Conference Agenda

This theoretical contribution sketches out how Hans-Christoph Koller’s theory of transformative educational processes (Koller, 2020, 2023) can be adapted for organisational education research in order to analyse organisational learning as the development of action-capability under conditions of the erosion of epistemic authority (Heidelmann 2023).

The paper starts from a diagnosis that, at societal level, post-truth conditions (Schindler, 2023) are increasingly salient: distrust in epistemic authorities is heightened (Crilley, 2018), evidential criteria are increasingly contested (Lathrop, 2025), and synthetic media (Ruiz, 2025) intensifies uncertainty about what counts as a fact. Under these conditions, organisations still remain key vehicles of collective action (Kornberger et al., 2025).

Much of the organisational learning and crisis-management literature implicitly assumes that disagreement concerns appropriate action given largely shared facts (Antonacopoulou & Sheaffer, 2014; Butsch et al., 2025). Yet, the problem at hand is more fundamental, insofar as the shared status of “facts” (cf. Baudrillard, 2019) and the legitimacy of epistemic authorities themselves are eroding (Adler & Drieschova, 2021; Bartsch et al., 2025). The paper therefore asks how organisations can learn to act without relying on symbolic certainty as a precondition of legitimate decision-making.

The proposed conceptual work is to shift focus from information acquisition to organisational action-capability. Here, organisational action-capability is provisionally defined as the collectively sustained capacity to (1) decide under conditions of (epistemic) uncertainty, (2) justify decisions as legitimate amid contestation, and (3) coordinate and enact decisions across organisational sites. This definition foregrounds knowing and acting as mutually entangled and institutionally mediated rather than sequential. It aligns with the interest in learning “in and of organisations” (Göhlich et al., 2016), and it provides a target for theorising learning where no “recipe-knowledge” (Schütz & Luckmann, 1974) is available, for instance in emergency response and civil protection contexts (in times of hybrid warfare) where time pressure, legitimacy demands, and contested information environments converge.

The paper proposes that systematic organisational-analytic adaptations remain scarce, and it takes as its point of departure Koller’s own observation that collective transformative processes in which the world-self relation of “a group of people, an organisation or an institution” is transformed have been under-examined and that the theory appears open to extension in this direction (Koller, 2022, p. 15).

In addition, Koller’s discussion of conceptual resources for capturing world-self relations, including Bourdieu’s habitus theory and narrative identity, is treated as an invitation to theorise collective and organisational formations (Koller, 2022, p. 15ff). The paper uses these openings to outline how “world-self relations” can be translated into organisational terms without reducing them to individual attitudes or to vague notions of culture.

Conceptually, the paper proposes – in accordance to Koller (2022, p.15) – to operationalise organisational world-self relations (at least in part) as habitus-like regularities in practice arrangements (Bourdieu, 2007, p. 191; Bourdieu & Wacquant, 2008, p. 161). This connects to Koller’s emphasis that habitus is collective as a common trait of group members without being centrally orchestrated (Koller, 2020, p. 6). Transformative organisational Bildung (cf. Koller, 2020), as proposed here, becomes the transformation of those habitus-like practice regularities such that the organisation can sustain warranted action under uncertainty rather than seeking a return to stable truth conditions.

The paper addresses three questions: (1) How can Koller’s theory of transformative educational processes be translated to the level of organisational learning under post-truth conditions? (2) Which dimensions of organisational world–self relations shape organisational action capability when epistemic authority is contested, and how can these dimensions transform? (3) What methodological implications follow for organisational education research that seeks to study such collective transformations beyond organisational self-descriptions?

As Artificial Intelligence increasingly reshapes educational systems worldwide, international organizations such as UNESCO and the OECD have promoted policy frameworks to define what counts as responsible AI governance. Documents such as Guidance for generative AI in education and research do not merely offer technical recommendations. They function as governance instruments that shape how educational futures are imagined, how risks are defined, and how legitimacy is assigned.

Rather than treating these frameworks as neutral policy tools, this study conceptualizes them as forms of epistemic intervention that seek to establish global authority over how AI should be understood and governed in education. While these organizations claim to set universal standards, little empirical research has examined how such global imaginaries are translated into organizational practice as they travel across governance levels, from international institutions to national ministries and into local educational organizations.

This creates a critical gap in educational leadership and governance research. It remains unclear how authority, expertise, and professional agency are reconfigured when AI driven governance norms are introduced into school systems. This study addresses this gap by investigating how global AI governance discourses are constructed, domesticated, and operationalized within educational organizations.

RQ1: How do international organizations discursively construct AI governance ideals as globally legitimate and scientifically grounded? What social imaginaries and visions of schooling do they mobilize to secure epistemic authority?

RQ2: Through what organizational processes do educational institutions reinterpret and domesticate these global AI governance norms? Which elements of AI driven schooling are selectively adopted, modified, or resisted in alignment with local institutional priorities?

Theoretical Framework:

The study is grounded in the concept of epistemic governance as developed by Alasuutari and Qadir. This perspective views governance as the shaping of how actors understand reality rather than as direct coercion. Governance operates by influencing what is perceived as true, necessary, and inevitable.

