New infrastructures for preparation

The future of educator preparation will not be built on the infrastructure of the past.

Credit hours, transcripts, isolated coursework, and episodic field experiences were designed for a world in which teaching was stable, predictable, and largely individual. They were built on assumptions that no longer hold: that learning is linear, that expertise can be measured in time, that readiness can be inferred from course completion, and that teaching is a solitary act. These structures are not simply outdated.They actively constrain what preparation can become.

To prepare educators for the complexity of contemporary learning, we need new infrastructures that align with the work educators will actually do and to help us shape what is possible for today’s learners. The legacy infrastructure of preparation, for example, is built on time. Candidates accumulate credit hours, complete semesters, and progress through programs based on calendars rather than capability.

Time serves as a proxy for learning, even though we know that learning does not unfold uniformly. It privileges those who can afford to move through programs at a prescribed pace, and disadvantages those whose lives require flexibility. 

Creating a future-ready learning infrastructure 

A future‑ready infrastructure must be built on capability, not time. Educator preparation programs need to acknowledge learning is personal and nonlinear. And then we need to model what that looks like. Competency-based, work integrated, credit for prior learning, 90-credit hour programs all need to become the norm but so do things like multiple entry points, flexibility to move from one program to another, and the ability to use work experience as field experience in order to integrate coursework with fieldwork.

And all of this needs rich, authentic, multidimensional evidence. It requires integrated learning records that capture what candidates can do, how they think, how they collaborate, and how they respond to the complexities of practice. These records must draw from multiple sources: design artifacts, reflections, team‑based assessments, AI‑enabled insight, and observations across contexts. They must make learning visible in ways that honor the depth of the work.

Integrating data and work-embedded pathways

This, in turn, requires data systems that are coherent and connected. Today, coursework and fieldwork often exist in separate silos. Candidates learn theory in one space and practice in another, with little integration between the two. Faculty and mentor teachers rarely share a common understanding of what candidates are working on or how they are developing.

A new infrastructure is needed to bridge these divides, allowing for an integration of coursework and fieldwork. It must allow faculty, mentor teachers, and candidates to see the same evidence, interpret it together, and design next steps collaboratively. It would support continuous feedback, not episodic evaluation, making learning visible not only for candidates, but for the teams who support them.

This infrastructure should also support flexible, work‑embedded pathways. Many of the most promising future educators are already working in schools as paraprofessionals, aides, tutors, and community educators.Yet traditional preparation structures often make it difficult for them to advance, creating barriers that disproportionately affect candidates from historically marginalized communities.

Earning while learning, teaching with teams

A redesigned infrastructure must remove these barriers by honoring the expertise candidates bring to the field and recognizing learning in real time. When candidates can earn while they learn and move through programs at a pace that aligns with their lives then equity as an aspiration becomes equity by design.

Team-based practice also calls for a different kind of infrastructure that supports differentiated, interconnected roles in school settings. This means creating the scheduling and logistical conditions for shared planning time and joint decision-making. Preparation programs can, in turn, model these structures so that candidates gain direct experience working as part of teams.

Supporting ethical use of AI

Finally, a future-ready infrastructure must support ethical use of AI and prepare candidates to interpret AI-enabled insight responsibly. If we want educators who can design responsive, relational, AI‑literate learning environments, we must build preparation systems that reflect those same qualities to support the future we are trying to create.