DEVELOPMENT OF CAREER NAVIGATION ASSISTANT FOR COMPUTER SCIENCE STUDENTS USING AJAYI CROWTHER UNIVERSITY AS A CASE STUDY

Abstract This study addresses the challenges faced by Computer Science students in navigating career choices by developing a web-based Career Navigation Assistant tailored for Ajayi Crowther University. The system provides personalized career recommendations through a rule-based weighted scoring algorithm that analyzes academic performance and personality traits, bridging the gap between education and industry demands. Built using React.js for the frontend, Node.js/Express and Python Flask for the backend, and Google Cloud Firestore for data storage, the platform integrates AI via the Gemini API to enhance dynamic recommendations. The Design Science Research methodology guided development, incorporating user feedback through iterative Agile processes. Evaluation results demonstrated strong usability (4.3/5 average satisfaction) and effectiveness, with 85% of users finding career suggestions aligned with their skills. While the system successfully improved students' awareness of career pathways, challenges included data curation for local contexts and latency in AI-generated responses. The study concludes that personalized digital career guidance tools can significantly enhance employability, with recommendations for future enhancements including gamification and deeper university system integration. This project contributes to educational technology by demonstrating how adaptive systems can address career indecision in evolving technical fields.