The decline in students’ performance in international assessments such as PISA has intensified calls for action to improve educational quality and strengthen teachers’ professional development -PD- (Salas-Pilco et al., 2022). When intentionally designed and pedagogically grounded, technology has the potential to support learning not only among students but also among teachers. Yet, despite the growing availability of evidence and digital tools for education, teaching as a profession continues to face challenges in engaging with evidence about effective practice (Ferguson & Clow, 2017). Teacher learning is a critical mechanism for translating evidence into changes in teaching practices and a lever for student learning (Tammets & Ley, 2023). In this context, Learning Analytics (LA) and Artificial Intelligence (AI) are increasingly being explored as means to support professional learning as they provide teachers with new forms of feedback, evidence, and opportunities for reflection (Pishtari et al., 2024). However, important challenges remain regarding the quality and interpretability of the evidence generated through LA, as well as the mechanisms through which such evidence can support teacher learning and knowledge transfer and ultimately contribute to closing the learning analytics feedback loop (Clow, 2012; Ferguson & Clow, 2017).  

Quick developments in generative AI are reshaping teaching work, assessment, and PD, creating both opportunities and risks for teacher learning, agency, and workload (Järvelä et al., 2025; Pishtari et al., 2024). Yet, teacher PD lags behind AI adoption, with many initiatives self-initiated, short‑term, fragmented, and weakly connected to classroom practice; evidence remains scattered and lacks longitudinal support (Salas-Pilco et al., 2022). The learning context in which teacher PD unfolds introduces another layer of complexity inherent to the use of workplace data. Unlike formal learning analytics contexts, workplace data is usually unstructured and characterized by an uneven definition of meaningful metrics (Bucalon et al., 2026). Research on LA and AI for teacher learning has mostly focused on teachers’ observable behaviors and teaching practices (e.g., Prieto et al., 2016) , dashboard use to support teacher awareness of learning processes (e.g., Dourado et al., 2021; Martinez-Maldonado, 2019). Recent work on teacher-AI complementarity highlights that AI can reshape teaching not only by automating tasks, but also by complementing or augmenting teachers’ work, with implications for teachers’ knowledge, skills, and  professional judgement. Yet, studies on teacher-facing tools remain comparatively scarce within LA and AI-in-education landscape, which has largely targeted student-facing applications (Topali el at. 2025). This highlights the importance to understand how teachers and AI can work together in ways that preserve and strengthen teachers’ professional agency and expertise (Ley et al., 2026). Thus, teacher learning becomes an important condition for productive teacher-AI complementarity: teachers need opportunities  to develop  AI literacy and to learn how to interpret, question, and pedagogically act upon AI- and data-generated insights in context. This requires not only the technical quality of data and AI outputs, but also their pedagogical relevance, actionability, and alignment with teachers’ knowledge (Pishtari et al., 2024). On top of access to data or AI tools, teachers also need knowledge, skills, interpretive frameworks, and opportunities for reflection to use them productively (Tammets & Ley, 2023). The underlying question pondered by the TAILA workshop is: “How can LA and AI be designed and enacted to support teacher learning while strengthening productive teacher-AI complementarity?”

The challenge is not simply how to provide teachers with more analytics or AI capabilities, but how to design conditions in which these technologies become meaningful resources for teacher learning and professional reflection. The LAK27 conference opens a space to discuss not only what these technologies can do, but how they can be designed and enacted to foster teacher learning. The TAILA workshop offers an opportunity for a collaborative space to examine current approaches, critically discuss their opportunities and limitations, and explore new ways forward. 

By the end of the workshop, participants will: 

• Have an overview of current approaches to support teacher learning. 

• Examine opportunities, limitations, and challenges associated with these approaches. 

• Identify conditions under which LA and AI can support teacher reflection and professional learning.

• Co-design principles and research questions for future work (e.g., a potential special issue on the topic).