Analysis of AI‑Based Student Sentiment Analysis for University Course Evaluation in University of Abuja, FCT
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Background of the Study
University course evaluation plays a vital role in enhancing teaching quality and student satisfaction. At the University of Abuja, FCT, the integration of AI‑based student sentiment analysis is being investigated as a novel method for gauging course effectiveness. Traditional evaluation methods—such as paper‑based surveys—are often limited by low response rates and subjective bias. In contrast, AI‑driven sentiment analysis leverages natural language processing (NLP) techniques to systematically analyze student feedback collected from digital platforms, social media, and online forums (Chinwe, 2023; Musa, 2024). These algorithms assess emotional tones, key phrases, and contextual language to provide objective insights into student perceptions of course content, instructional methods, and overall learning environment. By processing large datasets, the system can detect subtle trends and emerging issues that might otherwise be overlooked. The use of real‑time sentiment analysis enables the administration to promptly address concerns, thereby fostering continuous improvement in teaching practices. Additionally, integrating AI in course evaluations supports data‑driven decision‑making, enhancing transparency and accountability in academic planning. However, challenges such as ensuring the accuracy of sentiment detection, mitigating algorithmic biases, and protecting student privacy are critical. Pilot studies in other institutions have demonstrated that AI‑based systems can significantly improve the timeliness and reliability of course evaluations (Okoro, 2024). The current study aims to evaluate the practical implementation of AI‑based sentiment analysis in the context of the University of Abuja, examining its impact on course quality and student engagement. It will also consider the technical infrastructure needed for successful deployment and explore faculty and student acceptance of AI‑driven evaluation metrics. Through a comprehensive review of system performance and stakeholder feedback, the research intends to propose a framework for the integration of sentiment analysis tools into the university’s course evaluation process, ensuring that insights are actionable and aligned with academic improvement goals.
Statement of the Problem
The University of Abuja’s current course evaluation system relies heavily on manual surveys that are often plagued by low response rates and subjective interpretation of feedback. This inadequacy hampers the timely identification of issues affecting course quality. Although AI‑based sentiment analysis offers an innovative solution by automating the analysis of student feedback, its implementation is fraught with challenges. Data quality issues, such as inconsistent feedback formats and informal language, reduce the reliability of sentiment classification. Furthermore, concerns regarding data privacy and the potential misinterpretation of colloquial expressions complicate the system’s effectiveness. Faculty members have expressed skepticism about whether AI tools can accurately capture the nuances of student sentiment, potentially leading to misguided decisions (Abdul, 2023). Additionally, integrating the AI system with existing digital platforms requires significant technical adjustments and staff training. The gap between the potential benefits of AI‑driven sentiment analysis and its practical application in course evaluations remains wide. This study seeks to address these challenges by assessing the operational performance of an AI‑based sentiment analysis tool at the University of Abuja. Through a comparative analysis of traditional evaluation methods and AI‑enhanced feedback processing, the research aims to identify critical barriers and propose strategies to improve data accuracy, system integration, and overall reliability. Ultimately, the goal is to provide a robust framework that leverages AI to deliver actionable insights into course effectiveness while ensuring that student privacy and ethical standards are maintained (Chinwe, 2024).
Objectives of the Study
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To evaluate the accuracy and reliability of AI‑driven sentiment analysis for course evaluation.
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To identify technical and ethical challenges in implementing AI for feedback analysis.
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To propose strategies for integrating AI‑based tools into existing course evaluation systems.
Research Questions
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How effectively does AI‑based sentiment analysis capture student opinions compared to traditional methods?
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What data quality and privacy issues affect the system’s performance?
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Which strategies can enhance the integration of AI into the course evaluation process?
Significance of the Study
This study is significant as it investigates the application of AI‑based sentiment analysis to improve course evaluations at the University of Abuja. By automating feedback analysis, the research aims to deliver objective, timely insights into teaching effectiveness and student satisfaction. The findings will support academic administrators in refining evaluation processes and implementing data‑driven improvements that enhance educational quality while addressing privacy and bias concerns (Olayinka, 2024).
Scope and Limitations of the Study
This study is limited to the analysis of AI‑based sentiment analysis for course evaluation at the University of Abuja, FCT, and does not extend to other evaluation methods.
Definitions of Terms
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Sentiment Analysis: The process of using AI to interpret and classify emotions within textual data.
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Natural Language Processing (NLP): A branch of AI that enables computers to understand human language.
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Course Evaluation: The process of assessing the quality of educational courses based on student feedback.
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