Enhancing Student Motivation through AI-Based Personalized Learning in Secondary Schools in Damaturu Local Government Area, Yobe State
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- 1-5 Chapters
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- NGN 5000
Background of the Study
Student motivation plays a crucial role in the learning process, directly impacting academic performance, engagement, and long-term educational success. Motivation can be classified into intrinsic motivation, which arises from personal interest and curiosity, and extrinsic motivation, which is driven by external rewards or pressure (Adebayo & Yusuf, 2024). In many secondary schools, declining student motivation has become a growing concern, often linked to rigid curricula, lack of individualized attention, and outdated teaching methodologies (Okonkwo & Salisu, 2023).
The advent of artificial intelligence (AI) in education has introduced new possibilities for addressing these challenges through personalized learning. AI-based personalized learning systems analyze student behavior, learning styles, strengths, and weaknesses to create customized educational experiences tailored to individual needs (Olawale & Musa, 2024). Unlike traditional one-size-fits-all approaches, AI-driven systems can adapt lesson plans, provide real-time feedback, and recommend learning resources that align with each student's progress and interests (Adebayo & Ibrahim, 2023).
Despite the potential benefits of AI-based personalized learning, many secondary schools in Damaturu Local Government Area still rely on conventional teaching methods that fail to cater to the diverse learning needs of students (Okonkwo & Salisu, 2024). Large classroom sizes, teacher-centered instruction, and standardized assessments make it difficult to address individual learning gaps, resulting in reduced motivation and poor academic outcomes. Additionally, students who struggle with specific subjects often feel left behind due to the lack of targeted support and adaptive learning tools (Adebayo & Yusuf, 2024).
This study aims to explore how AI-based personalized learning can enhance student motivation in secondary schools in Damaturu. By assessing current motivational challenges, evaluating the effectiveness of AI-driven learning models, and identifying potential implementation strategies, the research will provide valuable insights into the role of artificial intelligence in improving educational experiences.
Statement of the Problem
Many secondary school students in Damaturu Local Government Area struggle with low motivation, leading to disengagement, reduced academic performance, and increased dropout rates. Traditional teaching methods often fail to accommodate diverse learning needs, making it difficult for students to stay interested and actively participate in their studies (Olawale & Musa, 2023). The lack of individualized attention further exacerbates learning difficulties, particularly for students who require additional support to grasp complex concepts (Adebayo & Yusuf, 2024).
While AI-based personalized learning has been widely recognized for its ability to improve motivation and engagement, its adoption in secondary schools in Damaturu remains minimal. Challenges such as limited access to digital resources, lack of awareness about AI-driven education, and inadequate teacher training hinder the effective implementation of personalized learning strategies (Okonkwo & Salisu, 2024). Without appropriate interventions, students may continue to struggle with low motivation, leading to long-term academic deficiencies and reduced career opportunities.
This study seeks to investigate how AI-based personalized learning can be leveraged to enhance student motivation in secondary schools in Damaturu. By identifying the barriers to AI adoption and proposing practical implementation strategies, the research aims to contribute to creating a more engaging and adaptive learning environment.
Objectives of the Study
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To assess the current state of student motivation in secondary schools in Damaturu Local Government Area.
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To evaluate the challenges hindering the adoption of AI-based personalized learning in secondary schools.
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To propose AI-driven personalized learning strategies to enhance student motivation and academic performance.
Research Questions
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What is the current level of student motivation in secondary schools in Damaturu Local Government Area?
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What challenges hinder the adoption of AI-based personalized learning in secondary schools?
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How can AI-driven personalized learning strategies enhance student motivation and academic performance?
Research Hypotheses
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The current level of student motivation in secondary schools in Damaturu Local Government Area is low.
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Limited access to technology and inadequate teacher training are major barriers to the adoption of AI-based personalized learning.
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Implementing AI-based personalized learning strategies will significantly enhance student motivation and academic performance.
Significance of the Study
This study is significant as it explores how AI-based personalized learning can improve student motivation in secondary schools in Damaturu. By examining the benefits and challenges of AI-driven education, the research will provide valuable insights for educators, policymakers, and school administrators. The findings will help in the development of adaptive learning strategies that cater to individual student needs, fostering a more engaging and effective learning environment. Additionally, the study will contribute to the growing body of knowledge on the role of artificial intelligence in enhancing education, serving as a reference for future research on AI applications in secondary school learning.
Scope and Limitations of the Study
This study is limited to secondary schools in Damaturu Local Government Area, Yobe State. It focuses on assessing student motivation, evaluating the challenges associated with AI-based personalized learning, and proposing solutions for implementation. The study does not cover primary schools, tertiary institutions, or other local government areas outside Damaturu.
Definitions of Terms
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AI-Based Personalized Learning: An educational approach that uses artificial intelligence to tailor learning experiences based on individual student needs, learning styles, and progress.
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Student Motivation: The level of enthusiasm, interest, and commitment that students exhibit toward their learning activities.
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Adaptive Learning: A teaching method that adjusts instructional content and pace based on a student’s performance and learning preferences.
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