The curated records of Task 2.2. The account view reads them grouped by level and domain; the database view opens the same records for filtering, sorting and downloading.
Teachers report a transition from occasional assistance with summaries and ideas towards regular use in preparing materials and designing learning activities. The account is given as a description of current routine rather than as a statement of intention, and it situates a design decision, and not merely a decision about use, within a single course.
A teacher reports assigning a voice to the model, creating several agents specialised in different areas of expertise, and selecting models that reflect a personal teaching style. The tool is not adopted as delivered but configured, and the configuration expresses a pedagogical preference rather than a technical one.
Teachers describe subjecting every AI output to a check for academic precision, pedagogical alignment and ethical standards before use, and describe the tool as a partner for refining ideas rather than an author. The framing that dominates the third round is augmentation rather than replacement, and it recurs regardless of the purpose of use.
Students describe using the tool to obtain direction, to get help with parts they do not understand and to identify the strengths and weaknesses of work they have already done, rather than to have the work done for them. The practice is located within the learning situation and depends on nothing other than the student's own decision as to what to ask.
A tool gives students immediate AI-generated formative feedback on text assignments against criteria the instructor defines. Teachers add contextual material and set expectations for each task, and these elements are passed to the model through quality-secured prompts, which keeps the criteria in the teacher's hands while the response time falls to the student's.
Students and teachers report declining to trust generated text, avoiding direct copying, and verifying academic material in particular, with citations identified as the element verified most frequently. The practice addresses the failure mode that respondents in every group identify as the principal challenge.
A published guide addressed to students positions generative AI as a producer of drafts rather than an authority. It teaches prompt construction from a minimal three-part form up to a five-part template, maps use onto the levels of Bloom's taxonomy, and closes with four practices: withhold identifying data, verify critical claims against at least two sources, treat the course instruction as binding, and state the purpose for which the tool was used.
A module on critical AI literacy for learning was offered to students from autumn 2025. Placing the subject inside a module rather than a workshop makes participation part of study rather than an optional addition to it.
Respondents in every group attribute improved results to their own learning: prompts have become more specific and goal-directed, extended exchanges are sustained more deliberately, and use has moved from a single tool to a set of tools selected for particular purposes. Explaining a task to the model is described as constituting a learning experience in itself.
Where formal training is absent or limited, competence is acquired through colleagues, peer networks, self-paced material and practical use. The channels differ by role: staff describe internal knowledge sharing and consultation with colleagues, while for students public video and social media are the most common route. The report finds that informal learning acts as a complement to formal provision rather than as a substitute for it.
Respondents across all groups report stating where and for what purpose generative tools were used, citing them where the work is academic, and reviewing generated content thoroughly before it reaches anyone else. Disclosure is expressed as a description of purpose rather than as an acknowledgement of a tool.
Respondents in every stakeholder group report withholding personal, sensitive and confidential material from prompts, preferring platforms the institution has licensed to publicly available ones, and using temporary chat modes so that no record is retained. The report characterises the pattern as a habit rather than as compliance with a rule.
An open education faculty offers a two-year associate degree in AI-assisted coding, carrying 120 ECTS. The programme is not a module added to an existing curriculum but a qualification whose outcomes are written around large language models, agent-based systems and generative AI applications, alongside algorithmic thinking, cloud technologies and AI-supported software testing.
The creation of personalised AI-aided learning appears in the plan for the 2025 academic year of an open university. Placing the intention in an annual plan is what separates it from an experiment: a plan carries a date, an owner and a review.
A teacher describes artificial intelligence being applied as part of study modules while modules about artificial intelligence are offered to teachers and students alike, often using the technology itself as the starting point. The account records curriculum change from two directions at once: the technology enters existing modules and becomes the subject of new ones.
The Graduate Education Institute revised its thesis writing guide in 2024 so that every student signs a declaration form, naming the generative AI programme used and the kind of support received. The form is a required document rather than a recommendation, which moves disclosure from good practice to a condition of submission.
A policy addressed to the whole university community aims at transparency on all sides while setting limits on where use is permitted. Use is prohibited during examinations and may be permitted for preparing assignments or activities provided transparency is ensured, which draws the line at the assessment event rather than at the tool.
A funded pilot examines the challenges and opportunities that generative AI poses for assessment in further and higher education. Placing the question inside a funded research project rather than a policy circular is what allows assessment redesign to be tested before it is prescribed.
Guidelines addressed to teachers link the use of ChatGPT directly to remote assessment. The pairing matters in a distance institution, where assessment is conducted at a distance by default and the tool question cannot be separated from the examination question.
A chatbot answers student questions on enrolment, examinations and graduation. The institution is explicit that the deployed assistant is rule-based rather than generative, while research on integrating large language models into academic and non-academic support runs separately at the Student Affairs Department of the Faculty of Open Education.
