Mapping of Cognitive and AI Models for Written Communication
Shi Chen
International Journal of Computational Linguistics Research, Vol. 17, No. 1, 2026.
DOI: https://doi.org/10.6025/ijclr/2026/17/1/35-53
Link: https://www.dline.info/jcl/fulltext/v17n1/jclv17n1_3.pdf
Abstract:
This study explores the integration of cognitive science and artificial intelligence to enhance English writing
instruction and assessment. It frames writing as a complex, recursive cognitive process involving planning,
generation, and revision mirroring functional mechanisms in modern AI systems, such as large language
models (LLMs). The paper proposes a conceptual framework that aligns human cognitive models with AI
architectures through components such as goal representation, memory (long-term and working), attentionbased
context handling, and feedback driven revision. This alignment is formalized mathematically using
state transition systems and probabilistic generation models.
An intelligent writing system was implemented in a 12-week online doctoral course (N=22) to support idea
generation and coherence development. Text coherence was measured via average degree centrality in
graph based representations. Experimental results showed that the proposed feature selection method
significantly outperformed LSTM across training set sizes (85-99% vs. 40-90% accuracy), particularly with
limited data. Adversarial training further improved robustness. Frequent subgraph analysis enabled effective
discrimination between coherent and incoherent texts, with low performing subgraphs filtered to preserve
reliability. The study concludes that hybrid AI-cognitive models enhance writing quality, engagement, and
efficiency while underscoring the need for ethical, explainable, and human centred AI design in educational
contexts.