Abstract
In this work, the authors provide a novel framework for the effectiveness of AI writing assessment systems by embedding state-of-the-art deep learning networks, user feedback mechanisms, and knowledge graph frameworks. Most writing assessment tools cannot give personalized, detailed feedback. To tackle this problem, we employ writing assessment transformer models BERT and GPT-3, which allow exploring and scoring the writing on various features, including phrase structure, semantics, vocabulary usage, etc. In our system, we propose a dynamic relational knowledge graph that incorporates writing concepts and their relations, making it easier for the system to devise contextualized thesaurus-wise suggestions. The addition of graph neural networks (GNNs) empowers the model by boosting the GNN's learning ability regarding the knowledge graph and improving comprehension of complex semantics. Additionally, we have included an iterative design whereby user feedback is collected, and the system adjusts the feedback given in light of historical feedback and changes in a user's writing behavior over time. The system reconceptualizes the problem of user AI interaction by incorporating its dynamic nature and movement towards the known user and not vice-versa, achieving higher efficiency. To assess user satisfaction and improvements in the quality of the prepared texts, the authors conduct a series of user studies evaluating the efficiency of this integrated system. However, the preliminary data obtained from the task performance analysis show that the results of the proposed framework are far better than those of traditional methods, achieving a better level of engagement and feedback while performing the assessment. This study underscores the potential of deep learning, feedback, and knowledge graph integration in leveraging writing education. It can potentially reform learners' capabilities, enabling them to write better and more effectively.