Ai-enhanced Centralized Knowledge Sharing Platform For College Students

• Author(s):

  Manasi Ahire | Yadnesh Bhoomkar | Savim Meshram | Prashik Ramteke | A. P. Kulkarni

• Keywords:

  Semantic Search, Information Retrieval, Large Language Models (LLM), Vector Database, Dense Retrieval, Natural Language Processing (NLP), Recommender Systems, Collaborative Learning, Knowledge Management

• Abstract:

  For New Batches Of Students Who Are Often Con-fused With Their Respective Admissions And Placements, Existing Digital Platforms Do Not Always Relate To Relevance And Often Appear Vague. Legacy Campus Search Engines Are Built On Simple Keyword-based Algorithms That Are Cumbersome And Not User-friendly. We Propose A Centralized Knowledge Base Powered By Artificial Intelligence (AI) Techniques That Enable More Efficient Search Through Semantic Retrieval, Large Language Models (LLMs), And Personalized Ranking. This System Includes Structured Q&A, Resource Sharing, And Context-based Ranking On The Basis Of User Profiles Such As Branch, Year, And Interests. The Proposed System Was Evaluated Through User Testing Followed By Performance Evaluation. Results Demonstrate That The Platform Can Lower Search Time By 30%–40%, Improve Result Relevancy By 25%–35%, And Yield Greater User Satisfaction Compared To Keyword-based Platforms. Findings Also Indicate That Users Had A Positive Experience With Interview Experience Summaries And Personalization Filters, Which Improved Information Accessibility And Encouraged Continued Platform Engagement. The Findings Confirm That Applying AI-based Ranking Alongside Semantic Search Significantly Increases Information Discovery And Peer Interaction Among College Stu-dents.

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Other Details

• Paper id:

  IJSARTV12I6105671

• Published in:

  Volume: 12 Issue: 6 June 2026

• Publication Date:

  2026-06-11

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