeed academically. Student data from 2015 to 2022 were collected from the Federal University Lokoja (FUL), Nigeria. The data was preprocessed using existing WEKA machine learning libraries where our data was converted into attribute-related file form (ARFF). Further, the resampling techniques were used to partition the data into the training set and testing set, and correlation-based feature selection was extracted and used to develop the students' attrition model to identify the students' risk of attrition. Random Forest and decision tree machine learning algorithms were used to predict students' attrition. The results showed that Random Forest has 79.45% accuracy while the accuracy of Random tree stood at 78.09%. This is an improvement over previous results, where an accuracy of 66.14%. and 57.48% were recorded for random forest and Random tree respectively. This improvement was because of the techniques demonstrated in this study. It is recommended that applying techniques to the classification model will improve the performance of the model.

Generative artificial intelligence is becoming embedded in higher education as students evaluate its usefulness and integrity implications. We analyzed Global ChatGPT Student Survey data to examine associations among academic-integrity concerns, satisfaction, and perceived learning benefits across national academic settings. The source dataset contained 23,218 records from 108 identifiable countries. The primary multigroup analysis included 8650 respondents from 15 countries, meeting study-defined thresholds for FIML-eligible sample size (≥300), complete nine-indicator data (≥200), completeness (≥80%), and model feasibility. A seven-country robustness analysis (N = 5871) applied stricter sample-size and completeness criteria. Configural and full metric invariance were broadly supported; full scalar invariance was not, so latent means were not compared. Satisfaction was positively associated with perceived learning benefits in all 15 countries. Academic-integrity concerns were negatively associated with satisfaction in 14 countries and directly with perceived learning benefits in 13, although five and six respective confidence intervals included zero. Robust Wald tests of unstandardized paths detected heterogeneity, while equality constraints caused only trivial global-fit deterioration. The robustness analysis reproduced the directional pattern and heterogeneity evidence. These findings provide a limited empirical window into students’ GenAI-related academic norms and evaluations; they do not directly measure culture or establish causal mediation.

Generative artificial intelligence is becoming embedded in academic practice as students and institutions negotiate acceptable assistance, originality, authorship, and academic integrity. We analyzed Global ChatGPT Student Survey data to examine relationships among academic-integrity concerns, satisfaction, and perceived learning benefits across national academic settings. The source dataset contained 23,218 records from 108 identifiable countries; the primary multigroup analysis included 8,650 respondents from 15 countries meeting prespecified sample-size, completeness, and model-feasibility criteria. A nine-indicator model assessed the three constructs. Configural and full metric invariance were broadly supported, allowing comparison of structural associations; full scalar invariance was not supported, so latent means were not compared. Satisfaction was positively associated with perceived learning benefits in all 15 countries, whereas associations involving academic-integrity concerns were predominantly negative. Robust omnibus tests detected heterogeneity in path magnitudes, although equality constraints produced only trivial deterioration in global fit. A seven-country robustness analysis (N = 5,871) reproduced the directional pattern and heterogeneity evidence. By evaluating measurement comparability before cross-national structural comparison, the study shows that broadly shared relational patterns can coexist with contextual variation in emerging digital academic cultures.

This chapter delves into the ethics of cybersecurity in emerging healthcare systems, examining the complex relationship between ethical considerations and technological advancements. We begin by defining evolving healthcare systems and then highlight the significance of cybersecurity in preserving trust among patients. We examine the benefits and drawbacks of integrating technology while highlighting the delicate balance that must be drawn between innovation and data security. When it comes to patient autonomy, informed consent, and responsible data sharing for investigation, ethical considerations are crucial. A thorough analysis of the legal framework is conducted, with a focus on the challenges associated with enforcing cybersecurity regulations in rapidly evolving healthcare systems. We analyse the ethical ramifications of AI and data breaches in the healthcare industry, promoting human oversight, fairness, and precautionary practices. In addition to providing a comprehensive road map for navigating this rapidly changing environment, the chapter concludes with a call to action that urges stakeholders to give ethical cybersecurity practices priority when creating and implementing new healthcare systems.