5.4 Full Roadmap for Teacher Education Institutions

From reacting to strategy Most institutions do not start from zero. They start from a patchwork of individual initiatives, informal tool use and unresolved concerns. The first stage makes that starting position visible and turns it into a shared strategic direction. Leadership opens the process rather than delegating it, because later stages require decisions about staffing, resources and curriculum that need institutional support. The institution assesses its readiness, needs and priorities, and establishes a cross-functional steering team that includes teacher educators, IT, students and quality assurance. Involving students is not symbolic: they experience the curriculum directly and, in teacher education, are also members of the future profession.

The stage is complete when the institution can state, in writing, where it stands, what it wants to achieve and who is responsible for taking the work forward.

Guidance comes early because uncertainty can become a major barrier to adoption. Staff who do not know what is permitted may either avoid AI altogether or use it without shared expectations. The institution develops institution-wide guidance covering AI use, data protection, academic integrity, disclosure expectations and a process for reviewing and approving tools. The decisive design choice is the framing: guidance should explain what is possible and under which conditions, rather than consist mainly of prohibitions. Draft guidance is circulated for feedback before adoption, improving both its practical usefulness and its legitimacy.

The stage is complete when staff can readily find a clear answer to the question “Am I allowed to do this?” and know where to turn when a case falls outside the guidance.

Teacher educators cannot model practices they have not had the opportunity to explore themselves. This stage builds their capacity through professional learning that is sustained rather than one-off, connected to content and subject didactics rather than focused mainly on tools, and embedded in practice through collaboration, coaching and structured reflection. These are features consistently associated with effective teacher professional development (Lipowsky & Rzejak, 2021). Competences are mapped against the DUAL.AI.TEACHer Competency Framework and the UNESCO AI Competency Framework for Teachers so that development has both a direction and a reference point. AI integration should build on teacher educators' existing professional expertise rather than treat AI as a separate technical competence. A single workshop does not complete this stage. The stage is complete when teacher educators have sustained opportunities to develop, apply and reflect on AI-related practice in their own teaching contexts.

Here AI moves from extracurricular activity into the formal programme. Course and module documentation is reviewed, and learning outcomes are aligned with the competency framework so that what is taught, practised and assessed points in the same direction. A central design principle of this roadmap is that AI literacy and AI-enhanced pedagogy should be embedded within subject didactics rather than confined to a standalone module. A dedicated AI course may be useful as a transitional step, but it risks signalling that AI is a separate topic rather than part of everyday subject teaching. Co-design with teacher educators matters because those who will teach the revised programme need to have shaped it.

The stage is complete when AI-related learning outcomes are visible in the regular documentation of multiple subjects or modules and are connected to the wider programme rather than isolated in a single course.

Changing the curriculum on paper is not enough if teaching and assessment remain unchanged. This stage redesigns learning tasks and assessment formats so that they are authentic, multimodal and AI-aware, and so that assessment focuses on what future teachers can understand, justify, create and apply rather than on outputs that a generative system can produce on request. Equally important is the modelling effect: pre-service teachers should experience thoughtful AI-integrated teaching as learners before they are expected to use it as professionals. Teacher education has a distinctive opportunity here because students experience teaching practices while simultaneously developing their own professional practice.

The stage is complete when AI-related learning outcomes are reflected in actual teaching and assessment practice, not merely added to module descriptions or disclosure requirements.

Teacher education institutions do not only prepare future teachers; they also support those already working in schools. This stage develops continuing professional development into a structured offer rather than a series of isolated events. The same quality conditions as in Stage 3 apply: sustained engagement, a clear content focus, opportunities to practise, and follow-up through collaboration, feedback or coaching. In the DUAL.AI.TEACHer model, this work can be linked to the certification pathway developed in T2.6, allowing professional learning to build over time. Stage 6 also creates a two-way connection with schools: institutions contribute professional learning, while in-service teachers bring current classroom experience back into teacher education.

The stage is complete when in-service professional learning forms a coherent and sustained offer with clear progression, rather than a calendar of unrelated single events.

Redesigned modules, CPD offers and assessment formats are trialled with real cohorts under realistic conditions, and feedback from teacher educators, students and other participants is gathered systematically. Piloting reduces the risk of scaling an approach before it has been tested in practice. It also makes implementation visible: colleagues can see how an approach works in a context similar to their own, discuss the results and adapt it before wider adoption. The purpose of the pilot is therefore not to demonstrate that the design was correct, but to discover what needs to change.

The stage is complete when documented feedback from authentic implementation has led to identifiable revisions.

Evaluation asks whether the changes achieved what they were intended to achieve. Depending on the intervention, this may include competency development, participation, feasibility, equity, staff and student experience, and wider programme effects. Equity is considered explicitly because AI integration may reduce some inequalities while creating or reinforcing others, for example across subjects, staff groups or students with different levels of access and support. Evidence is then used to revise curriculum, professional development, assessment and implementation.

This stage also establishes a regular review cycle for the guidance developed in Stage 2, which will need to evolve as technologies, practices and regulatory conditions change.

The final stage moves successful work from project status into the regular structures of the institution. This means clear ownership, defined responsibilities, appropriate resources and integration into existing processes such as programme development and quality assurance. Scaling should follow the institution's own context, capacity and profile rather than a single generic model. Institutionalisation also means creating mechanisms for renewal: AI technologies, educational practices and institutional needs will continue to change, so the model itself cannot remain fixed.

The stage is complete when the work continues after the people who started it have moved on.