Design of an AI-Based Chatbot for Student Grievance Redressal in Federal Polytechnic Bauchi, Bauchi State
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Background of the Study
Efficient student grievance redressal is essential for maintaining a supportive academic environment and fostering trust between students and administration. At Federal Polytechnic Bauchi, the traditional grievance redressal process is often hampered by delayed responses, high administrative workloads, and inconsistent handling of complaints. An AI-based chatbot, powered by natural language processing and machine learning, offers a promising solution by automating responses and providing immediate assistance to students (Olufemi, 2023). Such a chatbot can process and categorize grievances submitted through digital channels, provide real-time solutions, and escalate complex issues to human advisors when necessary. By integrating with institutional databases, the chatbot can access relevant policies and historical grievance data, ensuring personalized and context-sensitive responses. This system not only reduces the response time but also ensures that every complaint is addressed in a consistent and transparent manner, thereby improving overall student satisfaction and institutional accountability (Ibrahim, 2024). Furthermore, the chatbot’s 24/7 availability ensures that students have continuous access to support, irrespective of office hours. However, challenges such as ensuring the accuracy of responses, handling diverse linguistic expressions, and safeguarding data privacy must be overcome. This study aims to design, develop, and implement an AI-based chatbot tailored for student grievance redressal at Federal Polytechnic Bauchi, ultimately streamlining the process and enhancing student support services (Chinwe, 2025).
Statement of the Problem
The current grievance redressal system at Federal Polytechnic Bauchi is characterized by manual processing, which results in slow response times and inconsistent handling of student complaints. This inefficiency leads to prolonged resolution periods, leaving many issues unaddressed and contributing to student dissatisfaction (Adebola, 2023). Traditional methods lack the scalability and adaptability required to manage an increasing volume of grievances effectively, especially during peak periods. Additionally, the absence of a unified digital platform means that data on past grievances is often fragmented, hindering the ability to identify recurring issues or systemic problems. Without an automated system, the burden on administrative staff remains high, and students may feel neglected or frustrated by the lack of prompt support. The manual nature of the current system also increases the likelihood of human error, further compromising the integrity of the grievance resolution process. This study seeks to address these challenges by developing an AI-based chatbot that automates the initial stages of grievance redressal. The chatbot is intended to provide immediate, standardized responses to common issues while routing more complex cases to human advisors, thereby improving efficiency and consistency in handling complaints.
Objectives of the Study:
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To design an AI-based chatbot for automating student grievance redressal.
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To evaluate the system’s effectiveness in reducing response time and improving consistency.
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To propose strategies for integrating the chatbot with existing administrative systems.
Research Questions:
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How effectively does the AI-based chatbot address student grievances compared to traditional methods?
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What impact does the chatbot have on reducing response times and administrative workload?
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What are the challenges in implementing the chatbot, and how can they be mitigated?
Significance of the Study
This study is significant as it introduces an AI-based chatbot to streamline student grievance redressal at Federal Polytechnic Bauchi. The system’s ability to provide immediate, consistent responses will enhance student satisfaction and reduce administrative burdens. The findings offer actionable recommendations for integrating AI into academic support services, ultimately promoting transparency and efficiency in handling student concerns (Olufemi, 2023).
Scope and Limitations of the Study:
The study is limited to the design and evaluation of an AI-based chatbot for student grievance redressal at Federal Polytechnic Bauchi, Bauchi State, and does not extend to other support services or institutions.
Definitions of Terms:
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AI-Based Chatbot: An automated conversational agent that uses AI to interact with users.
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Grievance Redressal: The process of addressing and resolving complaints.
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Natural Language Processing (NLP): A branch of AI that processes and interprets human language.
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