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Optimising AI writing assessment using feedback and knowledge graph integration.

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  • 1Wenzhou University, Wenzhou, China.

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This study introduces an AI writing assessment framework using deep learning and knowledge graphs to provide personalized feedback. The novel system significantly improves writing quality and user engagement compared to traditional methods.

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AI writing assessmentDeep learningKnowledge graphNatural language processingUser feedback

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Educational Technology

Background:

  • Traditional writing assessment tools often lack personalized and detailed feedback capabilities.
  • There is a need for advanced AI systems to enhance writing education and provide tailored guidance.

Purpose of the Study:

  • To develop and evaluate a novel AI framework for writing assessment that integrates deep learning, user feedback, and knowledge graphs.
  • To improve the personalization, detail, and effectiveness of AI-driven writing feedback.

Main Methods:

  • Utilized state-of-the-art deep learning transformer models (BERT, GPT-3) for writing analysis.
  • Implemented a dynamic relational knowledge graph with graph neural networks (GNNs) for contextualized suggestions.
  • Incorporated an iterative user feedback loop for adaptive system adjustments.

Main Results:

  • The proposed AI framework demonstrated superior performance over traditional methods in writing assessment.
  • Preliminary studies indicated enhanced user engagement and feedback quality.
  • The system showed significant improvements in the quality of user-generated texts.

Conclusions:

  • The integration of deep learning, feedback mechanisms, and knowledge graphs offers a powerful approach to writing education.
  • This AI framework has the potential to significantly enhance learners' writing skills and effectiveness.
  • The dynamic and adaptive nature of the system redefines user-AI interaction for improved outcomes.