Evaluation of AI-Based Sentiment Analysis for University Course Feedback in Kwara State University, Malete, Kwara State
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- NGN 5000
Background of the Study
Student feedback is a crucial element in evaluating and improving course quality in higher education. At Kwara State University in Malete, the adoption of AI-based sentiment analysis has emerged as an innovative method to efficiently process and interpret vast amounts of qualitative course feedback. This approach utilizes natural language processing (NLP) and machine learning algorithms to automatically analyze the emotional tone of student comments, categorizing them into positive, negative, or neutral sentiments (Garcia, 2023; Morgan, 2024). Traditional manual review of feedback is labor-intensive and often subject to human bias, making it difficult to extract actionable insights in a timely manner. AI-driven sentiment analysis enables rapid processing of feedback data, providing real-time insights that can guide course improvements and pedagogical strategies. As universities strive to remain competitive and responsive to student needs, the role of data analytics in shaping academic programs becomes increasingly important. The integration of AI in sentiment analysis not only streamlines the feedback evaluation process but also uncovers underlying trends that might otherwise be overlooked, thereby enhancing decision-making processes (Singh, 2025). Additionally, the deployment of such systems supports the transition towards digital transformation in education, where data-driven approaches are paramount. However, challenges such as contextual interpretation, algorithmic accuracy, and data privacy remain. This study investigates the overall effectiveness of AI-based sentiment analysis in capturing the nuances of student feedback at Kwara State University and explores ways to integrate these insights into ongoing course development efforts (Davis, 2023).
Statement of the Problem
Despite the critical role of student feedback in academic improvement, Kwara State University faces challenges in effectively analyzing and utilizing course evaluations. Traditional manual methods for processing feedback are often slow, biased, and unable to capture the subtle nuances of student sentiment. Although AI-based sentiment analysis offers a promising alternative by automating the categorization of feedback, concerns remain regarding the accuracy and contextual understanding of these systems. In particular, the ability of AI algorithms to interpret ambiguous expressions or colloquial language poses a significant challenge (Kim, 2023). Integration with existing feedback systems further complicates the process due to issues of data interoperability and security. Additionally, there is limited empirical evidence regarding the long-term benefits of using AI for sentiment analysis in educational settings, creating uncertainty about its overall effectiveness. Misclassification of feedback may lead to misguided decisions by academic staff, potentially affecting course quality and student satisfaction (Lee, 2024). This study aims to critically assess the performance of AI-based sentiment analysis systems at Kwara State University by identifying both their strengths and limitations. By examining the technical challenges and contextual factors influencing sentiment detection, the research seeks to provide actionable recommendations for improving the integration and reliability of AI-driven feedback analysis. Ultimately, the goal is to ensure that student voices are accurately captured and translated into meaningful course enhancements, thereby supporting continuous improvement in academic programs (Watson, 2025).
Objectives of the Study
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To evaluate the accuracy and reliability of AI-based sentiment analysis in interpreting student course feedback.
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To identify the challenges and limitations of integrating AI-based sentiment analysis with existing course evaluation systems.
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To recommend strategies for improving the use of AI-driven sentiment analysis in enhancing course quality at Kwara State University.
Research Questions
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How accurately does AI-based sentiment analysis capture the nuances of student feedback?
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What are the primary technical and contextual challenges in using AI for course feedback analysis?
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Which strategies can enhance the effectiveness of AI-driven sentiment analysis in informing course improvements?
Significance of the Study
This study is significant as it assesses the potential of AI-based sentiment analysis to transform the processing of student course feedback at Kwara State University. The research provides critical insights into the technology’s effectiveness and challenges, offering a roadmap for enhancing course evaluation processes. The findings will inform institutional decision-making and policy development, ensuring that student voices are accurately represented and effectively acted upon (Nelson, 2024).
Scope and Limitations of the Study
This study is limited to the evaluation of AI-based sentiment analysis for processing course feedback at Kwara State University and does not extend to other forms of educational data analysis.
Definitions of Terms
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Sentiment Analysis: The computational process of determining the emotional tone behind a series of words.
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Natural Language Processing (NLP): A branch of artificial intelligence that deals with the interaction between computers and human language.
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Course Feedback: Evaluations and comments provided by students regarding their course experiences.
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