Exploring the Use of AI-Based Speech Recognition for University Student Engagement in Federal University Dutse, Jigawa State
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- NGN 5000
Background of the Study
Student engagement is a critical factor in academic success, and universities continually seek innovative methods to enhance interaction between students and educators. At Federal University Dutse, Jigawa State, AI-based speech recognition technology is being explored as a tool to foster greater student engagement in classroom settings. This technology employs advanced natural language processing and machine learning algorithms to transcribe and analyze classroom interactions in real time. By capturing and analyzing verbal communication, AI-based speech recognition systems can provide valuable insights into student participation, sentiment, and overall engagement levels (Bello, 2023; Musa, 2024). The system can also offer immediate feedback to instructors, enabling them to adjust their teaching methods to better meet student needs. Traditional methods of gauging engagement, such as manual observations or post-class surveys, are often subjective and lack the precision needed to make timely interventions. In contrast, AI-powered speech recognition provides an objective and scalable solution that can continuously monitor and assess classroom dynamics. The use of such technology is particularly relevant in large lecture settings, where it is challenging for instructors to monitor every student’s participation. However, the implementation of AI-based speech recognition faces challenges, including issues of accuracy in diverse acoustic environments, language nuances, and potential privacy concerns. This study aims to investigate the effectiveness of AI-based speech recognition in enhancing student engagement and to identify the operational challenges associated with its deployment in an academic setting. By integrating technological advancements with pedagogical practices, the research seeks to offer a framework that leverages AI to create more interactive and responsive learning environments (Sani, 2025).
Statement of the Problem
Federal University Dutse currently relies on traditional, often subjective, methods to measure student engagement during lectures, which can lead to inaccurate assessments and missed opportunities for improving instructional methods. Although AI-based speech recognition offers a promising alternative by providing real-time analysis of classroom interactions, its implementation is hampered by several challenges. One major issue is the accuracy of speech recognition systems in noisy or diverse linguistic environments, which can result in misinterpretation of student responses (Ibrahim, 2023). Additionally, concerns over data privacy and the potential misuse of recorded interactions have led to resistance from both students and faculty. The integration of AI technology with existing classroom infrastructure is also problematic, as it requires significant technical support and training. Furthermore, the effectiveness of the technology in truly enhancing student engagement remains underexplored, with limited empirical evidence available from similar academic settings. These challenges underscore the need for a comprehensive investigation into the practical utility and limitations of AI-based speech recognition in higher education. This study aims to address these issues by evaluating the performance of the technology in real classroom settings and identifying key factors that influence its effectiveness. The research will explore technical challenges, user acceptance, and the overall impact on student engagement, providing recommendations for overcoming barriers and maximizing the benefits of AI-based solutions in academic environments (Garba, 2024).
Objectives of the Study
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To evaluate the accuracy and effectiveness of AI-based speech recognition in measuring student engagement.
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To identify technical and operational challenges in implementing speech recognition systems in classrooms.
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To propose recommendations for integrating AI-based speech recognition to enhance student engagement.
Research Questions
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How accurately does AI-based speech recognition capture classroom interactions compared to traditional methods?
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What technical challenges hinder the effective implementation of speech recognition in academic settings?
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Which strategies can improve user acceptance and overall effectiveness of AI-based speech recognition in enhancing engagement?
Significance of the Study
This study is significant as it examines the potential of AI-based speech recognition to transform student engagement at Federal University Dutse. By providing objective, real-time insights into classroom interactions, the research aims to inform strategies that enhance teaching effectiveness and student participation. The findings will be valuable for educators, administrators, and technology developers seeking to integrate advanced AI tools into the learning process, ultimately contributing to improved academic outcomes (Aminu, 2024).
Scope and Limitations of the Study
This study is limited to the exploration of AI-based speech recognition for student engagement in classroom settings at Federal University Dutse, Jigawa State.
Definitions of Terms
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Speech Recognition: The process of converting spoken language into text using AI algorithms.
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Student Engagement: The level of participation, interest, and interaction exhibited by students during academic activities.
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Natural Language Processing (NLP): A branch of AI that focuses on the interaction between computers and human language.
