Pedagogical and Ethical Dimensions of AI-Driven Learning Management Systems in the Generative AI Era: A Conceptual Review - Justin, Shajeni (Breakthroughs Information Technology, 2025)
Objective: This study examines the evolution of AI-driven Learning Management Systems (LMS), particularly in the era of Generative AI, by analyzing their pedagogical implications, academic integrity concerns, ethical challenges, and the tension between technological optimization and human-centered educational values. Research Design & Methods: This paper employs a conceptual and theoretical literature review of reputable publications addressing AI integration in higher education platforms. The selected studies are analyzed thematically to identify recurring patterns, critical debates, and emerging pedagogical and ethical issues. Findings: The review indicates that AI-enhanced learning platforms offer significant opportunities for personalization, adaptive feedback, and learning efficiency. However, they also introduce risks related to academic integrity, algorithmic bias, data privacy, and the erosion of cognitive autonomy. Trust and fairness depend on the alignment between system design, human-centered pedagogy, and institutional ethical governance. Implications & Recommendations: Higher education institutions should adopt transparent and pedagogically grounded AI policies that prioritize human-in-the-loop approaches, data protection, and responsible AI literacy. Strategic governance is essential to ensure that technological advancement supports, rather than replaces, core educational values. Contribution & Value Added: This conceptual review proposes an integrative framework linking pedagogy, ethics, and academic integrity, emphasizing that sustainable trust in AI-driven educational systems is shaped by value alignment rather than technological sophistication alone.