The integration of artificial intelligence (AI) in higher education has the potential to revolutionize the delivery of personalized feedback to students. This bibliometric study aims to establish the theoretical and empirical landscape of the impact of personalized AI feedback in higher education, serving as the foundation for an upcoming intervention utilizing AI. Personalized AI feedback in higher education has the potential to significantly transform student learning and competency development. This study explores how AI can enhance the effectiveness of educational feedback and unveil new directions in research and pedagogical practice.

Employing RStudio and its biblioshiny package, we conducted a comprehensive bibliometric analysis to uncover the intellectual structure and emerging trends within this field. Our analysis includes a systematic examination of key publications, authors, and research networks that have shaped the discourse on AI-driven feedback in higher education. By mapping the evolution of this research area, we identified pivotal studies and theoretical frameworks that have contributed to our understanding of how AI can be leveraged to provide personalized feedback that is timely, relevant, and impactful.

The findings reveal significant advancements in AI-driven feedback mechanisms, highlighting their role in enhancing educational outcomes, promoting student engagement, and fostering adaptive learning environments. AI technologies, such as machine learning algorithms and natural language processing, enable the creation of feedback systems that can tailor responses to individual student needs, learning styles, and progress. These systems not only improve the precision and relevance of feedback but also provide scalable solutions for large and diverse student populations.

Moreover, the study examines the practical implications of implementing AI-driven feedback in higher education settings. It addresses the challenges and opportunities associated with integrating AI tools into existing educational frameworks, including the need for faculty training, ethical considerations, and the importance of maintaining a balance between automated and human feedback. The research also underscores the potential of AI to support continuous improvement in teaching practices by providing educators with data-driven insights into student performance and learning behaviors.

This bibliometric analysis serves as a valuable resource for educators, researchers, and policymakers seeking to harness the benefits of AI-driven feedback to improve the quality and effectiveness of higher education. By identifying current trends and gaps in the literature, the study offers a roadmap for future research and development in this field. The insights gained from this analysis will inform the design and implementation of innovative AI-based interventions aimed at enhancing educational outcomes and fostering an inclusive and supportive learning environment.

Keywords: AI Feedback, Higher Education, Bibliometric Analysis, Student Engagement, Adaptive Learning.