Research into student transition to and experience of first-year undergraduate study has been ongoing for many years. The corresponding research within the discipline of science has been less prolific and that which has been published tends to focus on using external factors such as previous grades, finances, travel time etc. as predictors for student success and retention. While these studies are valuable in identifying possible impacting factors, many of these factors are out of the control of university academics. In this study an attempt is made to examine the transition experience from a more student-centred approach, by identifying a student profile at the transition stage on entry to undergraduate science that is based on a conceptualisation of learning. It uses this profile to identity potential areas for academics to build upon student strengths and expectations of university study with the aim of highlighting opportunities to ease the transition period for both students and academics and to ultimately improve student integration and performance.

Generative artificial intelligence (GenAI) has rapidly emerged as a transformative educational technology, raising questions about how educators and pre-service teachers critically engage with AI-produced content. This case study investigates how WebQuests, a long-established, inquiry-based pedagogical model, can foster critical engagement with GenAI tools. Situated within an initial teacher education programme, a WebQuest, incorporating GenAI sources, was implemented with 24 pre-service language teachers, who engaged with curated resources alongside ChatGPT and Copilot to produce infographics for secondary school audiences. Data were collected through semi-structured interviews and were analysed using Braun and Clarke’s thematic analysis. Findings indicate that scaffolded engagement with GenAI encouraged participants to compare AI-generated outputs with trusted sources, critically evaluate accuracy and reliability, and reflect on integration into their future practice. Whilst pre-service teachers valued GenAI’s accessibility and efficiency, they expressed concerns about clarity, verbosity, and trustworthiness. The WebQuest model effectively supported synthesis of multiple information sources, fostering functional AI engagement and critical evaluation of its affordances and limitations. This case study concludes that integrating GenAI within structured, inquiry-based pedagogies advances digital and AI literacy in initial teacher education, whilst highlighting the need for institutional guidance, professional development, and further research in this area.

This paper shares the initial results of a small-scale research study which aimed to investigate the problem solving processes Year 1 undergraduate science students used while undertaking problem-based learning tasks in the chemistry laboratory. A qualitative case study, combining observation and semi-structured interviews, was used to explore learners’ experience of the problem-based learning (PBL) task. The literature on problem solving processes of experts generally places importance on domain specific knowledge, developed through experience, for the expert. This is of particular relevance to this study since the students have a range of different prior experiences in the ‘chemistry’ and ‘experimental/practical’ domains. Overall, it was shown that students revealed novice-like problem solving strategies and surface approaches to learning. However, one group revealed more expert-like characteristics, coupled with a deep approach, with this group successfully solving the problem. One implication of this study is that we need to be more aware of the learning approaches of our students, as well as their subject knowledge, as they enter into higher education through careful scaffolding of such problem-based task.