Exploring the writing hypothesis using artificial intelligence

Abstract

This study investigates innovative approaches to recording and analyzing the early stages of literacy development, where children begin to graphically represent their oral expressions. The primary contribution is the development of an artificial intelligence (AI)-powered computational tool designed to identify and document students’ writing progression. By leveraging machine learning techniques, the system provides educators with detailed, systematic, and frequent records, facilitating more informed instructional strategies. The research follows an experimental methodology, analyzing a dataset of 2,000 writing samples from early learners across diverse educational settings. To optimize the AI model’s effectiveness, a specialized dataset of word images in Portuguese was developed, varying in syllable count and standardized to 224×224 pixels resolution for consistency in training. The study employs both quantitative and qualitative data analysis techniques, integrating statistical evaluation and pattern recognition algorithms to assess student writing proficiency. Findings indicate that the proposed system enables more precise and efficient tracking of literacy development, allowing for early identification of potential learning difficulties. This advancement in AI-driven assessment provides educators with actionable insights, enhancing intervention strategies and supporting personalized learning. Future research could explore expanding the tool’s functionality to accommodate a wider range of educational contexts, further strengthening its role in literacy education.