This dissertation explores the multifaceted development of persuasive writing skills among secondary school students, with an emphasis on fostering reasoning and argumentation skills essential for targeted writing tasks. Student-constructed essays serve as valuable instruments for assessing scientific literacy and higher-order thinking; however, their evaluation involves many complexities and is susceptible to potential biases and various sources of measurement error. This study addressed two primary objectives. First, it identified key features influencing persuasive essay quality by utilizing product-oriented measures to analyze both microstructural and macrostructural dimensions of writing. A latent structure of writing assessment was established, and measurement invariance was tested across students with differing special education statuses. Second, building on insights from this foundational investigation, automated feedback prompts were developed and implemented using GPT, facilitating AI-based scoring and feedback mechanisms. This research underscores the relevance of factor-analytic findings in addressing gaps related to predictive models and AI-driven content-generation tools, ultimately supporting personalized learning and adaptive written feedback. The implications of this study align with AI’s expanding role in education, offering strategic insights into maximizing AI’s utility for enhancing educational equity and instructional effectiveness. A promising approach involves leveraging factorial models to inform generative AI in delivering tailored feedback, thereby enhancing students’ writing proficiency and overall performance.
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