ItemConference Paper

Leveraging Neural Networks for Personalized Student Engagement and Performance Prediction

Title
Leveraging Neural Networks for Personalized Student Engagement and Performance Prediction
Creator
Singh, Satyam; Singh, Tanish Pal; Maurya, Ankur; Sinha, Ambrisha; Sharma, Vandana; Singh, Ajay Vikram
Description
Neural Networks can be used to predict students' performance and future placement opportunities. Nowadays, it is a really difficult task for students to predict their chances of getting a good campus placement, even if they have prepared well for it. There is an intense competition among peers, and many factors that influence a student's placement. To manage this data and predict their chances, they need a reliable system. In this paper, we discuss a model in which the system focuses on three main areas: predicting placement chances, analyzing skill gaps, and offering personalized recommendations for improvement. The system would predict many potential career paths by analyzing academic records, extracurricular activities, and job market trends, while also highlighting immediate opportunities and long-term growth prospects. The integration of agentic AI further enhances this system by enabling autonomous decision-making and adaptive learning. Which ensures personalized guidance for each student. By dynamically refining the predictions which are based on real-time feedback, agentic AI helps to empower students to proactively navigate their career paths with greater confidence and precision. This approach provides a genuine solution, in order to improve placement strategies, by ensuring that the students are well-equipped to meet the challenges of the modern workforce. 2025 IEEE.
Source
2025 International Conference on Artificial Intelligence and Machine Vision, AIMV 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Agentic AI; Career Paths; Neural networks; Placement prediction; Skill gaps
Coverage
Singh S., Kalinga Institute of Industrial Technology, Deemed to Be University, Odisha, India; Singh T.P., Kalinga Institute of Industrial Technology, Deemed to Be University, Odisha, India; Maurya A., Bennett University, School of Computer Science Engineering & Technology, India; Sinha A., Galgotias University, School of Education, Greater Noida, India; Sharma V., CHRIST University, Computer Science Department, Bengaluru, India; Singh A.V., Amity University, AIIT, Uttar Pradesh, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152697-9;
Format
online
Language
English
Type
Conference paper

Citation

Singh, Satyam; Singh, Tanish Pal; Maurya, Ankur; Sinha, Ambrisha; Sharma, Vandana; Singh, Ajay Vikram, “Leveraging Neural Networks for Personalized Student Engagement and Performance Prediction,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25745.

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