Copyright © 2026 Authors retain the copyright of this article. This article 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.
@article{206903,
author = {Rithik Raj Vaishya and Shreeya Agrawal and Diya Asija and Rakshak Gupta},
title = {AI/ML-Based Auto Assessment and Evaluation Platform for EdTech Systems},
journal = {International Journal of Innovative Research in Technology},
year = {2026},
volume = {13},
number = {2},
pages = {3074-3084},
issn = {2349-6002},
url = {https://ijirt.org/article?manuscript=206903},
abstract = {With rapidly increasing use of digital education and training space, the need for quick, scalable and objective assessment platforms has grown. Traditional evaluation methodologies are largely reliant on human-intervention, which leads to lagging feedback, inconsistency and operational cost. Due to adoption of digital based education tools and virtual learning ecosystems has highlighted the shortcomings of manual assessment systems. Manual evaluation methods are time consuming, difficult to scale, and often affected by subjectivity and inconsistency. Auto assessment and evaluation platforms have overcome the limitations and shown to be successful using the potential of AI & ML.
This paper will provide a comprehensive study in the field of auto assessment and evaluation platform based on AI. The designs motivation, system architecture, operational workflow, underlying technologies, enabling technologies, applications and performance impact will be discussed. Conceptual and analytical research methodology is used through extensive review of existing literature, systems and industry practices and comparative system analysis to understand their effectiveness and limitations. The report will comment upon the benefits, limitations and future scope research directions to be undertaken towards improvement of accuracy, fairness and flexibility of such automatically evaluating systems, illustrating their increasing application in contemporary education and corporate learning environments training ecosystems.},
keywords = {Auto Assessment, Automated Evaluation, Artificial Intelligence in Education, Learning Analytics, Online Assessment Systems, Machine Learning, EdTech},
month = {July},
}
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