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LinkUpBuddy: A Generative AI-Integrated Platform for Alumni-Student Interaction and Career Development

Author

Listed:
  • Ramya S
  • Akash C
  • Gambhir R
  • Keerthi Raj S
  • Satwik Hegde

Abstract

Today’s very competitive job market does not make it easy for students to transition from the academic mindset to the professional mindset because they do not have enough practical exposure, the proper guidance, and the confidence to take on the practical scenarios in the real-world. Colleges improve student’s theory knowledge needed to get into the job market as they have so much more to do beyond academic skills to find a job and perform in it. Working alumni (with experience) can be a great source of support for students in their preparation. LinkUpBuddy is a platform that is built to cater as a bridge between students and alumni. It has three powerful modules: a Student-Alumni Networking module, for career guidance and mentorship, which helps in collaborative learning. A Gen-AI powered mock interview tool (InterviewAce) that evaluates students' audio responses against model answers to provide feedback, from which they can gain confidence to face real interviews. An ATS-friendly Resume Builder to create resumes for their job applications. For a better user experience, AI is used to cluster similar queries into an FAQ section, and a real-time hate speech analyzer for content regulation, which makes the platform safer and more professional. By helping students to ask company-specific questions, receive guidance, and prepare with confidence for their interviews, LinkUpBuddy helps students to make clearcut decisions and approach their career journey with clarity and focus.

Suggested Citation

  • Ramya S & Akash C & Gambhir R & Keerthi Raj S & Satwik Hegde, 2025. "LinkUpBuddy: A Generative AI-Integrated Platform for Alumni-Student Interaction and Career Development," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(3), pages 667-678, June.
  • Handle: RePEc:jbh:ijsrcs:v11:y2025:i3:id:1510
    DOI: 10.32628/CSEIT25113312
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25113312
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