An Investigation of the Use of AI for Intelligent Tutoring in Ahmadu Bello University, Zaria, Kaduna State
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Background of the Study
Intelligent tutoring systems (ITS) represent a significant advancement in educational technology, providing personalized instruction and real-time feedback to students. At Ahmadu Bello University, Zaria, Kaduna State, the application of AI for intelligent tutoring is seen as a promising strategy to enhance student learning outcomes. These systems integrate natural language processing, machine learning, and adaptive learning algorithms to tailor educational content to individual student needs (Garba, 2023). By continuously analyzing student performance data, ITS can identify learning gaps and deliver customized remedial resources, thus facilitating a more effective and engaging learning experience (Hassan, 2024).
The adoption of AI in tutoring addresses the limitations of traditional classroom settings, where one teacher is expected to cater to diverse learning styles and paces. Intelligent tutoring systems offer a scalable solution by providing individualized support that can adjust in real time based on student responses and progress. This personalized approach not only enhances comprehension but also boosts student motivation by making learning more interactive and responsive. Moreover, the data collected by these systems serve as valuable feedback for educators, enabling them to refine curriculum design and teaching strategies (Jibril, 2025).
Despite these advantages, the integration of AI-based intelligent tutoring systems poses several challenges, including issues of system reliability, user acceptance, and data privacy. The effectiveness of ITS depends on the quality of its algorithms and the robustness of its underlying data infrastructure. Furthermore, the success of such systems hinges on their ability to seamlessly integrate with existing educational practices and curricula. This study, therefore, seeks to evaluate the role of AI in intelligent tutoring at Ahmadu Bello University, examining both the benefits and the challenges associated with its implementation, with the ultimate goal of informing strategies to optimize the use of AI in enhancing student learning experiences (Garba, 2023).
Statement of the Problem
Despite the promising capabilities of AI-based intelligent tutoring systems, Ahmadu Bello University encounters several obstacles in their effective implementation. One major issue is the variability in the quality of input data, which can lead to inconsistencies in the personalized feedback provided by the system (Garba, 2023). Inaccurate or incomplete data may result in suboptimal tutoring performance, thereby affecting student learning outcomes. Moreover, there is significant resistance from some faculty members who are skeptical of relying on AI systems to supplement traditional teaching methods. Concerns over the potential depersonalization of the learning process and the reliability of AI-generated feedback further complicate the adoption of ITS (Hassan, 2024).
Additionally, technical challenges such as system integration with existing educational platforms, continuous maintenance of the AI algorithms, and ensuring data security present substantial hurdles. The ethical implications of collecting and analyzing extensive student data also remain a contentious issue, raising questions about privacy and the potential misuse of sensitive information (Jibril, 2025). These challenges underscore the need for a systematic evaluation of the current ITS implementations and the development of robust frameworks to mitigate associated risks. The study aims to identify these barriers and propose comprehensive strategies that balance technological innovation with ethical and practical considerations, thereby enhancing the overall effectiveness of intelligent tutoring systems in a university setting (Garba, 2023).
Objectives of the Study:
- To evaluate the effectiveness of AI-based intelligent tutoring systems in improving student learning outcomes at Ahmadu Bello University.
- To identify challenges in the implementation and integration of ITS within existing educational frameworks.
- To propose strategies for optimizing the use of AI in delivering personalized tutoring and support.
Research Questions:
- How effective are AI-based intelligent tutoring systems in enhancing student learning outcomes?
- What are the main challenges faced by educators in integrating ITS into traditional teaching practices?
- What measures can be taken to ensure the ethical and efficient implementation of intelligent tutoring systems?
Significance of the Study
This study is significant as it investigates the role of AI in intelligent tutoring at Ahmadu Bello University, providing insights into how personalized, adaptive learning can enhance educational outcomes. By addressing implementation challenges and ethical considerations, the research offers guidance for integrating advanced tutoring systems into traditional academic settings. The findings are expected to inform educators and policymakers on best practices, contributing to improved student performance and a more dynamic, responsive learning environment (Hassan, 2024).
Scope and Limitations of the Study:
This study is limited to the investigation of AI-based intelligent tutoring systems at Ahmadu Bello University, Zaria, Kaduna State, and does not extend to other educational technologies or institutions.
Definitions of Terms:
- Intelligent Tutoring System (ITS): An AI-based system that provides personalized instruction and feedback to students (Garba, 2023).
- Adaptive Learning: A method of instruction that adjusts the content and pace based on individual student performance (Hassan, 2024).
- Data Privacy: The protection of personal and sensitive information collected and used by educational technologies (Jibril, 2025).
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