Conference Agenda

Generative Artificial Intelligence (GenAI) tools such as ChatGPT have been transforming higher education since November 2022. Nowadays, students and staff alike use AI tools in higher education for diverse tasks such as text translation, checking grammar in assignments, grading homework, and communicating with one another (Wang et al., 2024; Abdullayeva & Musayeva, 2023; Fuchs, 2023; Rudolph et al., 2023). Studies on the uptake of AI amongst university students show relatively high adoption rates ranging from 73% (Fošner, 2024) to 92% (Freeman, 2025), varying by field of study and gender (Stöhr et al., 2024). However, this accelerated adoption poses potential risks to academic integrity and student learning. GenAI tools produce outputs difficult to differentiate from student work, complicating grading and raising questions about the extent of student learning and the value of higher education (Stöhr et al., 2024; Farazouli et al., 2024). Faculty concerns center on authorship misattribution, detection limitations, and pedagogical implications for assessment design (Wu et al., 2024). As a result of these concerns, higher education policymakers have accelerated development and adoption of institutional policies. However, many policies are not guided by learners' experience and practical concerns, leading to inefficient responses and mismatch in resources.

To address this, we adopt a novel computational social science approach combining policy analysis with online discourse to examine the utilisation and governance of GenAI within higher education. This methodological design enables us to assess the extent to which institutional guidelines align with the practical experiences of higher education stakeholders. First we analyse discussion surrounding GenAI in higher education, drawing on lived experiences shared online through 290 posts from academic communities on Reddit. This organic user-generated data reveals concerns including usage of GenAI, automated detection avoidance, faculty’s detection methods. Novel findings include discussions on the value of learning and increased workloads as well as unclear policies. We then conduct a comparative policy analysis of policy texts on AI usage from leading universities in the Anglosphere. Our analysis of policy texts from leading university groups shows that while such texts outline permissible GenAI use, they focus on abstract concerns including academic integrity, data privacy, and fair access. Furthermore, a comparative policy analysis identifies areas of divergence between university offered guidance and online discourse. The contrast with Reddit discussions highlights a significant gap: while policy statements remain high‑level and theoretical, the texts lack concrete guidance on practical issues that practitioners grapple with managing workload, navigating unreliable detection technologies, and addressing integrity and learning concerns, as well as students discussion on academic integrity and the value of coursework.

The findings shed light on alignments and gaps between institutional policy and everyday experiences, emphasising the necessity of designing human-centred policies. We use these findings as a basis when presenting five holistic policy recommendations based on this study’s findings to bridge the gap between the lived experiences of those using GenAI with higher education institutions and academic policies. These recommendations complement each other to provide an agile approach to ensuring that higher education institutions manage AI use in a way that is both principled and practical. Our recommendations define acceptable AI use, introduce transparency on AI systems, redesign assessments, support faculty capacity, and embed fairness with institutional policies to encourage more responsible GenAI use in higher education.

Background

The rapid diffusion of generative artificial intelligence in higher education has raised significant pedagogical, ethical, and epistemic concerns across Europe (OECD, 2026). European policy initiatives, including the European Digital Education Action Plan and ongoing debates surrounding the EU AI Act, explicitly emphasize the need to strengthen AI literacy, critical thinking, and responsible AI use in educational contexts (Digital Education Council, 2024). While these frameworks highlight the societal relevance of critical engagement with AI, higher education institutions often lack empirically grounded didactic models that translate such policy goals into concrete teaching practices (Han et al., 2025). As a result, there remains a gap between European-level policy aspirations and evidence-based instructional approaches that support students in critically engaging with GenAI tools in academic learning.

Research gap

Research consistently identifies critical thinking as a central competence in higher education and demonstrates that it can be fostered through targeted instructional interventions (Abrami et al., 2008; Halpern, 1998). At the same time, a growing body of literature addresses AI literacy and the educational implications of generative AI, discussing both its potential to support learning and its risks for superficial or uncritical engagement. However, much of this research remains conceptual, focuses predominantly on K–12 contexts, or relies on survey-based quantitative approaches.

