Evaluex Student Gradient System: A Survey of AI-Driven Academic Evaluation and Feedback Models

Abstract

Artificial Intelligence has brought transformative shifts in nearly every domain, and education is no exception.Yet,despitetherapidadoptionofdigitallearningplatforms and online assessments, the core evaluation processes in most institutions remain strikingly traditional—manual, delayed, and often lacking consistency [2]. Students wait days or weeks for feedback, instructors struggle to maintain fairness and pace, and institutions are left grappling with inefficiency. To address these longstanding challenges, this survey explores the evolution of AI-driven academic assessment tools and intro- duces the EvalueX Student Gradient System, a next-generation conceptual framework built around gradient-based performance tracking and LLM-powered evaluation [4]. Unlike conventional assessmentsystemsthatjudgestudentsatasinglepointin time,EvalueXemphasizesunderstandinghowstudentslearnover time—capturing growth, stagnation, and learning momentum. Through analysis of modern research across automated grad- ing, feedback generation, explainability, and human-AI collabo- ration, the survey identifies critical gaps in existing approaches and highlights why the future of academic evaluation must shift towardcontinuous,transparent,andstudent-centeredevaluation ecosystems. EvalueX represents that shift—a move from static evaluations to dynamic, personalized learning trajectories aug- mented by AI [3].

Introduction

Traditional assessment methods struggle to meet the demands of modern education, especially with large class sizes, diverse learning styles, and the need for timely, consistent feedback. Teachers often face delays and inconsistencies when grading hundreds of responses, while students receive feedback too late to support real learning. These limitations make assessment a bottleneck rather than a support system.

Artificial Intelligence—particularly Large Language Models (LLMs)—offers transformative solutions. Modern AI can analyze written answers with strong contextual understanding, provide instant and consistent grading, and generate high-quality personalized feedback. Unlike traditional systems, AI can also track student learning progress over time, identifying trends, weaknesses, and opportunities for early intervention. This shift toward continuous, trajectory-based evaluation enables a more holistic understanding of student development.

The paper introduces EvalueX, a unified AI-powered academic evaluation framework. EvalueX brings together gradient-based learning analytics, LLM-driven scoring, explainable AI, and centralized instructor controls. It integrates automated grading, detailed feedback, transparency, and performance monitoring into a single platform.

The literature survey highlights major research contributions across automated essay scoring, LLM-based grading, feedback generation, explainable assessment systems, and multi-agent evaluation frameworks. These studies show that modern AI models often match or exceed human grading accuracy, generate rich pedagogical feedback, and help identify hidden errors in student work.

Despite progress, challenges remain. Current systems struggle with long-term learning interpretation, transparency, consistent feedback quality, scalability, and bias control. Future work should focus on multimodal evaluation models, explainable AI (XAI), fairness algorithms, privacy-preserving training, and cloud-ready architectures.

EvalueX is presented as a practical next-generation solution that builds on these advancements to deliver fast, scalable, transparent, and student-centric academic evaluation for institutions.

Conclusion

AI is reshaping academic evaluation, offering consistency, insight, and scalability. However, existing systems lack holis- tic design and temporal awareness. EvalueX addresses these limitations by introducing a gradient-based, transparent, and student-centered model. The future of assessment lies not in static scoring but in dynamic, AI-augmented learning trajec- tories.

References

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Copyright

Copyright © 2025 Prof. G. H. Wani, Yash Pansare, Vaibhav Parabhane, Atharva Patil. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.