Aug 2026· Nurse Education Today· Vol 167, pp.
107326
· 0 citations· 15 references
Medicine
TL;DR
This paper begins from the author's own practice and observations among colleagues, then examines three sets of questions raised by faculty AI use that have not yet been adequately engaged in the nursing literature.
Abstract
Generative AI has entered nursing education rapidly and pervasively. While considerable scholarly attention has been directed toward student use of these tools, including concerns about academic integrity, citation fabrication, and the design of AI-resistant assessments. Far less attention has been paid to faculty practice. Yet many of us now routinely use generative AI in the work of teaching itself: generating grading rubrics, simplifying assignment instructions, aligning course materials to program outcomes, drafting feedback on student work, and refining policy language. This practice has spread without institutional guidance, without faculty disclosure norms, and with little professional conversation about what it means. This paper invites that conversation. It begins from the author's own practice and observations among colleagues, then examines three sets of questions raised by faculty AI use that have not yet been adequately engaged in the nursing literature. First, what is actually transferred when faculty paste assignments, rubrics, scenarios, and student work into commercial AI systems, and how do those transfers align, or fail to align, with the data practices of the platforms involved? Second, on whose behalf do faculty make these disclosure decisions, given that course materials embed the contributions of colleagues, clinical partners, prior developers, and students? Third, what does it mean for the development of nursing pedagogy when work once central to the formation of educators, including assessment design, feedback, and curricular alignment, is increasingly mediated through commercial generative tools? The paper does not argue that faculty should stop using generative AI. It argues that we should subject our own practice to the same reflection we have begun to ask of our students, and it offers a starting framework for that collective examination.
Abstract In Spring 2025, librarians at a regional comprehensive university in Southwest Florida conducted focus groups with faculty and students to explore their perceptions and experiences with artificial intelligence (AI). Previous research suggests that both groups recognize potential benefits of AI for learning, teaching, and productivity while expressing concerns regarding misinformation, ethics, and future societal impacts. The purpose of this study was to explore faculty and student perceptions of AI knowledge, training, and ethical use within a higher education context. Using a phenomenological approach, researchers conducted separate focus groups with faculty and students and analyzed the resulting discussions for recurring themes. Faculty participants expressed concerns related to critical thinking, institutional guidance, access to practical training, and adapting instruction to an AI -enabled environment. Student participants emphasized misinformation, future employment, and broader societal impacts of AI. Participants in both groups viewed AI as a tool that could be used ethically when applied appropriately. Although limited by its small sample size and single-institution context, the findings suggest opportunities for AI literacy initiatives and continued discussion regarding ethical AI use in higher education.
Em Farmer, Rachel Tait-Ripperdan, R. Cooke· Research on Education and Me...· 0 citations
Assessment strongly shapes student learning and engagement. Traditional assessment rubrics in health and medicine education often fail to account for emerging challenges posed by artificial intelligence. The use of AI tools risks encouraging surface-level learning and undermining academic integrity if not addressed through purposeful assessment design. This study evaluates the impact of transitioning from a traditional to an AI-aware rubric in a Health and Medicine course, examining how this shift influenced educators’ grading practices, instructional strategies, and perceptions of student engagement.
A qualitative research design employed semi-structured interviews with three educators who had taught the course using both original and revised rubrics. As the complete teaching team, their perspectives captured the experience of every educator who taught the course. Interview data were analysed using thematic analysis, with themes cross-referenced against the Twelve Tips for Designing AI-Resilient Assessments framework.
Analysis revealed five overarching themes: perceived transformation in student learning and critical thinking; refinement of assessment practices and expectations; expansion of teaching strategies and curriculum design; implementation challenges and assessment authenticity; and institutional readiness and professional development. Educators perceived that the revised rubric supported deeper student engagement with AI, increased their grading confidence, and prompted shifts in instructional approaches.
AI-aware rubric design can meaningfully reshape both teaching and assessment practices. However, sustainable implementation requires institutional support, structured professional development, and clearer policy guidance on acceptable AI use. The study also refines the Twelve Tips framework, positioning it as a flexible design heuristic rather than a prescriptive checklist. These findings are particularly relevant for undergraduate health professions programs navigating rapid educational and technological change.
