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Related Concept Videos

Classification of Systems-I01:26

Classification of Systems-I

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Related Experiment Videos

Intelligent automated essay scoring under uncertainty using type 2 neutrosophic ontologies.

Saad M Darwish1, Noha A Bagi2, Noha A El-Shoafy3

  • 1Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El-Shatby, Alexandria, 21526, Egypt. saad.darwish@alexu.edu.eg.

Scientific Reports
|June 3, 2026
PubMed
Summary

This study introduces Type-2 Neutrosophic Sets (T2NS) to enhance automated essay scoring (AES) systems. The novel approach improves scoring reliability by effectively handling uncertainty in educational assessments.

Keywords:
Ontology-Based Uncertain ReasoningSoft Computing under UncertaintyType-2 Neutrosophic LogicUncertain Information ModelingUncertainty-Aware Computing

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Fuzzy Logic

Background:

  • Computing with uncertainty is crucial in domains like decision support and intelligent assessment.
  • Automated Essay Scoring (AES) faces challenges due to linguistic ambiguity and subjective interpretation.
  • Traditional ontology-based AES systems struggle with uncertainty, impacting scoring reliability.

Purpose of the Study:

  • To propose a novel approach integrating Type-2 Neutrosophic Sets (T2NS) into ontology-driven AES frameworks.
  • To address the limitations of traditional AES in handling uncertain, indeterminate, and inconsistent information.
  • To enhance the robustness and interpretability of scoring decisions in automated essay evaluation.

Main Methods:

  • Concept extraction and rule-based scoring using ontologies.
  • Extension of ontology with Type-2 Neutrosophic Sets (T2NS) for uncertainty representation.
  • Application of membership functions to quantify uncertainty levels in essay evaluation.

Main Results:

  • The T2NS-enhanced framework significantly outperforms conventional AES methods.
  • Improved handling of uncertain, indeterminate, and inconsistent information in essay scoring.
  • More robust and interpretable scoring decisions achieved.

Conclusions:

  • Integrating T2NS into ontology-driven AES offers a superior method for managing uncertainty.
  • The proposed framework enhances the reliability and accuracy of automated essay scoring.
  • This approach provides a more sophisticated solution for educational assessment under uncertainty.