Artificial Intelligence (AI) and sentiment analysis are transforming the evaluation of student feedback, enabling educational institutions to extract actionable insights and enhance academic quality. Traditional feedback mechanisms face limitations in handling large volumes of unstructured data, interpreting nuanced student opinions, and providing timely information for decision-making. The integration of AI-driven sentiment analysis addresses these challenges by leveraging advanced Natural Language Processing (NLP) techniques, including machine learning, deep learning, and transformer-based models, to capture sentiment polarity, emotional intensity, and contextual meaning. Aspect-based sentiment analysis and real-time visualization tools facilitate precise identification of areas for improvement in teaching, curriculum design, infrastructure, and student support systems. Ethical considerations, including data privacy, fairness, and explainability, are essential to ensure trustworthy implementation of AI frameworks in educational settings. This chapter highlights emerging trends, methodological approaches, and institutional applications of AI-based sentiment analysis, demonstrating its potential to convert qualitative feedback into evidence-based strategies for continuous institutional improvement. The discussion emphasizes pathways for future research, including multilingual sentiment modeling, predictive analytics, and interpretable AI systems, which collectively support innovation in educational governance and student-centered learning.
The rapid evolution of Artificial Intelligence (AI) has created transformative opportunities within educational institutions, enabling a shift from traditional, manual feedback analysis to data-driven decision-making processes [1]. Student feedback serves as a vital indicator of teaching quality, curriculum relevance, and institutional effectiveness, but conventional mechanisms often fail to capture the full complexity of learner experiences [2]. Surveys, questionnaires, and open-ended responses are limited by rigid structures, low scalability, and subjective interpretation, making it challenging to generate actionable insights [3]. Large volumes of textual data further exacerbate delays in analysis and decision-making, reducing the effectiveness of institutional interventions [4]. AI-based methods address these limitations by providing automated and scalable approaches that extract meaningful information from diverse feedback sources [5]. Leveraging Natural Language Processing (NLP) and machine learning algorithms, AI enables the identification of sentiment polarity, emotional intensity, and contextual significance, allowing administrators to evaluate student experiences comprehensively [6]. Integrating AI with institutional analytics systems transforms qualitative feedback into structured data that supports evidence-based strategies, promoting academic excellence and operational efficiency [7].
The significance of student feedback extends beyond individual course evaluations to institutional development and accountability [8]. Accreditation bodies and ranking organizations increasingly consider feedback outcomes as a measure of educational quality, faculty performance, and student satisfaction [9]. Effective interpretation of feedback ensures alignment with accreditation standards and enhances institutional reputation at national and global levels [10]. Feedback analysis enables institutions to benchmark performance against peer organizations, identify strengths and weaknesses, and prioritize resource allocation to improve academic and administrative processes [11]. By systematically integrating feedback into decision-making, institutions can demonstrate responsiveness, transparency, and adherence to quality-assurance frameworks [12]. Student opinions provide insights into areas such as teaching effectiveness, curriculum design, infrastructure adequacy, and student support services, serving as a foundation for informed policy formulation and continuous quality improvement [13]. The ability to translate feedback into actionable interventions strengthens institutional credibility and fosters a culture of learner-centered education [14].
