With respect to AI writing technologies (AIWT), we pose three foundational questions about academic dishonesty. First, do writing instructors and students perceive differences between AI agents and human agents in classroom scenarios? Second, to what extent are writing instructor and student perceptions are aligned? Third, what types of writing scenarios are perceived as academic dishonesty? Answering these questions provides a baseline of comparison not only for future studies of AIWT collaboration but also contextualizes perceptions of human-to-human collaboration. We report on a large-scale experimental survey study that answers these questions using item response theory (IRT). Our findings demonstrate that while there are differences between AI and human agents of collaborations, writing instructors and students are generally aligned in their perceptions. Using a Rasch model, we find that academic dishonesty operates along a spectrum of textual production. Regardless of whether the collaborating agent is human or AI, the more an agent produces text, the more this collaboration is perceived as academic dishonesty. Conversely, the less text that is produced, the less this scenario is perceived as academically dishonest. In our discussion, we provide a data-driven heuristic to guide instructors and administrators.

• AI can offer useful writing feedback when used combined with peer review. • AI and peer responses were often similar and mutually reinforcing. • When AI and peer responses differed, the perspectives were often complementary. • Evaluating AI feedback fostered student agency and AI literacy. Cycles of drafting and revising are crucial for student writers' growth, and formative assessment plays an important role. However, many teachers lack the time or resources to provide feedback on drafts. While research suggests that AI feedback is high enough quality to be used for draft feedback, especially when assignment-specific criteria are used (Steiss et al., 2024), it must be used in a human-centered process. AI has the potential to reduce educational equity gaps in writing support (Warschauer et al., 2023), but when narrowly implemented, technologies can deepen divides (Stornaiuolo, et al., 2023). Peer and AI Review + Reflection (PAIRR) combines peer review best practices with AI review in an approach that emphasizes student agency and reflection. Using a mixed methods approach, this study examined student perceptions of AI utility in the context of peer review. Results indicate that AI tools offer useful feedback when combined with peer review. Students found the similarity between AI and peer feedback reassuring, while also valuing their complementary perspectives. Moreover, by evaluating AI outputs, students developed AI literacy, gaining familiarity with AI feedback's affordances and limitations while learning ethical ways to use AI in their writing processes.

In this paper, I explore first-year students' self-reported preferences for choosing source material in a digital, research-based writing setting. I argue that widespread skepticism towards online information has led to an "objectivity bias," where students prefer sources perceived as neutral and objective. Through qualitative interviews, I report that this bias may result in an overreliance on data-driven and empiricist sources, often at the expense of valuable personal narratives and experiential knowledge. I highlight the role of digital platforms and search algorithms in shaping these preferences and discuss the implications for teaching information literacy.