# Automatic Essay Evaluation Technologies in Chinese Writing--A Systematic Literature Review
## 5 Conclusions  
This article conducted a systematic literature review of 29 high-quality research works related to ACEE technology published between 2004 (the widely acknowledged year of publication for the first ACEE literature) and 2022. As critical technological issues, the development of corpus construction, feature engineering, and evaluation models collectively contribute to the advancement of ACEE. In summary, over the past decade, ACEE has made substantial progress in research on text preprocessing, feature extraction, and evaluation models. In recent years, multiple ACEE products have been applied in practice. However, research in this field is still in its early stages, with fragmented studies and fewer systematic or inherited research efforts. Chinese writing exhibits diverse expression patterns and a complex structure, which differ significantly from English in terms of vocabulary construction, sentence organization, and discourse structure. Hence, selecting and extracting features that determine the quality of Chinese essays is inherently more complex. Its application in the field of education is highly promising, and its development requires the integration and advancement of multiple related fields such as Chinese linguistics, pedagogy, and computer science. The ACEE research community and platforms are emerging, calling for broader participation from researchers. Looking ahead, there are several avenues for future research and development in ACEE:  
* Corpus Expansion: To enhance the effectiveness of ACEE models, creating more extensive and diverse Chinese essay corpora is imperative. Researchers should collaborate to build and share such corpora, facilitating the development of more robust ACEE systems. * Language-specific Challenges: Researchers should address language-specific challenges related to Chinese writing, such as handwriting quality, idiomatic expressions, prosody structure, etc. These aspects require dedicated attention to ensure accurate evaluation. * Deep Learning and Neural Networks: Given the success of deep learning models in English AEE, further exploration and adaptation of these techniques to Chinese AEE may yield significant improvements. * Data efficiency: The effectiveness of machine learning technologies in ACEE relies heavily on training data volume and diversity. Building comprehensive corpora that represent diverse writing scenarios is expensive and labor-intensive. Data efficiency enhancement is vital. * Multidimensional scoring: Multidimensional scoring in ACEE involves assessing various aspects of essays, providing more detailed feedback to students, and aiding educators in targeted teaching. This approach comes with challenges like determining the correlation between features and various scoring dimensions and the interaction between features, as well as model development. * Stability and generalization: Ensuring the stability and generalization of ACEE scoring models is vital for reliable, unbiased essay evaluation. It must consistently assess various tasks and student profiles, adapting to evolving educational needs while addressing potential biases. This ensures ACEE's versatility and trustworthiness as an equitable essay assessment tool. * Collaborative Platforms: The emerging ACEE research community and platforms should actively foster collaboration among researchers. Encouraging knowledge-sharing, joint projects, and creating open-source tools will accelerate progress. In conclusion, while ACEE has made significant strides, it is clear that there is ample room for growth and collaboration. The challenges that are unique to evaluating Chinese essays demand creative solutions and collective efforts from the research community. By addressing these challenges and embracing future opportunities, ACEE can further evolve to meet the needs of educators, learners, and the broader Chinese language community. Conceptualization, H.Y. and Y.H. ; formal analysis, H.Y. and Y.H. ; data curation, Y.H., X.B., H.X. and W.G. ; writing--original draft preparation, Y.H.