Design of an Adaptive Learning Management System Using Artificial Intelligence: A Case Study of Federal University, Wukari (Wukari LGA, Taraba State)
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Background of the Study
The rapid advancements in technology have transformed the landscape of education, with learning management systems (LMS) playing a central role in facilitating the delivery of educational content. However, traditional LMS platforms are often static and fail to cater to the diverse learning needs of individual students. To address this gap, adaptive learning management systems (ALMS) are being developed to personalize the learning experience for each student based on their unique learning styles, progress, and performance. By incorporating artificial intelligence (AI), ALMS can intelligently adapt course content, assessments, and feedback to suit the individual needs of students. This study focuses on the design and development of an AI-powered adaptive learning management system at Federal University, Wukari, which aims to improve student engagement, enhance learning outcomes, and provide personalized educational experiences.
Statement of the Problem
At Federal University, Wukari, the existing LMS lacks the capability to adapt to the individual learning needs of students. This leads to challenges such as disengagement, high dropout rates, and poor academic performance among students who struggle to keep pace with the curriculum. An adaptive learning management system powered by AI has the potential to address these issues by providing personalized learning paths for each student. However, the design, development, and implementation of such a system in the university context have not been fully explored.
Objectives of the Study
- To design and develop an AI-based adaptive learning management system for Federal University, Wukari.
- To evaluate the effectiveness of the adaptive learning management system in improving student engagement and academic performance.
- To recommend strategies for optimizing the use of AI in adaptive learning management systems.
Research Questions
- How can AI-based adaptive learning management systems enhance the learning experience at Federal University, Wukari?
- What is the impact of an adaptive learning management system on student engagement and academic performance?
- What challenges exist in implementing an AI-based adaptive learning management system at the university?
Research Hypotheses
- The AI-based adaptive learning management system will improve student engagement and academic performance at Federal University, Wukari.
- The adaptive learning system will face challenges related to student acceptance, system integration, and content personalization.
- AI-based adaptive learning systems will outperform traditional LMS platforms in terms of student satisfaction and academic outcomes.
Significance of the Study
The findings of this study will provide valuable insights into the potential of AI-powered adaptive learning management systems in higher education. The research will benefit Federal University, Wukari by offering a solution to enhance the learning experience for students, promote personalized learning, and improve academic success. Additionally, the study will contribute to the broader field of educational technology, demonstrating the effectiveness of AI in adapting educational content to individual student needs.
Scope and Limitations of the Study
This study will focus on the design, development, and evaluation of an AI-powered adaptive learning management system at Federal University, Wukari (Wukari LGA, Taraba State). The study will involve a select group of students and faculty members to assess the system’s effectiveness. Limitations of the study include the scalability of the system, the availability of training data, and the readiness of faculty and students to adopt the new technology.
Definitions of Terms
Adaptive Learning Management System: An LMS that uses AI to customize learning materials and assessments based on a student’s performance and learning preferences.
Artificial Intelligence (AI): The simulation of human intelligence in machines that can perform tasks such as learning, problem-solving, and decision-making.
Learning Management System (LMS): A software platform that facilitates the administration, delivery, and tracking of educational content and activities.
Student Engagement: The level of attention, interest, and participation a student exhibits in the learning process.
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