Scholars have increasingly focused on the use of GenAI

The gap

Evidence profile

Stated in the abstract and recommendations sections of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 3 journals. Those papers have been cited 86 times in total.

Supporting evidence — 3 representative gaps

• Using generative artificial intelligence/ChatGPT for academic communication: Students' perspectives (2024) · International Journal of Applied Linguistics · cited 86× · doi

  While scholars have increasingly focused on the use of GenAI in higher education since its inception, little is known about how key higher education stakeholders, particularly students, perceive its impact on teaching and learning within the context of academic communication, an area central to students' development of transferable skills and literacy competencies yet heavily influenced by the technology.

  generalstated in abstractevidence 5/5
  Keywords: higher education students scholars increasingly focused genai inception little known stakeholders particularly perceive impact teaching
• “AI Should Help them Learn, Not Learn for Them”: University Staff Perspectives on the Role of Generative AI in Education (2026) · Journal of University Teaching and Learning Practice · doi

  To effectively navigate the integration of GenAI into higher education, a comprehensive institutional strategy is essential. This strategy should include the development of clear guidelines, targeted training, and structured support for both staff and students (Fawns, 2022; Kutty et al., 2024; Sharples, 2023; Siemens, 2005). Within this context, a broader pedagogical discussion around GenAI use in learning and teaching is necessary, extending beyond resource provision and assessment design. A rethink of pedagogy that addresses the dynamic changes in educational practice is required. Importantly, these theoretical perspectives help illuminate the staff concerns identified in the findings, particularly those relating to integrity, workload pressures, uncertainty about appropriate use, and shifting expectations of academic labour. When viewed through a broader post digital and sociotechnical lens, these concerns become more intelligible, especially the tensions evident in staff reflections about assessment stability, confidence in working with GenAI, and the need for clearer pedagogical direction. This framing directly informs the strategies proposed below. For example, drawing on entangled pedagogy (Fawns, 2022; Siemens, 2005) we might consider how a new cooperative social digital learning model (Sharples, 2023) could shape our practice. This model could incorporate elements of self-regulated digital learning (Jin et al., 2023), co- regulated learning (Lodge et al., 2023) and explore the broader conversations of educational pedagogy and digital collaboration (Sharples, 2023). We should recognise AI in assessment as an ever-developing condition to be navigated (Corbin et al, 2025) whilst firmly prioritising accessibility and inclusion (Kelly et al., 2023). Building on this educational approach, an institutional GenAI educational framework (Hillier, 2023) would be beneficial including a whole institution program level review of our assessment practices (TEQSA, 2024). This would include a rethink of our curriculum approach to incorporate programmatic approaches (Baartman & Quinlan, 2024), evaluative assessment design (Bearman et al., 2024) and visible learning integration (Bearman et al., 2024) ensuring our learning and teaching strategies incorporate GenAI literacy and student social collaboration at its core. Practical strategies for staff in mitigating AI risk through visible assessment design and an AI integrated approach to assessment regimes would support academics currently navigating this challenging landscape. We need to be mindful of the complex interplay between efficiency and quality, enthusiasm and risk. Whilst universities face huge tensions in transforming educational practice given this complexity, we need to acknowledge the ‘wicked problem’ of AI which we must all navigate rather than solve (Corbin et al., 2025).

  generalstated in recommendationsevidence 5/5
  Keywords: assessment learning genai educational staff digital sharples broader design pedagogy practice need strategies incorporate approach
• The Affective Impact of Generative Artificial Intelligence on Instructors of First-Year Writing Courses (2026) · Teaching & Learning Inquiry The ISSOTL Journal · doi

  In response to our findings, we offer three preliminary recommendations for institutions and departments to address the affective impacts of GenAI on FYW instructors. The first involves a call to action to 1) provide clearer guidelines on addressing potential cases of unauthorized use of GenAI in written submissions, thereby reducing added labour and allocating some protections to vulnerable faculty, and 2) deliver guidance on conducting exploratory meetings with students in suspected cases of unauthorized GenAI use. For example, workshops on standards of evidence in academic codes of conduct and how they relate to suspected unauthorized GenAI use would alleviate the burden that Caroline identified in her attempts to “wade through” the “big, long, legal material” of institutional academic integrity policies in order to determine what approach to take in cases of suspected GenAI infractions. She proposed that a “forum like a lecturer question and answer” may better suit the needs of faculty. A second recommendation is that institutions expand the range of supports offered to faculty in the wake of GenAI, specifically addressing the emotional and interpersonal pedagogical effects of the technology. Whereas workshop sessions on integrating GenAI tools into teaching and assignment design proliferate, guidance on addressing issues of trust-building in the TSR would constitute valuable additions. A final recommendation comes from an unexpected finding: Five out of eight participants expressed gratitude for the chance to discuss the emotional consequences of the introduction of Paxton, Amanda, Phoebe Kang, and Ashley Yim. 2026. “The Affective Impact of Generative Artificial Intelligence on Instructors of First-Year Writing Courses.” Teaching & Learning Inquiry 14: 1–17. https://doi.org/10.20343/teachlearninqu.14.19 12 THE AFFECTIVE IMPACT OF GENERATIVE ARTIFICIAL INTELLIGENCE ON INSTRUCTORS OF FIRST-YEAR WRITING COURSES GenAI, sometimes voicing surprise at the “cathartic” (Gareth) effect of the interview. Filomena joked, “I feel like I should pay you, because you’re listening to me vent.” The opportunity to speak with colleagues about lived experience in this turbulent moment could be made available at minimal cost to the institution and would serve the collective good. We therefore recommend formal support for communities of practice in which instructors can share their experiences with each other for mutual support and acquire guidance from experts. This suggestion aligns with the findings of Kohnke, Di Zou, and Moorhouse (2024), who identified the value of communities of practice among English language instructors as they navigate the emergence of GenAI. FUTURE DIRECTIONS FOR RESEARCH In future research, we aim to expand our dataset by including a larger and more diverse group of writing instructors from various post-secondary institutions.

  generalstated in recommendationsevidence 5/5
  Keywords: genai instructors institutions affective first addressing cases unauthorized faculty guidance suspected writing academic identified like

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