In the landscape of literacies assessment, automated essay scoring (AES) emerges as a transformative technology, offering efficient evaluation of writing proficiency through automated algorithms. AES works by analyzing various linguistic features and patterns within essays, comparing them against pre-established scoring rubrics or models. These algorithms assess factors such as vocabulary usage, sentence structure, coherence, and argumentative depth to generate scores. The strengths of AES are multifaceted: firstly, it enables rapid and consistent evaluation of large volumes of essays, saving educators valuable time and resources. Additionally, AES provides immediate feedback to students, fostering timely reflection and improvement in writing skills. Moreover, AES can accommodate diverse writing tasks and genres, offering flexibility in assessment across different curricular areas. However, AES also faces several challenges. Critics argue that it may struggle to accurately capture nuanced aspects of writing, such as creativity, originality, and rhetorical sophistication, leading to potential biases in scoring. Additionally, the reliance on algorithmic evaluation raises concerns about fairness and transparency, as students may not fully understand the criteria used for assessment. Furthermore, AES may encounter difficulties in assessing non-traditional forms of writing, such as poetry or experimental prose. Despite these challenges, AES represents a valuable tool for enhancing literacy assessment practices, particularly in providing efficient feedback and scalability. Moving forward, continued refinement of algorithms and greater transparency in scoring criteria will be crucial for maximizing the effectiveness and equity of AES in evaluating writing proficiency across diverse educational contexts.