Higher education news for libraries and librarians

Research on 74,000 educator conversations shows how faculty use Claude for teaching, research, and building interactive learning tools.

This report presents the findings of the interviews that asked faculty to reflect on their perceptions of and experiences with generative AI in both teaching and research. Our study was driven by the following questions: To what degree are faculty adopting generative AI, and how is this changing their approaches and practices in teaching and research? What challenges are they facing in the aftermath of generative AI’s emergence? What support do they still need?

Drawing on lived experience research, Abigail Lewis explores the realities of class-based exclusion and how institutions can respond

University journals: a semi-systematic literature review of trends, challenges and future research directionsReviewsAuthorsMaryna NazarovetsAbstractUniversity journals (UJs), understood as scholarly journals published by or associated with universities, play an essential role in the dissemination...

The report identifies the urgent challenges facing universities today, as well as proposing solutions based on over 30 written submissions from students’ unions, universities, sector bodies, and individual experts. 

This study explores how college students interact with generative AI (ChatGPT-4) during educational quizzes, focusing on reliance and predictors of AI adoption. Conducted at the early stages of ChatGPT implementation, when students had limited familiarity with the tool, this field study analyzed 315 student-AI conversations during a brief, quiz-based scenario across various STEM courses. A novel four-stage reliance taxonomy was introduced to capture students' reliance patterns, distinguishing AI competence, relevance, adoption, and students' final answer correctness. Three findings emerged. First, students exhibited overall low reliance on AI and many of them could not effectively use AI for learning. Second, negative reliance patterns often persisted across interactions, highlighting students' difficulty in effectively shifting strategies after unsuccessful initial experiences. Third, certain behavioral metrics strongly predicted AI reliance, highlighting potential behavioral mechanisms to explain AI adoption. The study's findings underline critical implications for ethical AI integration in education and the broader field. It emphasizes the need for enhanced onboarding processes to improve student's familiarity and effective use of AI tools. Furthermore, AI interfaces should be designed with reliance-calibration mechanisms to enhance appropriate reliance. Ultimately, this research advances understanding of AI reliance dynamics, providing foundational insights for ethically sound and cognitively enriching AI practices.

To develop a robust policy for generative artificial intelligence use in higher education, institutional leaders must first create "a room" where dive

David Kernohan addresses the low likelihood that someone with special needs support gets to university, in an analysis of the government's annual widening participation data release

Intensive outreach work boosts higher education entry by “up to 29 per cent,” according to a study from the Higher Education Access Tracker (HEAT) project – and the biggest gains are for students from disadvantaged backgrounds. The study covers cohorts who were 18 at the start of academic years 2019–20 and 2020–21, comparing university entry rates against matched groups of peers who received minimal outreach. “Intensive outreach” is defined as 11 hours of activity, included at least eight of “high intensity” content such as campus visits or mentoring. The report does note that those students willing to participate in outreach activities may have higher levels of motivation.

KPMG and Mills & Reeve’s ‘Radical Collaboration’ report moves the conversation on collaboration in Higher Education forward. The report takes the theoretical and translates to the practical by detailing the many structural possibilities open to higher education providers - using case-studies and examples to illustrate models and their outcomes.