Biography
Y Parvathi Devi is a recent graduate in Computer Science and Engineering from Vellore Institute of Technology (VIT), India, having completed her Bachelor of Technology in 2026. She is currently working as a Graduate Engineer Trainee at Alight Solutions, India, where she is developing her professional experience in the technology domain. Her academic and professional interests include artificial intelligence, machine learning, data analytics, software development, and emerging technologies. During her undergraduate studies, she gained experience through academic projects and collaborative research, with a particular interest in applying technology to solve practical problems. Her research interests also extend to explainable and responsible artificial intelligence, intelligent systems, and data-driven decision-making. She is passionate about learning new technologies and exploring their applications across different domains. She has also developed skills in problem-solving, teamwork, and technical communication through her academic and professional experiences. Her current interests focus on expanding her expertise in artificial intelligence and related technologies while exploring opportunities to contribute to innovative and impactful technology-driven solutions.
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.