Conference Agenda

Teachers' digital skills are a fundamental aspect of their professional background in an era in which communication and data processing technologies are now a reality that pervades every sector of society, and especially schools. We are faced with teachers who are digital natives and others who are digital immigrants, with different profiles of skills and attitudes towards the use of information technology.

Clearly, educating teachers in the use and management of IT tools is essential. This action should be performed in a context of lifelong, lifewide and lifedeep learning (Garzon Artacho et al. 2020; Knight et al. 2023). However, the first step in this direction is out of doubt the assessment of the current situation, and this is precisely the purpose of the “Questionnaire on Teachers” Knowledge, Practice and Attitude towards Digital Technologies' that we are presenting.

The questionnaire was built in accordance with the six areas of DigiCompEdu, the European framework for the digital competence of educators (Redecker, 2017): 1: Professional Engagement; 2: Digital Resources; 3: Teaching and Learning; 4: Assessment; 5: Empowering Learners; 6: Facilitating Learners’ Digital Competence. A seventh area was added, relative to AI: Knowledge and use of AI. A part of the questionnaire’s items can be compared with similar items of the original study, while others were developed for inquiring further aspects, keeping however their pertinence to the area of reference. Moreover, further areas (General information, Openness to innovation in school and Use of digital programs and apps) were added. At present, the tool includes 8 scales: Digital skills, Sharing digital experiences, Getting students to manage ITC, Performing innovative practices, Teaching using ITC, Fostering equality of chances in ITC use, Using ITC securely and using Artificial Intelligence (AI).

The development of the questionnaire has followed and will follow these stages: 1) Planning; 2) Creation of the first beta version in Italian, Spanish and English; 3) Tuning of the beta version and creation of the alpha version in Italian (definitive); 4) Administration of this version to teachers from various countries (at present Italian and Chilean schools are participating); 5) Analysis of the data and comparison of the results. At present, stages from 1 to 3 were performed, 4 is ongoing and 5 will be performed during the next months. We count to complete all the stages for the month of May.

The tuning of stage 3 has been performed through administration of the items to a sample of Italian teachers and reduction of their number via correlational analysis, passing from 145 to 89. A Spanish version is undergoing at present an adaptation/revision and is being administered to a first sample of subjects The collected data will be analyzed and compared with the results coming from other studies (Lamschtein, 2022; García Beltrán and Salazar Guevara, 2022; Palacios-Rodríguez, Llorente-Cejudo, Lucas, M. and Bem-haja, 2025; Momdjian, Manegre and Gutiérrez‑Colón, 2024; Cortoni, 2021; Ancillotti, 2025 )

Across education systems, generative AI is moving rapidly from policy agendas into classrooms. This transition depends not only on infrastructure, regulation, or curriculum guidance, but also on the everyday organisational work of schools. In many schools, that work is carried by small, school-based AI reform teams: teachers and administrators who pilot AI-infused lessons, develop local guidance and safeguarding routines, produce demonstration materials, and respond to parents’ concerns about privacy, dependence, inappropriate content, and fairness. International discussions increasingly address ethics and governance, yet we still know comparatively little about the organisational micro-dynamics through which schools translate AI aspirations into everyday teaching practice—particularly when reforms unfold under tight timelines, visibility pressures, and uncertain pedagogical consequences.

This paper reports a qualitative case study from China that examines collaboration tensions within one school AI reform team. China represents a high-visibility implementation environment where schools may face strong expectations to demonstrate progress through showcase lessons and tangible deliverables. The purpose of this paper is not to claim universal generalisation from a single case. Rather, we use the case to clarify mechanisms and a descriptive vocabulary that can support international comparison. Specifically, we examine how (a) accountability schedules shape teachers’ opportunities to learn through trial and error; (b) role differentiation structures the sharing (or bottlenecking) of tacit judgement needed to use AI safely and effectively; and (c) parents’ risk perceptions and public scrutiny shape an emotional climate that feeds back into internal collaboration and learning. These issues are increasingly relevant internationally, including across Europe, where schools often have to balance innovation with safeguarding and where public confidence can significantly shape what schools feel able to test, disclose, or institutionalise.

