The literature on learning practices and strategies is rich with insights about the effectiveness of specific interventions and strategy instructions (e.g., Chamot, 1993; Spencer & Maynard, 2014), student’s perceptions and experiences (e.g., Ginns & Ellis, 2007; Nijhuis, Segers, & Gijselaers, 2007; Virtanen & Tynjälä, 2019), and cognitive and psychometric views on learning strategies and approaches (e.g., Biggs, Kember, & Leung, 2001; Neroni, Meijs, Gijselaers, Kirschner, & de Groot, 2019; Pintrich, Smith, Garcia, & Mckeachie, 1993). Such existing research is often psychologically framed, focusing on highly abstract aspects of learning like rehearsal, summarization, information organizing skills, and time management. However, when it comes to looking at students at universities, little is known about what they actually do in their everyday lives in order to learn. Do they, for example, meet with friends for sharing ideas? Do they converse with ChatGPT? Do they print out learning materials and use text markers? Do they listen to audio recordings of lectures when riding the bus? And so on.

Against this backdrop, we take a more sociological approach looking at what students do in their everyday lives. Adopting a practice-based perspective (Giddens, 1984), we aim at mapping the learning practices of university students, i.e., the micro-practices of their everyday lives enacted to learn. Our first main research question is, hence, the following:

1) How does university students’ learning look like in practice?

Furthermore, recent years have seen substantial changes in the education sector, especially due to the pandemic and the advent of new digital technologies like ChatGPT. Consequentially, there is a myriad of studies focusing on the impact of the pandemic and/or of new digital technologies on learning experiences and effectiveness (Carrillo & Flores, 2020; Orozco, Giraldo-García, & Chang, 2023). One big issue as of now is the opportunities and challenges that artificial intelligence (AI) poses for education in general (Zhu et al., 2023) and for higher education in terms of academic integrity in particular (Perkins, 2023).

However, empirical research on how the impact on the actual learning practices of students looks like, is yet to be conducted. Existing works are more based on assumptions and possibilities. We therefore see a lack of research, which we aim to overcome with our study comparing student’s learning practices before and after the pandemic and the introduction of artificial intelligence tools like ChatGPT. Therefore, we have derived a second main question:

2) How have university students’ learning practices changed through the pandemic and the introduction of AI tools like ChatGPT?

In our study, we will answer these questions through a longitudinal interview study at a German university. Using our practice-based approach, we identify university students’ practices of learning and how these have changed through the pandemic and the advent of artificial intelligence technologies like ChatGPT.

To answer our research questions, we adopt an interpretative approach (Lincoln & Guba, 1985) and conduct an exploratory qualitative interview study. In particular, we conducted a series of 19 focus group interviews with 87 students involving 4 interviewers in 2019 and 2020, and will conduct another series of 12 focus group interviews between February and July 2024. All interviews are conducted with teacher education students in different social science study programs at the Ruhr-University of Bochum, Germany. The interviewers have not been involved in the teaching and/or examination of the interviewed students to ensure that students have been able to speak freely and without pressure. We have been using the method of the problem-centered interview (Witzel & Reiter, 2012), which combines elements of structured and unstructured interview techniques to achieve a process of discursive-dialogic knowledge production be-tween the interviewer and the interviewees. In doing so, we have been able to facilitate open and comprehensive discussions among the participating students about how they learn with whom and when. To analyze the material, we are using a grounded theory-based approach, specifically the so-called “Gioia method” (Gioia, Corley, & Hamilton, 2013), which combines open (first order) coding with theory-centric (second order) coding. This analytical method is particularly suited for practice-based studies, be-cause it allows to inductively identify first order categories from the interviews which are then collapsed into distinctive practices on the second order level by cycling between the first order categories and practice theory. Employing this method allows us to identify the distinctive practices of learning enacted by the university students. Our longitudinal approach thereby enables us to map the learning practices of students as they develop over time. Specifically, we will be able to inquire into the impacts of the pandemic and the advent of artificial intelligence chatbots like ChatGPT on learning practices.

By using focus group interviews we have been able to effectively gather diverse perspectives and foster dynamic, interactive discussions that provide rich qualitative data around shared beliefs and learning practices. We will present the learning practices of students and how these have changed through the pandemic and the advent of artificial intelligence technologies such as ChatGPT. Thoroughly mapping and understanding the learning practices of university students, will be an important contribution to improving effective learning methods, detecting potential areas for improvement in higher education curricula, and understanding the unique needs of university students. University teaching personnel is confronted with diverse students that exhibit a large diversity of learning practices outside of the classroom. For diversity to result in substantial and equitable learning gains, it needs to be accompanied by intentional and wide-spread inclusion. Inclusive practices can be challenging for educators when working with students who are diverse on multiple and intersecting dimensions. Our results are of relevance for researchers in higher education in Europe and world-wide as they offer insights into how students enact learning in their everyday lives. Our results have moreover the potential to inform educators at universities about the students’ micro-practices of learning, which will enable them to take these into account when designing their courses and teaching concepts.

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