ChatGPT and other LLMs are at the forefront of pedagogical considerations in classrooms across the academy. Many studies have spoken to the technology’s capacity to generate one-off texts in a variety of genres. This study complements those by inquiring into its capacity to generate compelling texts at scale. In this study, we quantitatively and qualitatively analyze a small corpus of generated texts in two genres and gauge it against novice and published academic writers along known dimensions of linguistic variation. Theoretically, we position and historicize ChatGPT as a writing technology and consider the ways in which generated text may not be congruent with established trajectories of writing development in higher education. Our study found that generated texts are more informationally dense than authored texts and often read as dialogically closed, “empty,” and “fluffy.” We close with a discussion of potentially explanatory linguistic features, as well as relevant pedagogical implications.

Recently, formative feedback in writing instruction has been supported by technologies generally referred to as Automated Writing Evaluation tools. However, such tools are limited in their capacity to explore specific disciplinary genres, and they have shown mixed results in student writing improvement. We explore how technology-enhanced writing interventions can positively affect student attitudes toward and beliefs about writing, both reinforcing content knowledge and increasing student motivation. Using a student-facing text-visualization tool called Write & Audit, we hosted revision workshops for students (n = 30) in an introductory-level statistics course at a large North American University. The tool is designed to be flexible: instructors of various courses can create expectations and predefine topics that are genre-specific. In this way, students are offered non-evaluative formative feedback which redirects them to field-specific strategies. To gauge the usefulness of Write & Audit, we used a previously validated survey instrument designed to measure the construct model of student motivation (Ling et al. 2021). Our results show significant increases in student self-efficacy and beliefs about the importance of content in successful writing. We contextualize these findings with data from three student think-aloud interviews, which demonstrate metacognitive awareness while using the tool. Ultimately, this exploratory study is non-experimental, but it contributes a novel approach to automated formative feedback and confirms the promising potential of Write & Audit.