With the recent arrival of publicly available AI chatbots like ChatGPT, Copilot, Gemini, and Claude follows a need for knowledge about how students in higher education perceive and use these tools, and what this might mean for their learning processes. This scoping review analyzes 24 empirical articles published between 1 January 2022 and 5 September 2023 on students’ perceptions and use of AI chatbots in higher education. The articles were reviewed using a five-stage scoping review methodology. The findings underscore a global research interest in how students engage with AI chatbots, which is especially pronounced in Asia. The studies span diverse disciplines, with a predominance in science, technology, engineering, and mathematics disciplines. The empirical findings reveal that students perceive AI chatbots as highly useful and motivating as personal task assistants and for getting immediate feedback and help with writing, coding, and academic tasks. However, students are concerned about the accuracy and reliability of the responses from the chatbots, as well as potential negative impacts on their learning processes, critical thinking, discipline, and creativity. The purpose-driven use of AI chatbots among students and their potentially positive influence on motivation and learning processes offer insights for educators and policymakers. Our research concludes that while positive attitudes, perceptions, and critical use prevail, addressing students’ concerns is crucial for responsible AI integration in higher education.

Student gender differences in technology acceptance and use have persisted for years, giving rise to equity concerns in higher education (HE). To explore if such differences extend to generative artificial intelligence (genAI) chatbot use, we surveyed a large Norwegian HE student sample (n = 2692) using a fully mixed concurrent equal status design. Our findings show that men exhibit more frequent engagement with genAI chatbots across a broader spectrum of applications. Further, men demonstrate a heightened interest in genAI chatbots as tools and in their relevance to future career prospects. Women primarily utilize genAI chatbots in text-related tasks and express greater concerns regarding critical and independent thinking. Women also exhibit a stronger need to learn how to determine when it is wise to use and how to trust genAI chatbots. Consequences are discussed for the individual, society, and HE institutions in terms of social reproduction, diversity competence, and equitable teaching practices.

To cope with an unpredictable future, higher education in mathematics and science (MS) needs to educate a knowledgeable and skilled workforce. Co-operative learning (CL) is a teaching method associated with increased academic achievement and development of generic skills. Thus, the purposes of this scoping review are to assess the evidence base of CL in undergraduate MS education to inform teaching practices and to identify potential knowledge gaps to inform future research. The review covers 24 empirical studies conducted from 2010 to 2020 on the prevalence, uses, and outcomes of CL elements in undergraduate MS education. The results show that there are few such studies, and these are rarely conducted outside the US or in disciplines other than chemistry. The most frequently implemented CL elements in the included studies are heterogeneous group formation, the use of roles, and different CL structures. The most prevalent student outcome of implemented CL elements in the reviewed studies is enhanced academic success, followed by student attitudes, generic skills, and psychological health. The results have implications for future implementation of and research on CL in international MS higher education.

Identifying valid and reliable instruments measuring attitudes toward Artificial Intelligence (AI) and examining attitudinal gaps are becoming increasingly important as they may inform ethical and appropriate development, adoption, and regulation of AI technologies. In this study, we validated the 4-item AI Attitude Scale (AIAS-4) in a large sample of Norwegian university students (n = 2329). Using confirmatory factor analysis, Cronbach’s alpha and McDonald’s Omega, we found the AIAS-4 to be both valid and reliable. Further, through multiple regression analysis, we identified the students’ gender, age, and study discipline to be significant predictors of positive attitudes toward AI technology in our sample. Male students were more positive than female students and students between 21 and 25 were more positive than students under 21 and students over 25 years. As a novel discovery, we found that the students from the faculty of engineering and natural sciences were more positive than students from the faculty of health sciences and from the faculty of teacher education, culture, and sports, but not significantly more positive than students from the faculty of economics and social sciences. Our findings are discussed in light of previous research and, although limited by restricted generalizability, potential response bias, and the inherent limitations of the cross-sectional study design, they contribute new knowledge to the field of AI in higher education and society.

The adoption of generative artificial intelligence (genAI) in higher education (HE) has led to an urgent need to foster genAI literacy among staff. Thus, we drew on a genAI introductory course for staff at a major university college in Norway to examine, through a one-group pre-/post-test design, if participation was associated with an increased genAI acceptance, i.e. intention to use genAI tools. We posed five hypotheses based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and the Artificial Intelligence Attitude Scale (AIAS) to explore the predictive relationship between pre- and post-course genAI acceptance, the moderating effects of demographic and experiential variables, and changes in the underlying factors of genAI acceptance over time. Through PLS-SEM we discovered that Habit and AI attitudes were significant predictors of genAI acceptance at the beginning (T1), but that none of the examined variables significantly predicted genAI acceptance at the end of the course (T2). Behavioral intentions at T1 did not significantly predict Behavioral intentions at T2, either. However, the UTAUT2 factors combined with AI attitudes explained 79.5% of the variance in genAI acceptance at T1 and 82.4% at T2. Neither gender, age, experience with genAI, nor teaching position moderated the relationship between genAI acceptance at T1 and T2. Last, but not least we found a significant and large increase in genAI use from T1 to T2. Similarly, all UTAUT2 predictor variables significantly increased while AI attitudes did not. Our study is the first to implement a pre-/post-test design utilizing the UTAUT2 framework among HE staff. Our findings suggest that course participation is associated with changes in prior acceptance and intention patterns, potentially reducing disparities in familiarity and use, thereby facilitating professional development within a collegial community. Our novel results have implications for future research and HE institutional professional development worldwide.