Trends in Higher Education

The shift from secondary school or college to university represents a period of change characterised by multiple transitions, educationally, socially, and emotionally. As students move from strictly regulated school environments to the relative independence of university study, they arrive at their expectations of university life. For some, their expectations of university will not change across the course of their degree, while for others, partial or total expectation shifts may occur. The current study conducted nine focus group sessions in 2018 with a total of 46 undergraduate psychology students (32 first-year and 14 third-year students), seeking to explore the academic factors that shape students’ experiences across their degree. Using thematic analysis, the study conceptualised five main themes: prior experience, adjustment to university, staff relationships, the experience of studying, and future career plans. By exploring the entry year and the final study year, we have shown how students’ expectations change across their undergraduate studies. We suggest that managing first-year students’ expectations would help in their initial transition. Ongoing support such as between-module check-ins and continued employability support across the span of each student’s degree would be beneficial for their overall experience. Additionally, the findings also highlight the key role played by staff in developing a feeling of belonging.

The rapid advancement of artificial intelligence (AI) technologies has created both opportunities and challenges in higher education. AI tools are increasingly regarded as potentially transformative, yet also contested, resources for enhancing teaching and learning. This study investigates the behavioral intentions and use behavior of Generative AI (GenAI) tools among Portuguese university students by applying the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model. Seven predictors (performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, habit, and price/value) were included to develop a model explaining the behavioral intention and use behavior of AI tools. Data were collected from 323 Portuguese university students and analyzed using partial least squares structural equation modeling. The results show that only hedonic motivation and habit significantly influence behavioral intention, with habit emerging as the strongest predictor. Behavioral intention, in turn, positively affects use behavior. Moreover, gender also moderates the relationship between habit and behavioral intention. These findings deepen our understanding of GenAI tools’ acceptance in higher education and underscore the central roles of enjoyment and routine in adoption processes. They further provide insights to support institutional strategies, inform curriculum design, and support the ethical integration of GenAI technologies in academic contexts.

The Technological Pedagogical Content Knowledge (TPACK) framework is widely used to conceptualize teacher knowledge as an interplay of content, pedagogy, and technology. Following recent research interests in examining TPACK as contextually situated knowledge, this study investigates how pre-service physics teachers (undergraduate students in a physics department) and in-service science teachers perceive the domains of TPACK and explores what these differences imply for university-based teacher education. A total of 48 pre-service physics undergraduates and 27 in-service teachers completed an adapted 21-item self-assessment questionnaire, which combined validated items with context-specific modifications. Data analysis included internal consistency reliability tests, independent samples t-tests, and correlation analysis. Results revealed that pre-service teachers reported higher self-assessed competencies, especially in integrative domains, although their knowledge structures appeared less coherent. In contrast, in-service teachers exhibited more coherent and integrated knowledge frameworks, possibly reflecting their accumulated professional experience, despite reporting lower self-confidence. These findings confirm the contextual and situated nature of TPACK, highlighting the divergence between perceived competence and structural coherence. The study contributes by proposing that university science education programs should not only promote theoretical understanding of TPACK but also deliberately embed technology-rich, practice-oriented experiences.