Comparative Study of Machine Learning and Expert Systems in Career Guidance for Students: A Case Study of University of Jos (Jos North LGA, Plateau State)
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Background of the Study
Career guidance is a critical aspect of university education, helping students make informed decisions about their future career paths. Traditional methods of career counseling, which often rely on human expertise, are time-consuming and can lack personalized recommendations. Machine learning (ML) and expert systems, powered by artificial intelligence (AI), have the potential to offer more accurate, data-driven, and personalized career guidance. This study focuses on comparing the effectiveness of machine learning models and expert systems in providing career guidance for students at the University of Jos, located in Jos North LGA, Plateau State.
Statement of the Problem
Despite the growing importance of career guidance, many students at the University of Jos still rely on traditional methods, which can be inefficient and lack adaptability to individual needs. Both machine learning and expert systems can offer personalized career advice, but the extent to which each method can improve decision-making for students remains unclear. This study aims to fill this gap.
Objectives of the Study
1. To compare the effectiveness of machine learning algorithms and expert systems in providing personalized career guidance for students at the University of Jos.
2. To assess how each system influences students' career decisions and satisfaction.
3. To explore the potential challenges in implementing AI-driven career guidance systems at the University of Jos.
Research Questions
1. How do machine learning algorithms and expert systems compare in providing personalized career guidance to students?
2. Which system offers more accurate career recommendations based on students' academic performance and interests?
3. What challenges are associated with implementing machine learning and expert systems in career counseling at the University of Jos?
Research Hypotheses
1. Machine learning-based career guidance systems will offer more personalized and accurate recommendations than expert systems.
2. Students who use AI-powered career guidance systems will experience higher satisfaction with their career decisions.
3. Challenges in implementing these systems will include data privacy concerns, system integration issues, and a lack of technical infrastructure.
Significance of the Study
This study will provide insights into the potential of AI technologies, specifically machine learning and expert systems, to improve career guidance at universities. The findings will help university administrators make informed decisions about integrating AI systems into student services.
Scope and Limitations of the Study
The study will focus on comparing machine learning and expert systems specifically for career guidance at the University of Jos. Limitations include the difficulty in quantifying career success and satisfaction, and the need for a robust dataset for training machine learning models.
Definitions of Terms
• Machine Learning (ML): A subset of AI where algorithms learn patterns from data to make predictions or decisions.
• Expert Systems: AI systems designed to mimic the decision-making abilities of human experts in a specific domain.
• Career Guidance: Services provided to students to help them explore career options and make informed decisions about their future paths.
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