Investigation into AI-Based Student Internship Matching Systems in University of Abuja, FCT
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- NGN 5000
Background of the Study
Internships play a vital role in bridging the gap between academic learning and practical industry experience for university students. However, many students face challenges in finding relevant internships that align with their academic focus and career aspirations. Additionally, employers often struggle to find suitable candidates that meet their specific needs. Traditionally, internship matching processes rely on manual methods or simple online job boards, which are often inefficient and lead to mismatches. Artificial intelligence (AI) offers an innovative solution by analyzing students' academic backgrounds, skills, and career preferences to match them with suitable internship opportunities.
AI-based internship matching systems use machine learning algorithms to analyze large volumes of data from both students and companies to generate personalized internship recommendations. These systems can optimize the matching process by ensuring that students are matched with internships that best fit their qualifications and career goals, while companies are connected with candidates who meet their specific requirements. At the University of Abuja, FCT, implementing such a system could significantly enhance the quality of internship placements, benefitting both students and employers by streamlining the internship matching process.
Statement of the Problem
The current internship matching process at the University of Abuja is time-consuming, inefficient, and often results in students being placed in internships that do not align with their career aspirations or academic background. Additionally, companies find it challenging to identify students with the specific skills and qualifications they require. This study aims to investigate the effectiveness of AI-based internship matching systems in improving the efficiency and outcomes of internship placements for students at the University of Abuja.
Objectives of the Study
- To design and implement an AI-based internship matching system for students at the University of Abuja.
- To evaluate the effectiveness of the AI-based system in matching students with relevant internship opportunities.
- To identify the challenges and benefits of using AI for internship matching in Nigerian universities.
Research Questions
- How effective is the AI-based internship matching system in connecting students with relevant internship opportunities?
- What impact does the AI-based system have on improving the internship experience for both students and employers?
- What challenges and benefits arise from integrating AI-based internship matching systems at the University of Abuja?
Research Hypotheses
- The AI-based internship matching system leads to more relevant and aligned internship placements for students compared to traditional methods.
- Students matched with internships using the AI system report higher satisfaction with their internship experience.
- The integration of AI-based internship matching systems faces challenges related to user adoption, data availability, and technological infrastructure.
Significance of the Study
This study will contribute to improving the internship experience at the University of Abuja by offering a data-driven, AI-based solution that better matches students with internships that align with their skills and career goals. It will also provide valuable insights for other Nigerian universities seeking to enhance their internship placement processes using advanced technologies.
Scope and Limitations of the Study
The study will focus on the design, implementation, and evaluation of the AI-based internship matching system at the University of Abuja, FCT. The research will be limited to undergraduate students and will not include postgraduate students or external internships provided by organizations outside the university’s partnership network.
Definitions of Terms
AI-Based Internship Matching: The use of artificial intelligence algorithms to match students with internship opportunities based on their skills, academic background, and career aspirations.
Machine Learning: A subset of AI that enables systems to learn from data inputs and improve performance without explicit programming.
Internship Placement: The process by which students are matched with internship opportunities in industry or organizations for experiential learning
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