The analysis focuses on three interconnected dimensions. First, it examines epistemic work that redefines the ontology of schooling, framing schools as data driven and calculable environments. Second, it explores how actor identities are reconfigured, particularly how authority shifts from pedagogical expertise toward technical and algorithmic knowledge. Third, it analyzes how normative standards are reconstructed, including the growing valorization of algorithmic objectivity as a marker of rational decision making.

To capture the political and technical dynamics of AI governance, the study introduces the concept of calculated governance. This concept emphasizes the dual role of AI as both a computational technology and a strategic resource used to legitimize organizational power. AI is not only calculated in a technical sense but is also deployed in a calculated manner to justify decisions, allocate authority, and manage institutional accountability.

Calculated governance thus highlights how educational leadership increasingly relies on quantified evidence and automated systems to support policy decisions, often creating a gap between algorithmic rationality and professional educational judgment.

Topic and research problem

Generative AI has entered higher education and organizational learning contexts, often framed through promises of efficiency and data-driven decision-making. Within this discourse, AI is positioned as a means to optimize teaching, assessment, supervision, and organizational processes by accelerating action and reducing uncertainty. Such approaches conceptualize knowing as data and analytics, and acting as optimized decision-making (e.g. Zine, 2025). Critical scholars have noted that much contemporary AI adoption in education risks prioritizing efficiency and automation over professional judgement, responsibility, and democratic accountability (Selwyn, 2024).

From the perspective of organizational education, this raises a critical question: what happens to professional judgement, responsibility, and learning as situated practice when AI is introduced as a decision-support or automation technology? While data-driven approaches offer valuable insights, they risk marginalizing forms of knowing that are tacit, relational, ethical, and historically situated—dimensions central to organizational education research (Göhlich et al., 2016).

This paper takes an alternative starting point by examining AI not as a tool for optimization, but as a practice-shaping epistemic actor that intervenes in how knowing and acting are negotiated in organizational and educational practices.

Research aim and questions

The study investigates how non-efficient AI can reconfigure knowing–acting relations in organizational education, focusing on supervision, collegial judgement, and students’ engagement with vocational subject practices.

The overarching research question is:

How does a practice-oriented AI, designed to resist automation and rapid solution-making, reshape relations between knowing and acting in organizational education?

This question is elaborated through three subsidiary questions:

• How are tacit assumptions and theories-in-use made visible in supervision and collegial dialogue when AI challenges rather than confirms professional reasoning?
• How are responsibility and judgement redistributed in organizational practices when AI slows down action instead of accelerating it?
• How do students’ interactions with such AI mediate the integration of theory and vocational practice in professional education?

Theoretical framework

The study is grounded in the Theory of Practice Architectures (Kemmis et al., 2022), which conceptualizes practices as interwoven arrangements of sayings, doings, and relatings shaped by cultural-discursive, material-economic, and social-political conditions. Organizational learning is understood as emerging within and through practice complexes rather than as individual cognitive acquisition.

To articulate knowing–acting tensions within these practices, the study draws on Argyris and Schön’s distinction between espoused theories and theories-in-use (Argyris & Schön, 1996). This enables analysis of how professionals and students act on implicit logics that diverge from declared theoretical positions—logics that become visible in moments of resistance, hesitation, and breakdown.

Together, these perspectives enable an understanding of AI as an intervention that participates in practice architectures by shaping what can be said, done, and related in organizational education.

Positioning within organizational education research

The paper is situated within the tradition of organizational education as articulated in the Research Memorandum of EERA Network 32 (Göhlich et al., 2016), emphasizing process-related reflection, organizational learning in, by, and between organizations, and critical engagement with power, responsibility, and ethics.

In contrast to co-design approaches where human facilitators mediate reflection (e.g. Belfield & Petrescu, 2025), this study examines what happens when epistemic mediation is partially delegated to an AI system designed to interrupt habitual action. Rather than catalyzing action through acceleration, the AI under study introduces friction and delays closure, foregrounding conditions for knowing and acting in practice.

European and international relevance

Across European higher education and organizational contexts, institutions face pressure to adopt AI-driven solutions while maintaining professional judgement, ethical responsibility, and educational quality. The study addresses cross-national challenges and offers conceptual tools to engage with AI as a constitutive condition of contemporary organizational learning.

A simulation-based training model was developed in an article-based dissertation to guide the practitioners in pedagogically viable inclusion of immersive virtual reality (IVR) training scenarios within occupational safety training curriculums. The research question for the study was, “How should the pre- and post-briefings and individual IVR training scenarios be designed and implemented in a simulation-based training model to support the achievement of safety training objectives in the sociocultural setting of the workplace?” Sociocultural and cognitivist theories of learning were applied in the study to address the question (see Billett, 1996). The study draws on research on workplace learning, occupational safety training, and IVR learning.

In this presentation, the model’s relationship to the organizational sociomaterial setting of safety training is discussed. The safety trainers are considered key actors who uphold positive safety culture in the work community. To be able to take advantage of virtual reality technology, it is vital they have access to sociomaterial resources, such as the support of their peers and the management, and possibilities for professional development. The combination of group discussion and IVR practice enables the co-creation of an active and flexible learning space (e.g. Damşa et al., 2019). The trainers' pedagogical content knowledge is important, as it enables them to adapt the training to the learners, and address any misinterpretations or misunderstandings that may arise during the training (see Gherardi, 2012).