Abstract
The increasing adoption of artificial intelligence (AI) writing tools has transformed academic writing practices among university students. This study investigated the influence of Grammarly AI writing tools on undergraduate students perceived academic writing performance in selected Northern Nigerian universities. Anchored on the Technology Acceptance Model (TAM) and Self-Regulated Learning (SRL) Theory, the study examined the level of Grammarly usage, students perceived academic writing performance, the relationship between Grammarly usage and academic writing performance, and the predictive influence of Grammarly usage on writing performance. A quantitative cross-sectional survey design was adopted. The population comprised 8,450 undergraduate students drawn from three Northern Nigerian universities, from which 392 respondents were selected using a multistage sampling technique. Data were collected using a structured questionnaire validated by experts in Educational Technology, Measurement and Evaluation, and English Language Education. The instrument yielded a Cronbach’s alpha coefficient of .88, indicating high reliability. Data were analysed using descriptive statistics, Pearson Product-Moment Correlation, and linear regression. Findings revealed high Grammarly usage (M = 4.09, SD = 0.83) and high perceived academic writing performance (Grand Mean = 4.15). Grammarly usage was positively related to academic writing performance (r = .56, p < .01) and significantly predicted writing performance (β = .56, p < .001), accounting for 31% of the variance (R² = .31). The study concludes that Grammarly is an effective supplementary writing-support tool and recommends its integration into university writing instruction with appropriate ethical guidance.
Keywords: Artificial Intelligence, Academic
Writing Performance, Automated Writing Evaluation, Grammarly, Undergraduate
Students, Nigeria.
DOI: 10.36349/sojolics.2026.v02i01.005
author/Iorhemen, B. T., Fatai, S., Alhamdu, Y., &Wasagu, S. M.
journal/Sokoto JOLICS 2(1) | June 2026 |
