Biography
D. Nayanasri is a Computer Science and Engineering student at Vellore Institute of Technology (VIT), with an academic interest in Artificial Intelligence, Machine Learning, Data Science, and Explainable AI. Her work focuses on applying intelligent computational techniques to develop practical solutions for real-world problems. She has worked on projects involving machine learning, natural language processing, generative AI, and explainable artificial intelligence. Her current project, “Explainable AI in Education: Personalized Student Support through Learning Analytics and Career Guidance,” explores the use of AI-driven learning analytics to understand student performance, provide personalized academic support, and assist students in making informed career decisions. The project emphasizes explainability and transparency so that AI-generated predictions and recommendations can be better understood by students and educators. She is particularly interested in developing responsible and user-centered AI systems that combine predictive analytics with interpretable recommendations. Through her academic projects and research-oriented work, she aims to explore how AI can be used to create accessible, personalized, and meaningful solutions in education and other domains.
Conference contributions 1
A MULTI-MODAL AND EXPLAINABLE AI APPROACH FOR STUDENT RISK ASSESSMENT USING DYNAMIC FUSION
The students of today encounter numerous sources of challenge, including academic demands, emotional fitness, and preparation for their profession. The result is that intelligent support systems need to be provided across a range of integrated interfaces. While there are numerous solutions that address portions of these challenges, they have typically been found to be supported via multiple signatures, and are often located between different platforms that make it difficult for the student to use each service in an efficient manner. This paper will present the Smart Student Suite as an Integrated and AI-enabled student support platform which addresses the four identified areas of support (Academic, Emotion, Career and Writing) as identified. The Academic Module will utilize predictive analytics to identify at risk students and to understand how they are likely to perform in the future. The Emotion Module will utilize multimodal analysis to help determine a student's emotional state and to provide appropriate support in a timely manner. The Writing Module will utilize advanced natural language processing techniques to support the student via support in the improvement of their writing structure, clarity and overall writing performance. The Career Module will also utilize trade fraud detection methodologies to identify unsafe job postings and also to ensure the student's access to legitimate opportunities. By co-locating these features into a single platform, the system is intended to provide a proactive and holistic support environment that will enhance student's academic performance, enhance student's mental well-being, and improve graduate career readiness.