This study explores the arrival of Generative Artificial Intelligence (GenAI) technologies in a collaborative Problem-Based Learning (PBL) environment from the perspective of students. It examines the emerging practices students develop individually and in groups as well as the tensions they navigate in response to GenAI’s sudden arrival, its inherent limitations and multifaceted affordances, as well as the evolving landscape of formal and informal rules that frame its use. Grounded in Cultural-Historical Activity Theory, the study adopts an exploratory research approach, utilizing both qualitative and quantitative data from a survey administered to 1st and 5th semester IT students in a systemic PBL university (n = 83). Quantitative data were analyzed using descriptive statistics and principal component analysis, while qualitative data were analyzed through thematic analysis. The findings indicate a high adoption rate and generally favorable attitudes toward GenAI across both semesters. While 1st semester students were more likely to report no prior experience with GenAI compared to 5th semester students, no notable differences were observed regarding frequency or patterns of use. Overall, the arrival of GenAI manifests in the students’ individual and collaborative study practices by enabling or obstructing both epistemic and pragmatic mediations of the objects. However, these interactions have been found to be accompanied by several tensions– specifically between the mediation of different objects, between students and their project groups, and between students and the rules framing GenAI use, which are discussed and contextualized in light of theory and contemporary research.

Feedback is an integral part of the learning process. However, delivering feedback effectively remains challenging, particularly within massified higher education systems that are characterised by large cohorts and increasingly diverse student populations. The emergence of generative artificial intelligence (GenAI) enables new ways of embedding feedback into educational offerings, some of which may be highly beneficial. In this paper, we introduce Abundant Feedback as a conceptual lens for examining the new capabilities that may be enabled by GenAI. We present a four-dimensional framework identifying the dimensions of GenAI feedback as abundance of Volume, of Availability, of Relevance and of Character. Through a systematic literature search, we describe how these dimensions manifest in recent empirical studies, and identify two educational domains, Computer Programming and Foreign Languages, as early adopters of AI-generated feedback. Beyond merely digitising existing scarce feedback processes, we discuss the emergence of new learner-driven feedback practices that are enabled by abundance, that both stimulate and demand student feedback literacy. Our multi-dimension abundance framework provides a lens, as well as the vocabulary and conceptual tools, to guide the implementation of GenAI feedback in ways that help realise the potential of artificial intelligence to enhance student learning.

Aalborg University (AAU) uses Problem Based Learning (PBL) in which students develop professional competencies by engaging with authentic problems. All Engineering master students at AAU have had a PBL Competency profile as a compulsory exercise. The profiles are structured around the AAU PBL Competency Framework. This framework is comprised of 48 individual competencies, grouped into four categories; meta- reflective, problem-oriented, interpersonal, and structural. Students are free to choose which of these competencies they incorporate in their profile. A total of 1095 PBL Competency profiles were reviewed across more than 50 STEM study programs, and each was evaluated for the presence or absence of each of the 48 competencies in the PBL competency framework. This study explores the relative prevalence of each competency within the profiles submitted by students, and the balance across the different categories. Further, it investigates the extent to which students made these competencies explicit, and evidence the competence. The study reveals that the competencies are not equally represented in the competency profiles, with a subset of seven competencies comprising 40% of all reported competencies. The study further reveals that the students generally do not provide evidence to support their claims of competence.