Investigation into the Role of AI-Based Sentiment Analysis in University Course Reviews in Federal University Gashua, Yobe State
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- NGN 5000
Background of the Study
University course reviews are essential for continuous improvement in teaching and curriculum design. At Federal University Gashua, Yobe State, the increasing volume of student feedback on course content and instructional methods has made manual analysis inefficient and subjective. AI-based sentiment analysis offers a promising solution by automatically processing and categorizing student opinions into positive, negative, and neutral sentiments. This technology leverages natural language processing and machine learning algorithms to analyze large datasets from course evaluations, online forums, and social media platforms (Umar, 2023).
By applying sentiment analysis, the university can gain valuable insights into student satisfaction, identify areas of improvement, and track the evolution of course perceptions over time. The automated nature of AI-based analysis ensures consistency and objectivity, reducing the biases inherent in manual reviews. Additionally, the system can provide real-time feedback to instructors, enabling prompt adjustments to course content and teaching methods (Aminu, 2024). However, challenges such as contextual misinterpretation, handling of sarcasm, and data privacy concerns must be addressed to ensure accurate and ethical sentiment analysis. The integration of AI-based sentiment analysis in course reviews aligns with broader digital transformation initiatives in higher education, aiming to improve educational quality through data-driven decision-making. This study intends to investigate the effectiveness of AI-based sentiment analysis in interpreting student course reviews at Federal University Gashua, assessing its accuracy, reliability, and impact on curriculum development, and providing recommendations for its effective implementation (Umar, 2023; Aminu, 2024; Suleiman, 2025).
Statement of the Problem
Federal University Gashua currently relies on traditional, manual methods to analyze course reviews, which are often labor-intensive and subject to human bias. This traditional approach leads to delayed responses and inconsistent interpretation of student sentiments, resulting in missed opportunities for timely course improvements (Umar, 2023). Although AI-based sentiment analysis has the potential to provide immediate and objective insights, its application faces several challenges. The accuracy of sentiment analysis can be compromised by issues such as misinterpretation of context, inability to detect sarcasm, and difficulty in handling nuanced expressions of student opinion (Aminu, 2024). Moreover, integrating such a system with existing data collection processes is complex, and there are concerns about data privacy and ethical use of student feedback. These challenges hinder the full utilization of AI-based systems for improving course quality and student satisfaction. Therefore, there is a critical need to investigate the effectiveness of AI-based sentiment analysis in capturing accurate and actionable insights from course reviews. This study seeks to evaluate the performance of these systems, identify the key limitations, and propose strategies to overcome these challenges to ensure that AI-based sentiment analysis can effectively contribute to the enhancement of academic programs (Suleiman, 2025).
Objectives of the Study:
• To evaluate the accuracy of AI-based sentiment analysis in interpreting course reviews.
• To identify challenges and limitations in the current system.
• To recommend improvements for integrating AI-driven sentiment analysis into course review processes.
Research Questions:
• How effective is AI-based sentiment analysis in capturing student opinions in course reviews?
• What are the major challenges in applying sentiment analysis to course review data?
• How can the accuracy and ethical use of sentiment analysis be improved in this context?
Significance of the Study
This study is significant as it investigates the application of AI-based sentiment analysis to university course reviews, offering insights into improving curriculum development and teaching quality. The outcomes will support data-driven decision-making, enhance student satisfaction, and foster a responsive educational environment at Federal University Gashua (Umar, 2023).
Scope and Limitations of the Study:
This study is limited to the application of AI-based sentiment analysis in processing course reviews at Federal University Gashua, Yobe State, and does not extend to other forms of student feedback.
Definitions of Terms:
• AI-Based Sentiment Analysis: The use of artificial intelligence to determine the emotional tone behind textual data (Aminu, 2024).
• Course Reviews: Evaluations provided by students regarding course content and instructional quality (Umar, 2023).
• Sentiment: The emotional attitude or opinion expressed in textual feedback (Suleiman, 2025).
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