This conceptual paper aims to explore the complex nature of integrating AI technologies in teacher professional learning, highlighting the potential for AI to synergize teacher noticing and decision-making processes, support adaptive teaching, foster alignment with competence frameworks, and cultivate professional vision, thereby framing teacher practices within the framework of professional vision. We argue that rather than looking at the process of adopting AI solutions by teachers from a technology perspective or how teachers contribute to designing and developing such tools, we take the perspective of the teacher and ask how such tools are meaningfully integrated into teacher practices. In our conceptual paper, we illustrate the case of a novel approach to the teacher training model where the development of teacher' professional vision and professional learning is combined with the design of the AI solutions. We argue the importance of involving teachers into the design of AI solutions through professional learning models to support teachers to develop knowledge-based reasoning skills and at the same time to learn about pedagogical concepts and develop new mental models.

This paper introduces a learning analytics policy and strategy framework developed by a cross-European research project team — SHEILA (Supporting Higher Education to Integrate Learning Analytics), based on interviews with 78 senior managers from 51 European higher education institutions across 16 countries. The framework was developed adapting the RAPID Outcome Mapping Approach (ROMA), which is designed to develop effective strategies and evidence-based policy in complex environments. This paper presents four case studies to illustrate the development process of the SHEILA framework and how it can be used iteratively to inform strategic planning and policy processes in real world environments, particularly for large-scale implementation in higher education contexts. To this end, the selected cases were analyzed at two stages, each a year apart, to investigate the progression of adoption approaches that were followed to solve existing challenges, and identify new challenges that could be addressed by following the SHEILA framework.

Integration of technology in schools rests on effective teacher education programmes that help teachers create new teaching and learning methods and adopt them for classroom use. Social learning processes play a key role in this, but there is a lack of understanding of their role in technology adoption and in evidencing them in teacher education programmes. Using the knowledge appropriation model, we propose a self-report questionnaire instrument to evidence knowledge creation and learning practices during training. With a sample of N = 109 in-service teachers participating in the Teacher Innovation Laboratory, a teacher professional development programme that is built around school−university co-creation partnerships, we demonstrate the instrument to be reliable and to differentiate between groups who completed different programmes. The instrument predicted intended adoption of technology-enhanced learning methods beyond individual level constructs, highlighting the important role that social practices play for the eventual adoption of technologies in the classroom.

Student agency, or agency for learning, refers to an individual’s ability to act and cause changes during the learning process. Recently, learning analytics (LA) has demonstrated its potential in promoting agency, as it enables students to take an active role in their learning process and supports the development of their self-regulatory skills. Despite the growing interest and potential for supporting student agency, there have yet to be any studies reviewing the extant works dealing with the use of LA in supporting student agency. We systematically reviewed the existing related works in eight major international databases and identified 15 articles. Analysis of these articles revealed that most of the studies aimed to investigate student or educators’ agency experiences, propose design principles for LA, and to a lesser extent, develop LA methods/dashboards to support agency. Of those studies developing LA, none initially explored student agency experiences and then utilized their findings to develop evidence-based LA methods and dashboards for supporting student agency. Moreover, we found that the included articles largely rely on descriptive and diagnostic analytics, paying less attention to predictive analytics and completely overlooking the potential of prescriptive learning analytics in supporting agency. Our findings also shed light on nine key design elements for effective LA support of student agency, including customization, decision-making support, consideration of transparency and privacy, and facilitation of co-design. Surprisingly, we found that no studies have considered the use of LA to support student agency in K–12 education, while higher education has been the focal point of the LA community. Finally, we highlighted the fields of study and data visualization types that the studies mostly targeted and, more importantly, identified eight crucial challenges facing LA in its support of student agency.

Technology-enhanced learning solutions carry the potential for transforming teacher learning and the delivery of professional development activities. This article proposes that the tools that professionals use in their practice can be considered an important mediator for professional learning. We carried out three design experiments, the results of which were used to formulate the conceptual design of a digital learning ecosystem for fostering teachers’ professional learning, which is operationalized through different phases, namely competency-based planning of professional development, designing pedagogical practices, enactment of student-centered learning enriched with technology, and reflection upon own practice to plan comprehensively for competency-based professional development.

This paper explores the Estonian novice teachers’ learning and knowledge building (LKB) practices in the extended professional community during the induction programme using well-known knowledge conversion model. The assumption in this study is that a teachers’ participation in the extended professional community facilitates their professional development. The survey was conducted with 101 novices after their induction programme. The patterns of novices’ LKB practices in the professional learning community during the induction programme were explored. The analysis showed to what extent extended professional community may be formed during the induction year. LKB practice patterns among the novices were identified. We discovered that many novices felt that there was insufficient support from other teachers and from university experts. It appears that it is challenging to develop a coherent induction programme as the extended professional community of educators, where different partners collaborate and share professional knowledge is challenging.