• Guidelines for Educators and School Leaders
  Practical guidance on using education data ethically, inclusively, and meaningfully to support student learning and school improvement. Building on the case studies, learning stories, and workshops, the guidelines promote reflective professional practice, stronger collaboration, and critical engagement with AI and EdTech. The Guidelines are particularly useful for those practitioners who want to adopt data use and inquiry practices in schools.
• Policy Recommendations for Meaningful Use of Data in Education
  This set of recommendations for policy makers at European, national and local levels highlight coherent digital strategies, robust governance, ethical data practices, and professional development, aiming to use data in a way that fosters equity, innovation, and is pedagogically relevant.
• MOOC Resource Pack
  A comprehensive training resource for teachers and school leaders, covering topics such as teacher inquiry methods, education data analytics examples, and the evaluation of data-driven digital tools for safety, equity, and pedagogical value.

All the case studies, learning stories and other reports are compiled in a ‘knowledge base' section on the website, allowing searching for outputs by country and topic.

Final event in Madrid - Education stakeholders discussed about the key takeaways of the project

 We presented the results of the Agile EDU project to education stakeholders from all over Europe in Madrid at the EMINENT conference. In parallel sessions, participants discussed the project's recommendations and guidelines. One session focused on lessons learned for policy makers and one for practitioners.

Some points that were raised in the roundtable sessions:

• School self-assessments, such as SELFIE can be a good starting point for defining challenges that can be tackled through data collection. When schools perform the same self-assessment a second time, they tend to evaluate themselves more negatively, as a result of being more aware of their areas for improvement.
• Although schools need help interpreting data, e.g., found on dashboards of national standardised tests, they can also benefit from support in translating these interpretations into concrete actions.
• Teachers need a more accessible format to communicate the Agile EDU guidelines due to the limited time they have to read documents. Practice examples that are ready for use can be beneficial.
• When schools have data on school indicators, these need to be tailored for communication with external stakeholders, including parents.
• Data can help identifying equity challenges. Data on access to digital devices, parent-teacher relationships, student background, and also environmental sensor data to investigate how students with disabilities are affected by environmental factors (e.g., noise, air quality).
• When OpenAI opened its API to use, municipalities in Nordic countries made agreements to develop their own AI platforms that keep student data away from commercial use. However, instead of each municipality developing their own solutions, there is benefit in doing this centrally, including for the group procurement of other digital technologies for schools.
• Participants discussed to what extent teachers can understand when their students use AI for their work. AI tools are also not efficient in detecting AI use. To make AI use more visible and be able to explore more meaningful uses, teachers can debate with their students about the benefits and harms of using AI (e.g., cognitive laziness) and reflect with them on the process of using AI.
• Students need to become more aware of data security issues and learn about actions to maintain it, but schools must address this topic in a more engaging way.
• To make data open to public access to promote data exchange, databases also need to heavily invest in cybersecurity against breaches.
• There is a need to facilitate researchers' access to data collected through EdTech tools, which can be help understanding what works pedagogically, ultimately beneficial both for EdTech and education researchers.
• All stakeholders can benefit from data literacy training. In past data analytics projects, some schools found out after analyzing the data they collected that their initial assumptions about student learning were not entirely correct. This shows how important it is to have a school team that ongoingly reflects on how to collect data on the impact of new measures taken in school.