To make these dynamics analytically legible, we use the Chinese lion dance as an interpretive metaphor and propose a rhythm–movement–atmosphere framework. The metaphor is not used as decorative storytelling; it functions as an organising lens for describing how collective reform work becomes synchronised—or misaligned—when pace, role coordination, and public attention interact.

Rhythm refers to temporal governance: who sets the pace, how deadlines are negotiated, what counts as “ready,” and whether cycles of experimentation, reflection, and revision are protected or compressed by milestone-driven accountability. Movement refers to how work and expertise are coordinated across roles (e.g., demonstration teachers, collaborating teachers, administrators): who carries responsibility for “public success,” how tasks and risks are divided, and whether tacit judgement becomes shareable team knowledge. Atmosphere refers to how external audiences shape the emotional and relational climate: how parental concerns, trust, and visibility affect what the team feels safe to discuss, test, and admit, and how misunderstandings can transform into pressure that reshapes practice.

We address three research questions: (1) How do accountability-driven milestones structure the rhythm of AI reform work, and what does this mean for teachers’ learning through trial and error? (2) How does role differentiation shape collaboration, particularly the sharing of tacit judgement required to evaluate AI outputs, respond to classroom uncertainty in real time, and manage risk boundaries? (3) How do parents’ concerns and public scrutiny shape the atmosphere of reform, and how does this atmosphere feed back into team learning and coordination?

The study contributes empirically by providing a fine-grained account of how an AI reform team operates under temporal pressure and heightened visibility, including the “invisible work” of checking outputs, rewriting materials, documenting safeguards, and negotiating acceptable classroom uses. Conceptually, it offers a deliberately modest framework: findings remain anchored in a China-based case, while rhythm–movement–atmosphere is offered as comparative lenses that other researchers can use to interrogate where, when, and how similar tensions emerge under different governance regimes and community expectations.

The development of teachers’ digital professional competence is a central challenge in today’s schools, where ever‑evolving technological solutions and digital practices continually reshape the conditions and possibilities for teaching. At the same time, research shows that professional development often becomes fragmented when it takes place at the individual level without a shared focus area that unifies the collective and creates anchoring in the school’s practice. This study investigates how teachers can jointly identify and develop a shared development question or object, which can serve as a unifying starting point for professional learning related to digital competence in schools.

The study is based on a research and development project in which teachers participate in collective learning processes aimed at strengthening their digital professional competence through exploratory and practice‑oriented approaches. Using design-based research and communities of practice as its theoretical framework (Wenger, 1998; Engeström, 2015), the study focuses on how teachers negotiate a shared development object—an area they wish to understand and improve together—and how this object gradually evolves through collective reflection and changes in practice.

The analyses show that the work of formulating a shared development question involves both opportunities and challenges. A key opportunity lies in the way a shared development object can strengthen collective reflection and ownership of digital professional development. It creates a shared language and a common purpose that can help build a culture of learning within the teaching staff, rather than one centered on individual technology use. Through collaborative exploration of digital tools in teaching, space is created for dialogue, sharing experiences, and developing professional judgment in relation to complex digital practices.

At the same time, several challenges tied to the process are identified. One of the most prominent is the tension between individual needs and collective priorities: Teachers have different experiences, digital skills, and subject‑specific perspectives, making it difficult to agree on what should constitute a “shared” development area. In addition, time pressures and organizational expectations may limit opportunities for in‑depth collective work. The study also highlights how power relations, role perceptions, and the school’s existing learning culture influence how a shared development object takes shape—and the extent to which it contributes to transformative learning for both individual teachers and the professional community as a whole.

