Assessing Writing Outcomes: A Comparative Study of Artificial Intelligence Versus Traditional Assessment Techniques
Assessing Writing Outcomes: A Comparative Study of Artificial Intelligence Versus Traditional Assessment Techniques
Yasamiyan Alolaywi
TLDR
It is suggested that AI can be a valid and effective supplementary tool for evaluating student writing and recommended that educators consider integrating AI into their assessment processes to enhance accuracy and effectiveness.
Abstract
This study investigated the differences between Artificial Intelligence (AI) and traditional assessment techniques in evaluating university students’ writing assignments, focusing on grammar, spelling, vocabulary, article usage, and writing mechanics. The research aimed to assess the accuracy of both methods, save instructors’ time, and address a gap in the literature. Fifty freshmen university students enrolled in a writing class (AHEE 101) at Qassim University participated. A mixed-methods comparative design was employed, where students’ writing was evaluated using both traditional grading and the AI tool QuillBot. Statistical analysis, specifically t-tests, showed no statistically significant difference in the overall writing scores between the two assessment methods (p > 0.05). However, a detailed comparative analysis of individual writing components (grammar, spelling, vocabulary, article use, punctuation, and capitalization) revealed that the AI tool demonstrated a notable advantage in error detection accuracy. These findings suggest that AI can be a valid and effective supplementary tool for evaluating student writing. The study recommends that educators consider integrating AI into their assessment processes to enhance accuracy and effectiveness. Future research should include longitudinal studies to assess the long-term impact of AI-based feedback on students’ writing development.