Recent reviews (e.g., Chara-De lo Rios et al., 2025; Zhai et al., 2024) identify a clear lack of empirically evaluated didactic models that foster critical thinking in higher education AI contexts, particularly within European settings. Moreover, few studies investigate students’ real-time reasoning processes while interacting with GenAI tools. By adopting a qualitative, process-oriented perspective, this study directly addresses these gaps and contributes empirical evidence on how critical thinking unfolds during students- GenAI interaction.

Theoretical Framework

The theoretical framework of this study is primarily based on the integrative model of critical thinking proposed by Dwyer et al. (2014), which conceptualises critical thinking as a set of interrelated cognitive processes involved in reasoning and judgement. The framework emphasises four key dimensions: analysis, evaluation, inference and reflective judgement, which are particularly relevant in complex and ill-structured problem contexts. Building on the Delphi Report (Facione, 1990), these dimensions involve identifying argumentative structures, evaluating the credibility and coherence of information, drawing reasoned conclusions and reflecting on uncertainty and the provisional nature of knowledge.

To operationalize critical thinking in AI-related learning environments, the study draws on the didactic framework developed by Faust and Mayweg-Paus (2024). This model structures critical reflection along the machine learning life cycle and uses guiding questions related to transparency, ethics, reliability, trustworthiness, security, control, and data protection. In the present study, this framework serves both as the instructional intervention and as an analytical lens for examining how students critically engage with GenAI systems.

Research Questions

This study examines how a didactic model for critically questioning machine learning systems across their life cycle influences university students’ critical thinking when interacting with generative AI (GenAI) tools. The main research question is: In what ways does instruction based on this model shape students’ critical engagement with GenAI outputs?

Two sub-questions guide the analysis. First, the study explores how students with and without prior instruction in the model differ in their cognitive critical thinking skills engagement with GenAI-generated content. Second, it investigates which additional forms of reflective behavior, such as prompting strategies or attitudes toward AI outputs, emerge in students’ think-aloud processes during interaction with GenAI tools.

During study, students often offload cognitive operations, from the body to the environment (Clark & Chalmers, 1998), for example through tools such as pen and paper (Storm & Stone, 2015). This process allows them to reduce cognitive load when dealing with tasks that demand multiple cognitive operations (Burnett et al., 2025). For example, students may offload cognitive operations, such as coding and decoding in the case of notes (e.g., encoding ideas into bullet points or symbols), or comparison (e.g., using a spreadsheet to automatically compute differences and rankings), which can free resources for other operations and facilitate understanding (Kirsch, 2010). However, students' widespread use of generative AI (genAI) such as ChatGPT in their studies may have caused a major shift in the patterns of cognitive offloading, especially high-level cognitive operations, which have implications for developing crucial cognitive skills. Additionally, the use of genAI spans a spectrum of epistemic agency from integration (Gonsalves, 2024), to scaffolding (Darvishi et al., 2024) to complete outsourcing (Chen, 2025), with students exercising progressively less control over and responsibility for their knowledge work. Students offloading behaviors when studying with AI agents is yet understudied, leaving and important gap on the account of (a) the offloading of cognitive operations from students to genAI during study activity, and (b) the agency profile of student-genAI interaction as students engage in knowledge work. In our study, we aimed to clarify when genAI use functions as an extension that reorganizes cognition without displacing learners’ central epistemic role, and when it becomes a substitute that relocates substantial knowledge work outside the learner.

This study is grounded in two interconnected theoretical perspectives that together enable focused analysis of student-genAI interactions: Distributed Cognition and Epistemic Agency. Distributed Cognition (Hutchins, 1996) provides the foundational lens for understanding how cognitive activity emerges from coordinated interactions among components of a sociotechnical system, comprised of individual students, genAI tools, and their learning communities. This framework allows us to conceptualize genAI not merely as an external aid but as an integral component of a distributed cognitive system where knowledge work unfolds across human and technological agents. The concept of epistemic agency (Scardamalia, 2002; operationalized by Damşa et al., 2008) enables examination of the degree to which learners maintain responsibility for and control over their knowledge work, including setting epistemic goals, evaluating outputs, revising ideas, and regulating their learning processes. Together, these frameworks provide an adequate analytical structure for investigating both what cognitive operations students offload to genAI and how these interactions can be characterized in terms of agency.

