Can We Trust AI to Assess Writing? An Analysis of Scoring Reliability and Feedback Consistency - Fitriani, Fitriani; Eko Rini, Puput Zuli (Universitas Negeri Semarang, 2025)
This study analyzes AI-generated writing assessments' scoring reliability and feedback consistency using ChatGPT. Adopting a mixed-methods approach, 23 student descriptive texts were evaluated across three assessment rounds. Quantitative findings showed high scoring reliability, with an Intraclass Correlation Coefficient (ICC) of 0.93, indicating excellent consistency across repeated evaluations. Qualitative analysis revealed that ChatGPT consistently addressed five core writing criteria—content, organization, vocabulary, language use, and mechanics. However, the feedback varied in focus and detail across rounds, and the absence of reference to prior feedback limited its support for revision as a recursive process. The findings suggest that although ChatGPT demonstrates reliable scoring and generally stable feedback themes, it lacks the continuity to facilitate sustained writing development. To enhance its pedagogical value, AI-based feedback systems should be designed to build upon previous responses, thereby enabling more effective support for students' progressive improvement in writing.