The findings indicate that establishing a shared development question is not just a methodological or organizational measure but a professional and cultural negotiation that requires time, trust, and mutual recognition. However, the process can lay the groundwork for lasting change if supported by leadership, collaborative structures, and a shared understanding of digital competence as more than technical skills—as an integral part of professional practice.

This study contributes insights into how teacher communities can build shared learning trajectories related to digital professional competence and how the very process of developing a shared development object can function as a catalyst for collective professional learning and sustainable school development.

The use of evidence to inform teaching has become a priority in continuing professional development (CPD) and school improvement (Brown, 2015; Coldwell et al., 2017; Kennedy, 2014; Nutley et al., 2003; Owen et al., 2022; Slavin, 2020). Evidence-informed Practice (EIP) is based on the simple idea that instruction is strengthened when teachers draw on evidence of practice and relevant pedagogical research to guide what happens in their classrooms, rather than relying solely on their intuition or past experience (Brown, 2017; Nelson & Campbell, 2017). However, the enactment of EIP in schools remains uneven and difficult to sustain (Dagenais et al., 2012; Hemsley-Brown & Sharp, 2003; Malin & Brown, 2019).

Persistent barriers include issues of accessibility, relevance, and timeliness (Hering, 2016). Research is frequently inaccessible, locked behind paywalls or written in ways teachers find impractical (Schaik et al., 2018). Time constraints represent perhaps the most significant challenge (Cooper et al., 2017; Mandinach & Gummer, 2016). Meaningful engagement with classroom evidence and pedagogical research, and conducting authentic critical reflection is time-intensive and can exacerbate workload pressures in a profession already experiencing high levels of attrition and leadership turnover (Borman & Dowling, 2008; Finnigan et al., 2016). As a result, a persistent gap remains between researchers that produce scholarship and educators who ultimately decide what happens in classrooms, stunting professional learning and frustrating efforts to improve schools (Farley-Ripple et al., 2018).

This paper introduces Deliberative Instructional Agents (DIAs), a new class of educational technology for specialised instructional coaching that leverages advances in AI to make Evidence-informed Practice more feasible within the realities of everyday teaching. DIAs integrate the interrelated processes of evidence capture, research-informed analysis, and critical reflection into a single system. The result is an epistemic engine that efficiently situates teachers at the intersection of evidence of practice, pedagogical research, and their own professional experience, creating the conditions for situated professional learning as a matter of routine. In doing so, DIAs lay the groundwork for a new sociotechnical infrastructure for professional learning and school improvement (Lang et al., 2017).

To illustrate this potential, we present exploratory research using a DIA designed to support dialogic pedagogy, an instructional approach that emphasises learning through purposeful discussion, reasoning, and debate (Alexander, 2020; Barton, 2024; Knight, 2025; Mercer & Littleton, 2007). Specifically, the DIA integrates three innovative tools to support professional inquiry into dialogic teaching. Pierrot, a data collection tool, represents a technical breakthrough in speaker diarization, accurately transcribing and attributing speech during multi-person, in-person classroom discussions, without the need for specialised hardware. Cadence, the analytical component of the platform, automates transcript analysis using established research frameworks to distinguish between types of talk and applies an innovative mathematical model to identify critical instructional moments where there is opportunity for teachers to elicit deeper student engagement. Finally, Shape, an intelligent middleware, was developed to facilitate non-judgemental reflection on significant moments and trends in the learning analytics. This interactive function allows users to unpack the collated data and associated research through text-based conversation directly in the user dashboard, all while being guided by research-informed protocols for teacher noticing (van Es & Sherin, 2021; Mason, 2002).

How do teachers engage with evidence, analytics, and experiential knowledge during professional inquiry, and what does this reveal about how professional learning unfolds in practice? This paper will detail the DIA design and present results from exploratory research involving initial use of the proof-of-concept DIA. The research aims to advance theory by empirically examining professional learning as it unfolds through teachers’ engagement with evidence, analytics, and experiential knowledge during routine instructional inquiry.