The study addresses two interrelated research questions: (RQ1) what cognitive operations are higher education students offloading to genAI? This question is addressed through analysis of the cognitive operations embedded in students' prompts to genAI systems; (RQ2) How can their interactions be characterized, especially in terms of agency? This question is examined through an analytical framework that categorizes student-genAI interactions along a spectrum of epistemic agency– integration (highest agency, where students actively incorporate genAI outputs into their own knowledge work), scaffolding (moderate agency, where genAI provides support structures that students build upon), and outsourcing (lowest agency, where students delegate cognitive operations with minimal personal engagement or evaluation). By examining these questions through the lens of distributed cognition and epistemic agency, this study aims to illuminate the implications of genAI use for students' development of cognitive skills and their ability to engage as epistemic agents during study.

In recent decades, European higher education has undergone rapid internationalization, marked by increasing mobility of students and academic staff across national borders. Iceland reflects this broader European trend. At the University of Iceland, the country’s largest public university, international students and staff constitute a growing proportion of the academic community, including at doctoral and early-career levels. Women represent the majority of students and a substantial share of academic staff, while a significant number of doctoral graduates and researchers hold foreign citizenship. These demographic shifts position Icelandic academia within wider European debates on internationalization, equality, and academic labor.

While internationalization is often framed as a marker of excellence and diversity, research across Europe shows that it also reshapes power relations within universities, particularly for those whose employment, residence status, and career progression are structurally precarious. Foreign women in academia frequently occupy short-term, grant-dependent, or informally defined positions, where gender intersects with nationality, language, and migration status. Within highly hierarchical academic environments, these conditions can increase vulnerability to discrimination, harassment, and other forms of violence, while simultaneously limiting access to institutional protection and redress.

This paper examines the experiences of foreign women working in Icelandic academia, with a focus on employment-based violence (EBV). EBV is understood as psychological, sexual, economic, and symbolic harm occurring within the context of academic employment. The central research question guiding the study is:

To what extent can the violence experienced by foreign women in Icelandic academia be understood as personal (direct) violence, structural violence, or as a product of their interaction?

By addressing this question, the study responds to gaps in European and international research that have tended to focus narrowly on sexual harassment, often treating violence as an individual or behavioral problem rather than as an institutional and structural phenomenon shaped by employment precarity and migration status.

The theoretical framework draws primarily on Johan Galtung’s distinction between structural and personal violence, complemented by Sara Ahmed’s analysis of institutional complaint. Structural violence refers to harm embedded in social and institutional arrangements that produce unequal life chances without a clearly identifiable perpetrator. As Galtung (1969) argues, such violence is “built into the structure” and manifests through unequal power relations and normalized disadvantages. In contrast, personal or direct violence involves identifiable actors and observable acts of harm, such as harassment, intimidation, or abuse of authority.

While analytically distinct, these forms of violence are deeply interconnected. In academic institutions, personal acts of violence are often enabled, normalized, or rendered invisible through structural conditions such as informal power networks, opaque decision-making, ineffective reporting mechanisms, and institutional silence. Structural violence thus creates the conditions under which personal violence can occur repeatedly and with impunity.

Ahmed’s Complaint! (2021) extends this framework by illuminating how institutional responses to reported harm function as mechanisms of structural violence. Ahmed conceptualizes complaint not merely as an individual act but as a process that exposes how institutions work to protect themselves. Complaints are frequently delayed, dismissed, or reframed as personal problems, while complainants—particularly those in precarious or foreign positions—are positioned as disruptive or risky. In this way, institutional procedures meant to ensure equality may operate as non-performative commitments that reproduce harm rather than resolve it.

By combining these perspectives, the paper conceptualizes employment-based violence in academia as a phenomenon produced at the intersection of individual actions and institutional structures. Drawing on narrative and longitudinal qualitative data, including follow-up interviews, the study foregrounds how foreign women retrospectively interpret their experiences and how violence continues to shape their professional trajectories, mobility decisions, and sense of belonging. In doing so, the paper contributes to European and international scholarship by framing EBV as a structural issue within internationalized higher education and by centering the voices of foreign women, a group that remains underrepresented in both research and policy discussions.