Suzanne Estaphan, Tehzeeb Zulfiqar· Frontiers in Education· 0 citations
The rapid integration of generative Artificial Intelligence (AI) into higher education writing instruction is outpacing pedagogical frameworks, creating profound disruptions in how writing is taught, assessed, and valued. While AI shifts writing from individual production to collaborative human–AI processes, instructors face escalating challenges, including the erosion of traditional authorship, uncertainty in evaluating AI-mediated work, threats to assessment validity, and growing student dependency on AI tools. These tensions expose a widening gap between technological adoption and pedagogical preparedness, placing faculty at the center of unresolved ethical, instructional, and institutional dilemmas. This systematic review synthesizes empirical research published between 2023 and 2025 on generative AI (e.g., ChatGPTand other GPT-based systems) in higher education writing instruction. Following PRISMA guidelines and SPIDER framework, 19 peer-reviewed studies were analyzed. Findings suggest that AI can enhance drafting, revision, and feedback processes, improving coherence, metacognition, and writing confidence. However, these benefits are accompanied by persistent concerns regarding ethical ambiguity, inconsistent policy guidance, and insufficient faculty training. The review contributes theoretically by reconceptualizing writing pedagogy for AI-mediated processes and practically by providing guidance for instructional design, assessment strategies, and institutional policy. Gaps identified include a lack of longitudinal studies, limited exploration of faculty perspectives, and inconsistent integration of AI literacy and ethical considerations. Implications for research, practice, and policy are discussed.
Samira Dichari, Fadi Jaber· Journal of Education and Tra...· 0 citations
To effectively integrate AI into education, university instructors need to have AI-specific technological, pedagogical, and content knowledge (TPACK). Research indicates that university instructors often feel unprepared with these competencies; thus, they lack the knowledge to effectively integrate AI tools into their teaching practices. While substantial literature exists on AI in education, there remains a significant gap in faculty-centered research, particularly regarding instructor knowledge, comfort, and ethical understanding of AI implementation in higher educational contexts. This study examined faculty perceptions regarding their AI-TPACK knowledge, ethics-based AI knowledge, and then assessed whether those perceptions were influenced by training in AI tools. Using a quantitative descriptive cross-sectional research design that is based on Celik’s TPACK framework, this empirical research examined faculty members’ self-perceived competencies in each area. The study found that AI-focused professional development significantly enhanced faculty knowledge and comfort with AI integration among the 100 faculty members who participated in the study. Also, the findings revealed a critical gap in ethical AI knowledge, underscoring the urgent need for ethics-specific training to build faculty confidence in classroom implementation. Universities should leverage these findings to offer targeted professional development initiatives that close identified gaps and improve instructional effectiveness and faculty digital literacy skills.
Jaime Januse, S. Jindal, Stephen Gregoire· AI-Enhanced Learning· 0 citations
While generative AI tools are directly changing how undergraduate computer science is learned and taught, they are also reshaping the relationships between instructors and students. In contrast to existing tool-oriented research on how instructors view and adopt AI, this study investigates how instructors think about their roles and responsibilities to students through their course AI policies. Based on 13 semi-structured interviews with CS instructors in the US, we found that while instructors recognize that AI tools could harm student learning, AI policies primarily seek to AI-proof assessments without directly addressing student learning. Although policies such as switching to paper exams can preserve assessment integrity in the short term, instructors report extra burden of policing student AI use behaviors and worsening relationships with students. Based on the experiences of several interviewees, we make recommendations on AI policies that are more learning-oriented and could guide students toward healthier AI usage instead.
Aim/Purpose
While student evaluations of instruction remain the most common mechanism for evaluating teaching effectiveness, they remain controversial and are plagued by criticisms of bias.
Background
The Assessment Council at a Mid-Atlantic Historically Black College or University (HBCU) has determined that student evaluations of instruction provide feedback for instructors, give students a voice, and help improve instruction and course efficacy and that the information collected from SETs can be used for formative evaluation, helping faculty and programs make adjustments to improve instructional design and delivery, and to help inform acknowledgements of teaching excellence as well as decisions about promotions, tenure, and compensation. Accordingly, a task force was formed of students and faculty challenged with crafting a new student evaluation of instruction instrument built around three areas of focus: Teaching Excellence, Course Design and Clarity, and Inclusive Learning. A draft fifteen question instrument was prepared and widely distributed for feedback before being piloted in the summer and fall of 2025. The data was subsequently downloaded and imported into SPSS where Cronbach’s Alpha and Intraclass Correlation Coefficients were calculated with results indicating that the instrument's items are internally consistent and reliable.
Contribution
This presentation will provide information on a minority serving institution's work to redesign and validate a new student evaluation of instruction instrument to be more reflective of instructional quality and which is part of a larger framework used to evaluate teaching excellence that includes annual evaluations, dossier preparation for post tenure review and/or promotion and tenure, peer review, and consideration against predetermined and discipline relevant activities/expectations.
Nicole A. Buzzetto-Hollywood· InSITE Conference· 0 citations