Conference Agenda

The rapid integration of generative artificial intelligence (AI) into educational systems has fundamentally altered the landscape of teacher professional development (TPD). While global policy frameworks, such as the EU’s DigComp 3.0, increasingly emphasize "digital agency," these normative visions often collide with the complex realities of classroom practice. The challenge of AI integration is frequently misrecognized as a technical problem of skill acquisition (techne), ignoring the profound ethical and pedagogical uncertainties that teachers must navigate. This study examines how Taiwan’s educators move beyond technical compliance to develop "collective phronesis" that emphasizes a shared, dialogically constructed practical wisdom, and how this collective capacity functions as a critical infrastructure for system resilience.

In Taiwan, the Ministry of Education has launched several initiatives, such as the "Digital Learning Enhancement Project," to bridge the digital divide and promote AI literacy. However, longitudinal engagement with the "Digital Pilot School Initiative" reveals a persistent "implementation gap." While teachers demonstrate growing technical proficiency, they struggle significantly in domains requiring situated professional judgment, such as empowering learners and fostering ethical digital citizenship. This gap suggests that standardized competence frameworks, while necessary, are insufficient for guiding action in the indeterminate zones of AI-mediated pedagogy.

This study posits that sustainable AI integration requires a theoretical shift from individual expertise to collective capacity. Drawing on Aristotelian ethics, we conceptualize phronesis not merely as individual practical wisdom but as a socially constructed professional sensibility. We argue that in the face of rapid technological disruption, individual judgment is fragile; system resilience emerges only when phronesis is collectively constructed, externalized, legitimized, and stabilized through professional communities. TITATI (Teachers in & through AI and Technology Integration), a grassroots cross-school professional learning community established in 2024, provides a distinctive empirical site for examining this process. This community brings together high school teachers nationwide to collaboratively develop AI-integrated lesson plans and share their pedagogical experiences across diverse classroom contexts under the guidance of experienced coaches.

The study asks: How is practical wisdom regarding AI integration socially constructed among teachers, and how does this collective phronesis bridge the gap between abstract policy frameworks and situated practice?

To address this, the analysis focuses on three interrelated dimensions:

1. How do teachers transition from instrumental adoption (techne) to ethical and pedagogical reasoning (phronesis) when engaging with generative AI?

2. Through what dialogical processes does individual tacit judgment transform into shared professional norms within heterogeneous communities?

3. How does collective phronesis act as a mediating mechanism that activates digital competence frameworks (like DigComp 3.0) and fosters organizational adaptability?

Theoretically, the study synthesizes Aristotelian epistemology with sociocultural theories of learning. It challenges the individualistic bias in TPD literature by positioning the "community of practice" not just as a support network, but as an epistemic engine capable of generating new ethical norms. Furthermore, it extends the concept of system readiness (Wang et al., 2020) by identifying collective phronesis as the "missing link" that translates policy readiness into actualized system resilience. Through examining TITATI's dialogical practices, this study reveals how bottom-up professional wisdom negotiates with and revitalizes top-down digital education policies.

Generative artificial intelligence (GenAI) has gained traction as both a tool for and a topic of learning. However, the potentialities ascribed to this technology – such as being a “partner”, “tutor” or “facilitator” – starkly contrast with the reality of educators only partially knowing how to apply it meaningfully to support students’ learning. This seems particularly relevant in vocational education and training (VET), where professional learning is often less formalized than in other branches of the educational system (Zhou et al., 2022). Moreover, overly optimistic claims about the instrumental nature of GenAI for learning gains and successes can easily lead to adoptions that supersedes pedagogical deliberation (Chan, 2025).

In a current research project, we work with teachers in a specific field of vocational education in the Nordics, namely Social and Health Care education, which combines nursing, support, and practical assistance for, for example, the elderly. A growing number of students in this field originate from non-native-speaking countries, and the majority of them are women (Aarkrog, 2020).

In the project, researchers, teachers and didactical consultants collaborate to strengthen the participation of these students, both in school and subsequently in the Danish labor market. The project builds on the thesis that GenAI as a technology with large language-based capacities can be applied as a resource for reducing the language- and participation-related challenges that bilingual women experience in Social and Health Care education. Over the last two years there has been increasing recognition of the deep interconnection between vocational practice and digitalization, specifically for migrant and multilingual workers (Bradley et al., 2025; Lindström & Hashemi, 2019), along with a growing awareness that pedagogies integrating GenAI as a specific tool to support these groups are both overlooked and needed (e.g., Creely & Barnes, 2025).

