Ethical Implications of AI and Machine Learning in Education: A Systematic Analysis

Abstract

Ethical considerations should be examined to determine how AI and ML affect education. Educational AI and ML bring privacy, security, and student data usage problems. This research examined AI and ML ethics in higher education at selected universities. Ethical issues AI and machine learning in education provide fairness, privacy, and openness. AI training data may perpetuate educational biases and impair student achievement. For complete comprehension, mixed methods research included quantitative and qualitative data. Four Lusaka district universities contributed 100 survey respondents. The initiative included four universities' department chairs, professors, and students. Structured open-ended interviews and questionnaires collected data. Quantitative questionnaire data was descriptively examined in SPSS and Excel, while semi-structured interview data was thematically evaluated. According to research, AI may reduce educational monitoring and learner engagement. Another concern is the digital gap and AI access. AI's sophisticated skills may be inaccessible to impoverished students, worsening educational inequity. The report advised training students and staff on data security and providing explicit permission procedures for data use in AI-driven educational systems, including strong encryption, anonymisation, and access limits.