Nowadays ICT competence has become one of the issues that determines skills and knowledge learners should obligatory acquire as it plays a major role in knowledge generation, information extraction and text processing. Information extraction from English scientific and technical texts is a complicated process due to the presence of rhetorical elements belonging to other genres such as, for example, terms based on metaphoric meaning extension, metonymic proper names, and other stylistic devices. The challenges are also associated with decoding of meaning of intra-disciplinary and cross-disciplinary polysemic terms, as well as culture-specific items. Taking into account the advancement of ICT in any field of knowledge, text processing tools are able to perform multiple functions, such as classifying texts according to genres and functions, distinguishing polysemic items, as well as interpreting different models of meaning extension. But, at the same time, the ability of computer to search for the inquired information depends on the ability of the human operator to perform cognitive analysis, set the criteria, frame the content, and thus predict the results. The present paper aims to identify the main aspects of ICT competence in second language acquisition, in general, and in teaching academic (research) writing, in particular. To improve written communication skills, it is necessary to develop learners’ conscious understanding of conventions of academic text creation and language resources, which should be used to convey the intended message. At present, to be proficient in academic writing has become even a more challenging task due to the constantly growing volume of information, the intertextual character and multimodal nature of a modern LSP text. In relation to the second language teaching, academic writing is seen as a purposeful and situated social practice introduced by genre pedagogy. Writing the research work, learners should also become more proficient in the use of computer-assisted tools that address such issues as, for example, text processing, information mining, terminology management, pre-editing, post-editing, and proofreading.