As one entry point to achieving better participation of bilingual students, the project engages teachers through an action-learning and design-thinking inspired workshop series (e.g. Lindvig & Mathiasen, 2020; see also Schmitt & Brutzer, 2025). Over six-months, teachers meet to develop and refine custom-made chatbots intended to support bilingual students’ learning and participation. Teachers themselves define the pedagogical challenge and how they wish to address it (e.g., developing a chatbot that supports subject-specific language training, understanding Social and Health Care concepts, or providing an AI study-buddy for questions about life and health care in the Nordics). Between meetings, teachers test the chatbots under real-life classroom conditions, resulting in solutions that vary in breadth and depth.

It has become evident through the project, which will continue to work with teachers in 2026, that not all participants succeed in developing fully functional and scalable chatbots. This seems to be due to various reasons, both external factors such as lack of time and support in the school ecosystem, but also to questions which learning and support needs are being identified for bilingual students. Nevertheless, even when they do not succeed technically, participants engage in meaningful learning about the potential and challenges of GenAI as an element in their teaching.

In this presentation we will dive deeper into the question what types of learning and learning processes participants experience in the tension between creating a deliverable product and attending to their own interests, problem-definitions and contextual constraints. Through the lens of Cultural Historical Activity (CHAT, e.g. Engeström, 1987) we analyze the contradictions induced by both the workshop design and the affordances of GenAI.

In a context marked by multiple and entangled crises — social, ecological, democratic and epistemic — education research is increasingly challenged to interrogate not only what counts as knowledge, but also how knowledge is produced, mediated and mobilised. As Barnett (1997, 2007) argues, higher education is no longer a stable site of knowledge transmission, but a space of uncertainty in which learners must develop judgement rather than certainty. The rapid expansion of artificial intelligence (AI) in higher education intensifies these challenges, reshaping the conditions under which knowing and acting take place and amplifying tensions between automation, performativity and educational responsibility (Biesta, 2010).

This paper addresses these concerns by presenting a pedagogical experiment that reframes AI not as a tool for knowledge optimisation, but as a contrasting device for critical inquiry within a situated learning framework grounded in University Social Responsibility. Rather than enhancing efficiency or replacing interpretative work, AI is mobilised to expose the limits, assumptions and reductions inherent in algorithmic forms of representation.

Developed within the master’s course Sociology of Physical Activity and Health at the Faculty of Sport, University of Porto, the experience engaged students in a service-learning project connected to Turma do Mar, a community-based surfing initiative involving participants in situations of social vulnerability. The pedagogical design deliberately combined theoretical instruction with embodied participation and social intervention in a real-world context, foregrounding the entanglement between academic knowledge, lived experience and ethical responsibility.

The experiment responds directly to contemporary concerns within education research regarding the growing authority of data-driven and visual forms of knowing (Beer, 2019). Rather than positioning AI as a neutral or authoritative source of interpretation, generative AI tools were introduced at a later stage of the process to produce narrative descriptions based solely on photographic records of the fieldwork. These AI-generated narratives were explicitly framed as partial, decontextualised and provisional accounts, intended to be critically examined rather than accepted.

Students were invited to contrast AI-generated descriptions with their own reflective logbooks, interviews and collective discussions. This comparative work enabled them to identify tensions between visual appearance and lived experience, between algorithmic description and situated understanding. Through this process, AI functioned as a means of making epistemological assumptions visible, exposing the limits of computational mediation in capturing relational, embodied and ethical dimensions of social practice.

From the perspective of “knowing and acting”, the experience highlights how critical judgment emerges through friction rather than coherence. Learning was not oriented towards producing definitive interpretations, but towards cultivating students’ capacity to navigate uncertainty, question representational authority and recognise the interpretative labour involved in educational research and professional practice.

The findings suggest that AI can contribute to critical educational aims when its use is carefully bounded, pedagogically mediated and embedded in socially responsible practices. In a time of polycrisis, this approach offers a way of engaging with technological change while reaffirming the public role of higher education as a space for ethical discernment, reflexivity and situated knowledge production.