Writing an English research article for novice English as an additional language (EAL) writers is a challenging task that requires experience and training at both the sentence and meaning levels. One strategy that EAL writers employ when writing a research article is the use of formulaic sequences (FSs). However, available FS corpora are general purpose and are very limited in size. Previous studies have reported the effectiveness of FS usage in writing using a small set of FSs. The present work proposes an assistive environment for academic writing improvement through the use of domain-specific FSs. FSs are extracted from published articles and are classified under rhetoric categories using a machine learning technique. The user can then search for and add new FSs of his/her choice from any research article using proposed prototypes. The effectiveness of the proposed approach was evaluated in a real environment. The results show a positive impact of the proposal in terms of academic writing improvement. Novice writers who worked with the proposed prototype reported a significantly higher degree of perceived usefulness than those who worked with the traditional phrasebank approach.