This paper examines how an AI driven elective recommendation bot can support clearer and more personalized course selection for students at Capilano University. Many students struggle to choose electives that fit their degree, interests, and long term goals, and current tools like MyCapMap provide requirements but not guidance. Through a combination of literature review and interviews with two academic advisors and a computer science instructor, this project explores whether AI can responsibly ease this pressure without replacing the human side of advising. The research on course recommendation systems shows strong potential for personalization, while also highlighting issues of trust, transparency, and over reliance. The interviews reinforced that any AI tool must act as an assistant, not a decision maker, and should reduce repetitive work rather than replace advisors. Building on these insights, the project developed a hybrid elective recommendation bot that uses rule based filtering and AI reasoning to match students with suitable courses based on their transcripts and interests. The findings suggest that AI can meaningfully improve the elective selection process when paired with human oversight, clear explanations, and ethical design. The paper concludes by outlining future research directions, including real time course data, better interest mapping, and deeper integration with advising workflows.
