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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65215
Volume:
10 |
6 |
Jun 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Engineering
Sinhgad College of Engineering
Computer Engineering
Sinhgad College of Engineering
Computer Engineering
Sinhgad College of Engineering
Computer Engineering
Sinhgad College of Engineering
Computer Engineering
Sinhgad College of Engineering
Computer Engineering
Sinhgad College of Engineering
The AI-Based Student Connect Portal is an intelligent, multi-user communication platform designed to bridge the gap between faculty, students, and parents through artificial intelligence. The system automates institutional tasks such as notice summarization, categorization, and prioritization to ensure that important information reaches the right audience efficiently. Using LangChain4j and Gemini AI, the platform enables faculty members to retrieve student performance data through natural language queries, eliminating the need for SQL knowledge and improving data accessibility. It also supports secure, permission-aware data retrieval using IAM-integrated RAG mechanisms to ensure privacy and role-based access. Students benefit from an AI chatbot that provides instant responses, summarized notices, and grievance tracking, while parents receive multilingual updates, performance summaries, and real-time alerts about their child’s progress. Administrators can manage notices, respond to grievances, and monitor institutional activities through a unified dashboard. By combining AI-driven automation, secure data handling, and natural language interaction, this system enhances transparency, reduces manual workload, and promotes a smarter, more connected academic environment.
KEYWORDS
The AI-Based Student Connect Dashboard does what it set out to do — it builds a smarter, more secure communication layer between students, faculty, parents, and administrators. Bringing together AI, NLP, and cloud infrastructure in one platform means less manual work, better transparency, and faster access to academic information for everyone involved. Prompt-to-SQL querying lets faculty retrieve student data in plain English. Students and parents get summarized notices, multilingual updates, and grievance tracking without having to chase anyone down for information. That's a meaningful shift from how most academic portals currently work. The cloud-based architecture means the system can scale as needed and stay available without interruption — which matters in any real institutional setting. IAM-based authentication and role-aware retrieval ensure the right people see the right data, and nothing more. At its core, this project shows that AI-powered automation isn't just a theoretical upgrade for campus communication. It's a practical one — and this system is a concrete step toward making academic environments genuinely smarter and more connected.
[1] Jooyoung Jeong, Sang-Goo Lee, “Permission-Aware RAG: Identity and Access Management (IAM)-Based Access Filtering in Multi-Resource Environments” IEEE, Dec 2024. [2] Buchepalli Praneeth, Mohana, Eshitha Chowdary Nattem, et al., “Optimization of Customer Feedback Summarization Using Large Language Models (LLM) and Advanced Augmented Retrieval Generation”, IEEE, July 2025. [3] Rodrigo Pedro, Miguel E. Coimbra, Daniel Castro, Paulo Carreia, Nuno Santos, “Prompt to-SQL Injections in LLM Integrated Web Applications: Risks and Defence”, 2025. [4] Y. Gao et al., K. M. Fitria, “Retrieval Augmented Generation for Large Language Models”, Feb 2025. [5] L. Ammann, S. Ott, C. R. Landolt, and M. P. Lehmann, “Securing RAG: A Risk Assessment and Mitigation Framework”, 2024. [6] H. Liu, X. Chen, and X. Liu, “Application of Weight Distribution Method”. [7] Combining Sentiment Dictionary and TF-IDF for Text Sentiment Analysis”, 2022. [8] K. Muludi, K. M. Fitria, and J. Triloka, “Retrieval Augmented Approach: Document Generation Question Answering Using Large Language Models”, July 2023. [9] X. Li, Y. Cao, Y. Ma, and A. Sun, “Long Context vs. RAG for LLMs: An Evaluation and Revisits”, 2024. [10] Pedro et al., Li et al., “Natural Language to SQL Querying”, 2025.
Publication Date
6/20/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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