From Grammarly to GPT: Exploring the Use of AI-Assisted Academic Writing Tools Among Doctoral Researchers

Abstract

The rapid integration of artificial intelligence (AI) into academic writing has reshaped research practices in higher education, particularly at the doctoral level, where advanced writing, critical thinking, and scholarly autonomy are essential. This study investigates Algerian doctoral students’ engagement with AI-assisted academic writing tools, focusing on their familiarity, usage patterns, perceived benefits, and ethical concerns. Adopting a mixed-methods approach, data were collected through an online questionnaire administered to 30 PhD students specializing in English language–related disciplines across various Algerian universities. Quantitative data examined tool awareness, frequency of use, and perceptions of effectiveness, while qualitative responses provided insights into ethical considerations and recommended practices. The findings reveal universal awareness of AI writing tools, with ChatGPT, Grammarly, and QuillBot emerging as the most frequently used applications. Participants primarily employed these tools for grammar correction, paraphrasing, idea generation, and organizational support. Overall perceptions were predominantly positive, highlighting improvements in writing quality, efficiency, and cognitive support. However, notable ethical concerns were expressed regarding academic integrity, overreliance, authorship boundaries, and potential impacts on critical thinking and independent writing development. Importantly, all participants emphasized the need for clear institutional guidelines and formal training to ensure responsible and transparent AI use. The study concludes that while AI tools offer substantial pedagogical value as assistive resources in doctoral academic writing, their integration must be guided by ethical frameworks, critical engagement, and institutional policy. These findings contribute to the growing discourse on AI in higher education and underscore the necessity of balanced, discipline-sensitive approaches to AI adoption in advanced academic contexts.