GenAI has demonstrated functionality that seems, uncannily, to parallel reading and writing by identifying/reformulating information from source texts and generating novel content and argumentation. These skills are essential yet challenging for many students tasked with producing literature reviews. This study takes the first steps to investigating the feasibility of a GenAI-facilitated literature review. This investigation starts from the ‘human-in-the-loop’ position that complex processes can be deconstructed and compartmentalized, and that component functions needed for these processes can be delegated to machines while humans contribute to, or control, the overall process. We explore the hypothesis that certain functions of the literature review process, such as information extraction and content classification, might be able to be automated. Prompts modeled on recommended practices for research synthesis were designed to identify and classify particular types of content in research articles. Outputs produced by two GenAI models, GPT-3.5 and GPT-4o, were assessed for reliability with a human coder. Overall, the results posit concerns about the models’ performance on this task, cautioning against direct uses of GenAI output as learning scaffolding for students developing literature review skills.

Producing publishable quality research articles is a difficult task for novice scholarly writers. Particularly challenging is writing the Discussion/Conclusion section, which requires taking evaluative and interpretive stances on obtained results and substantiating claims regarding the worth of the scholarly contribution of the article to scientific knowledge. Conforming to the expectations of the target disciplinary community adds another dimension to the challenge. Corpus-based genre analysis can foster postgraduate writing instruction by providing insightful descriptions of rhetorical patterns and variation in disciplinary discourse. This paper introduces a pedagogically-oriented cross-disciplinary model of moves and steps devised through top-down corpus analysis. The model was applied to pedagogical materials and tasks designed to enhance genre and corpus-based teaching of Discussion/ Conclusions with an explicit focus on rhetorical conventions.

This article aims to engage specialists in writing pedagogy, assessment, genre study, and educational technologies in a constructive dialog and joint exploration of automated writing analysis as a potent instantiation of computer-enhanced assessment for learning. It recounts the values of writing pedagogy and, from this perspective, examines legitimate concerns with automated writing analysis. Emphasis is placed on the need to substantiate the construct-driven debate with systematic empirical evidence that would corroborate or refute interpretations, uses, and consequences of automated scoring and feedback tools intended for specific contexts. Such evidence can be obtained by adopting a validity argument framework. To demonstrate an application of this framework, the article presents a novel genre-based approach to automated analysis configured to support research writing and provides examples of validity evidence for using it with novice scholarly writers.