Operational practices introduced and expanded since October 2024 include AI-based early-warning systems for students, AI-assisted feedback, empathic conversational agents, pilots for automated assessment and personalised learning support. The range shows an institution moving from single tools to a portfolio held under one project.
Pilot projects at an institution the source does not name trial two institutional tools: a digital assistant intended to support students as a study companion, and a module writing assistant intended to support staff in creating and reworking content. The pairing repeats the pattern seen elsewhere, where the same institution builds one instrument for learners and another for the staff who design their learning.
A chatbot named Minerva has been in use on the university web pages since 2023. Participants in the third round still name it as the institutional practice they recognise, which makes it one of the few assistants in the partnership that respondents identify by name across rounds.
The staff counterpart of the student guide addresses academics and researchers. On the teaching side it covers learning outcomes, operations inside the learning management system, assessment and rubrics, accessibility and inclusion. On the research side it covers preparation for literature review, structured reading notes, translation, method and reporting checklists, and reference verification. Data minimisation, disclosure and institutional policy compliance are treated as obligations rather than recommendations.
Resources developed for educators and learners include a course on AI in teaching hosted in the learning management system, an experimentation environment, and a regional hub whose materials teachers can integrate into their own courses. Delivering staff development inside the same system staff already teach in removes one barrier to participation.
A workspace of materials on the basics of artificial intelligence in university work was published for teachers, researchers and support staff in 2025 and is updated as new material appears. A living workspace answers a difficulty that fixed guidance does not, since the tools change faster than documents are revised.
The vice-rectorate for educational innovation runs a training and awareness programme built on several pillars, accompanied by guides for generalists, teachers and students and by courses for teaching, administrative and service staff. Locating the programme in a vice-rectorate gives it a budget line and a permanent owner.
The university maintains a set of instruments that are revised regularly: an AI guide, recommendations for action, a service agreement on the use of AI systems, and a register of processing activities. The combination places advisory guidance and data protection documentation in the same institutional package.
A policy issued by the rector in April 2024 is accompanied by instructions for using AI-based applications in studies. Issuing the instrument at rectoral level gives it institution-wide reach rather than leaving adoption to individual faculties.
A framework introduced in 2025 provides internal guidelines for integrating AI across educational, administrative and support services, with the stated aim of turning the university into an AI-enabled institution. It answers the gap left a year earlier, when policy was still under discussion and the decision was delegated to individual teachers.
An AI committee was established alongside pilot tests of tools in learning design and IT support and seminars for staff, students and the general public. Extending the seminars beyond the university is unusual in the partnership and treats AI literacy as a public rather than an internal concern.
While institution-wide policy was still under discussion, the university adopted an interim rule under which each teacher decides the level at which AI may be applied in learning activities and may rule it out entirely. The arrangement keeps courses operating during a policy vacuum at the cost of leaving students facing different rules in different courses.
A steering committee covering the whole institution was installed in October 2024, at a point when the university reported no institutional practices in place. The sequence constitutes a finding in itself, in that the governance structure was established before any application had been deployed.
A working group was set up to develop an institution-wide AI policy and guidelines, with availability announced for 2026. Recording the group rather than the policy is itself the finding: in the second round the institution had teacher guidelines but no institutional instrument.
Students may use generative AI to support their learning and, unless instructed otherwise, in assessment preparation, provided the use is acknowledged. The policy pairs permission with three prohibitions covering confidential information, personal data and copyrighted assessment content, and warns that over-reliance may hinder skill development.
A library guide published in May 2025 proposes structures for making the use of AI tools transparent: which tools were used, for what purpose and to what extent. A related journal policy states that authorship cannot be attributed to AI, that its use is confined to improving language or readability, that it must be cited, and that authors remain responsible for the validity of what it generates.
Information security guidelines issued in December 2023 direct users to work primarily with Microsoft Copilot on a university device while logged in with university credentials. The instrument governs data exposure by prescribing where the interaction takes place rather than by listing prohibited content.
The institution updated its AI guidelines in April 2025 and its recommendations for action in July 2025, both advisory in character, while an overarching AI strategy and guidelines for AI in examinations were under development. Alongside them the university maintains a mandatory internal regulation of learning analytics to ensure data privacy, which is the one binding instrument in the set.
New assessment approaches aim to keep tasks robust to the use of AI while maintaining academic integrity and providing differentiated support for diverse needs. Staff capacity is developed through training on responsible use, ethics and inclusivity, with the institution reporting a shift from high-level guidance to concrete application.
A dedicated research unit was established in 2023 to study developments in data analytics and artificial intelligence in higher education and to run projects, with training planned at several educational levels. The institution reports that the infrastructure of the unit was still being set up at the time of the survey.
