The affordances of Artificial Intelligence (AI) and ethical considerations across the instruction cycle: A systematic review of AI in online higher education

As Artificial Intelligence (AI) advances, discussions regarding its potential in education have attracted significant attention. This systematic review synthesizes AI affordances in online higher education, particularly identifying various ways AI is used throughout the online instruction cycle. We analyzed fifty-five studies focusing on publication trends, research methodology and quality, AI affordances during the design, facilitation, assessment and evaluation stages, and ethical considerations in this context. The findings revealed the applications of AI-empowered systems and Machine Learning (ML) models in various tasks such as establishing learning objectives in design, supporting cognition in facilitation, automatic grading in assessment, and measuring instruction quality in evaluation. We also discussed the trends regarding the limited incorporation of theoretical frameworks, a dominance of quantitative methods, a focus of big data, and a tendency towards personalization and adaptation. The ethical considerations were categorized for each research phase-data collection, analysis, and interpretation and usage, with an emphasis on validation approaches for AI generated outputs. These findings have implications on effectively integrating AI technologies by highlighting possibilities and competencies required for practitioners and inferring potential opportunities for researchers.