Artificial Intelligence–Driven Feedback Mechanisms in Education: A Critical Analysis of Student and Teacher Perspectives, Adoption Patterns, and Pedagogical Implications

Abstract

The rapid integration of Artificial Intelligence (AI) in education has significantly transformed classroom feedback mechanisms, shifting from traditional delayed responses to real-time, personalized, and data-driven interactions. This study examines the perspectives of both students and teachers regarding AI-driven educational technology (EdTech) tools, focusing on awareness, adoption patterns, perceived benefits, and associated challenges. Using an AI-supported analytical framework, the study highlights how intelligent systems enhance student engagement, promote self-directed learning, and reduce teachers’ administrative workload. The findings reveal a growing acceptance of AI tools for academic support, particularly in areas such as content generation, formative assessment, and personalized feedback, aligning with earlier research that emphasizes AI’s role in adaptive learning environments (Kumar & Singh, 2021; Hooda et al., 2022).

However, the study also identifies critical concerns, including issues of data privacy, algorithmic bias, academic integrity, and over-reliance on automated systems, which may hinder critical thinking and creativity (Saxena, 2021). A notable gap exists between student enthusiasm for AI tools and teachers’ cautious approach, emphasizing the need for ethical guidelines, professional training, and institutional support. The study concludes that a blended approach—where AI functions as a supportive tool while teachers provide contextual, emotional, and higher-order feedback—is the most effective strategy for sustainable integration. These insights contribute to the ongoing discourse on optimizing AI in education and provide practical implications for educators, policymakers, and EdTech developers in designing inclusive and effective learning ecosystems.