The university runs experimental environments that bring a feedback centre, automated grading, personalised support and an immersive collaboration hub together with access to various models for testing. The arrangement gives staff a bounded space in which tools can be trialled before any decision about institutional deployment.
An administrator describes access to a commercial model being extended to all staff, both teaching and administrative, and frames it as a move from isolated or experimental use to integration across the university. Universal access changes the equity question from who can afford a subscription to what the institution has licensed.
A student reports that every university in the country now offers courses related to AI, and a teacher in the same round describes AI-related content being incorporated into curricula alongside the establishment of dedicated research centres. Two independent accounts describe the same national movement from different positions.
A support staff respondent reports that certificate programmes, associate degrees and undergraduate degrees in the field are available at universities, alongside a large number of scientific events each year. The account records provision at the level of qualifications rather than of individual courses.
A partnership deliverable sets out a framework for learning design with generative AI, organised in stages from analysis and planning through activity design to development and production. It is the only instrument in the material that addresses curriculum design directly rather than regulating the use of tools inside an existing design.
A teacher reports that questions for a national standardised language examination are now generated using artificial intelligence. The account is brief but marks a national assessment body applying generative tools to item production rather than regulating their use by others.
Germany funds specific projects to expand research and knowledge in AI, among them the AI Campus platform. The partnership records it first as a national practice and later as one of four federal initiatives, with a regional hub bundling transfer and networking in North Rhine-Westphalia.
A government action plan published in January 2025 sets out a roadmap for maximising the potential of AI across the country. Recorded as a change in nationwide practice rather than in policy, it marks the point at which the national conversation shifts from regulation towards deployment.
The conference of Spanish rectors published recommendations covering opportunities, risks, ethics and evaluation. The document is not law but guides the university system, and it explicitly delegates implementation to individual universities while proposing shared principles, competence definitions, internal discussion processes, equity of access and an assessment of ecological cost.
A draft law approved by the government in March 2025 aligns national provision with the EU AI Act. Its appearance in the second round is one of the clearest instances of European regulation propagating into national legislation during the observation period.
Material published in April 2025 by the national agency for education and the ministry gathers legislation and recommendations for 2025, prepared with practitioners, researchers and other stakeholders. The partner records the policies it contains as advisory rather than binding.
A national task force was established and seminars and workshops on generative AI and large language models in education were held. The pairing of a committee with public events is the shape national activity takes where no education-specific framework yet exists.
Guidelines for the confederation were published on 1 July 2025 and a report on AI in education followed a day later. In a partnership where the previous round recorded no national policy at all for this country, the appearance of two instruments within two days marks a distinct change of pace.
Respondents describe a framework in which the European AI Act operates together with recent national legislation of September 2025, ministerial guidelines for schools and a national AI strategy covering 2024 to 2026. The layering of European and national instruments is what respondents themselves report as governing use.
An administrator describes a national framework combining the national AI strategy, an AI in education policy document and action plan for 2025 to 2029, and generative AI guidelines from the research council. The same account states that no fully standardised regulation for generative AI in education exists, leaving universities responsible for their own institutional policies.
The regulation is the single most frequently named instrument across countries in the third round, cited by respondents regardless of where they work. It functions as the common reference layer beneath national frameworks that otherwise differ widely in form and force.
A support staff respondent reports that the European regulation takes effect nationally on 2 August 2026 while its prohibitions already apply. The distinction between staged application and full entry into force is what institutions have to plan against.
The Council of Higher Education published an ethical guide covering the use of generative AI in scientific research and publication in higher education institutions in May 2024. The partner reports that the guidance is advisory, which leaves each university to convert it into binding institutional rules.
Principles established in September 2024 were circulated to all public institutions, universities included. The instrument reaches higher education not through education policy but through public administration law, which is a distinct route into the sector.
A central portal offers generative AI applications to all universities in North Rhine-Westphalia and is accessible to both staff and students. The arrangement answers procurement, access and cost at the level of a state system rather than leaving each university to negotiate separately.
A national pilot introduces an educational version of a commercial assistant with teacher training and phased student access from late 2025. It is one of the few national instruments in the partnership that provides a tool rather than a rule.
The quality assurance agency curates resources aimed at helping the education sector use generative AI while maintaining academic standards, and has published supplementary advice on the tools. The instrument works through the quality assurance route rather than through legislation.
Assessors, reviewers and panellists are explicitly prohibited from using generative AI in their assessment activities, while applicants are advised to exercise caution over bias and data protection. The asymmetry between a prohibition for evaluators and advice for applicants is itself a policy design choice.
A programme launched in October 2024 in collaboration between the Council of Higher Education and the Presidency of Defence Industries channels AI in the defence industry into undergraduate senior projects and graduate theses. It is a national instrument that works through the curriculum rather than through regulation.