CHATUR: A Semantic-Based Social Learning and Conceptual Assessment Framework for Automated Descriptive Answer Evaluation

• Author(s): Hrushikesh Shashikant Gawade; Dr. Netraja C. Mulay
• Paper ID: 1718458
• Page: 4741-4749
• Published Date: 29-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

Traditional automated assessment systems used in e-learning platforms mainly depend on keyword matching and basic textual similarity methods. While these methods can detect lexical similarities and repeated expressions, they often struggle to interpret contextual meaning and accurately measure conceptual understanding. This issue becomes more prominent in modern social learning environments, where learners actively participate in generating, sharing, and evaluating knowledge collaboratively. Current assessment systems also struggle to provide detailed concept-level analysis, identify misconceptions accurately, classify responses according to cognitive complexity, and generate transparent feedback for learners. In addition, many advanced AI-driven grading approaches require substantial computational resources, which limits their practical applicability in scalable or resource-constrained educational platforms. To address these limitations, this research proposes the CHATUR framework designed for integration within a collaborative social learning and assessment platform similar to Reddit and X. In this environment, users can both contribute educational content and learn from shared community knowledge. The proposed system employs lightweight transformer-based semantic embedding techniques to evaluate descriptive answers efficiently in a dynamic user-driven setting. It performs contextual similarity analysis between student responses and reference answers, extracts key concepts to examine concept coverage, and identifies misconceptions using rule-based analysis methods. The framework also applies multi-dimensional scoring to assess partial correctness and uses cognitive-level classification to evaluate the depth of understanding demonstrated in responses. Furthermore, CHATUR incorporates explainable AI techniques to provide users with clear and meaningful feedback regarding their performance. Since the framework is optimized for CPU-based execution, it remains accessible, scalable, and suitable for large-scale deployment in low-resource educational environments. By combining AI-powered assessment with collaborative knowledge sharing, the proposed system aims to improve user engagement while delivering more accurate and insightful evaluation of conceptual understanding.

Keywords

Semantic Assessment, Sentence-BERT, Descriptive Answer Evaluation, Explainable AI (XAI), Natural Language Processing (NLP), Conceptual Understanding, Semantic Similarity, Social Learning Platform, Automated Assessment System, Transformer-Based Learning, Educational Technology, AI-Based Evaluation.

Citations

IRE Journals:
Hrushikesh Shashikant Gawade, Dr. Netraja C. Mulay "CHATUR: A Semantic-Based Social Learning and Conceptual Assessment Framework for Automated Descriptive Answer Evaluation" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 4741-4749 https://doi.org/10.64388/IREV9I11-1718458

IEEE:
Hrushikesh Shashikant Gawade, Dr. Netraja C. Mulay "CHATUR: A Semantic-Based Social Learning and Conceptual Assessment Framework for Automated Descriptive Answer Evaluation" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1718458