This article describes five first-year writing instructors’ experiences with personalizing shared curriculum across three different course delivery formats (face-to-face, hybrid, online). The data is drawn from teaching journals that the co-authors, a non-tenure track, part-time Lecturer and a tenured Writing Program Administrator, and three Graduate Student Teaching Associates completed throughout Fall 2022. The findings illustrate both benefits and drawbacks related to shared curriculum: discussing and troubleshooting curriculum in a community of practice is highly valuable, but separating course delivery from course design is challenging. In our study, those challenges manifested as disconnects between course content and disciplinary identity, as well as personal feelings of failure. On the other hand, the need to personalize shared curriculum across multiple delivery formats proved productive, especially when instructors used asynchronous online materials as a starting point to develop hybrid and face-to-face lesson plans. Ultimately, we advocate for more conversations about how writing programs can support contingent faculty as they personalize shared curriculum through both course delivery and design, and we offer an example of a successful community of practice that revises shared curriculum in response to community members’ experiences with teaching in multiple modalities.

Our First-year Writing program began intentional student engagements with generative AI in the fall of 2022. We developed assignments for brainstorming research questions, writing counterarguments, and editing assistance using the AI tools Elicit, Fermat, and Wordtune. Students felt that the tools were helpful for finding ideas to get started with writing, to find sources once they had started writing, and to get help with counterarguments and alternate word choices. But when given the choice to use the assistants or not, most declined. Generative AI at this stage is unreliable, and many students found the tradeoff in reviewing AI suggestions to be too time consuming. And many students expressed a preference for continuing to develop their own voices through writing. Our experience in engaging AI led to the creation of the DEER praxis, which emphasizes defined engagements with AI tools for specific purposes, and generous use of reflection.

We share our experiences working with large-language model generative AI for a full semester in a professional writing course, integrating it into all projects. We discuss how we adapted our teaching, learning, and writing to using (or purposefully not using) AI. Issues we discuss include balancing integration of AI to avoid potential overreliance, the importance of centering authorial agency and decision-making, negotiating grading and evaluation, the benefits and drawbacks of AI throughout the writing process, and the relationships we build or could build with AI. We close with recommendations for faculty and students.

This article examines the potential uses—and limits—of so-called “distraction-free” writing software, especially in academic writing contexts. It does so by presenting findings from two different qualitative studies, one in which graduate students experimented with such tools and reflected on their experiences, and another study in which undergraduate students composed reflective essays about their writing processes. Taken together, these findings indicate that distraction-free writing may only prove useful within a relatively narrow band of composing activity. Moreover, they suggest that participants’ beliefs and understandings of what constitutes writing activity—and distraction from it—are both broader and more fluid than tacit assumptions embedded in distraction-free writing software. Ultimately, the point is not necessarily to critique this class of software, but instead to use it as an occasion to better understand phenomena related to composing processes, such as attention, distraction, and motivation.

There have been few studies examining the variation that exists within modes of feedback: for example, comparing how electronic text feedback created using Google Docs differs from electronic text feedback created using Microsoft Word or how audiovisual feedback created using TechSmith Capture differs from audiovisual feedback created using Screencast-O-Matic. However, the programs that instructors use to create feedback have different affordances, meaning that even within a single mode, the feedback students receive on their writing can vary significantly. To better understand the variation that exists within a single mode, this study investigates how affordances of Canvas Speedgrader, Google Docs, and Turnitin GradeMark impacted electronic text feedback.Based on analysis of 131 feedback files created using the 3 programs, in conjunction with 5 student surveys, and 2 instructor interviews, the study provides insights into how instructor written commentary (location, form, type, focus, and mitigation) varied by program and how participants perceived of feedback provided through the 3 programs. The study...s primary finding is that the affordances of the programs used to create electronic text feedbackresulted in significant differences ininstructorcommentary and instructor and student perceptions of feedback.