Universities were already managing significant complexity. Faculty responsibilities, program demands, administrative requirements, quality assurance, and student expectations have all been intensifying. None of this is new.

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Then generative AI arrived, and suddenly every institution feels pressure to articulate a strategy. Universities must now reckon with the implications of AI for how they approach innovation, academic standards, research integrity, and institutional governance, all while taking into account the diverse needs of faculties and departments. The pressure is on, and it’s amplified by sector-wide uncertainty about how to respond in ways that are both meaningful and responsible.

‍

The sector’s response is understandable: pilots, new partnerships, committees, strategy groups, and tool experiments everywhere. Yet the results are fragmented. People working hard, but largely in isolation. Little shared visibility into what is helping, what is hindering, or what should be reconsidered.

‍

There is also the risk of superficial use cases. A chatbot here, an automated writing prompt there. They are easy to implement, but what has actually been gained? Or lost? In higher education, this can lead to uneven adoption across departments and uncertainty about whether these tools truly strengthen teaching, learning, or research.

‍

In the midst of so much activity, it becomes important to pause and ask what value is actually being delivered for students, faculty, and the wider academic mission.

‍

As we support universities with the advent of AI, one of the ideas the EDT&Partners team returns to is the importance of foundations. Before choosing a tool, what if we began by asking what universities actually need as the basis for building well?

‍

That is what we have tried to do with Lecture. It is not a platform to adopt. It is a set of modular, open-source tools that form an evolving framework. A framework that can serve as a foundation for building AI responsibly in higher education. It includes components that support course design, translation, assessment preparation, and feedback, but these are only starting points.

‍

What sets Lecture apart is that it is designed for education, shaped with pedagogy, responsible use, and academic realities in mind. For universities, this means alignment with academic integrity standards, supporting discipline-specific approaches to teaching and learning, and respecting the autonomy of faculty who develop courses, teach, and evaluate progress. We are also working with institutions to establish governance structures, risk protocols, and clear academic and technical guardrails. The aim is to help universities introduce AI with confidence and clarity.

‍

In this landscape, we see two different but related challenges emerging.

‍

The first is longstanding: how to strengthen teaching and learning, reduce administrative burden, and create more equitable and effective learning environments. Generative AI, used thoughtfully, can support this work by freeing up time, revealing patterns in data, and enabling more tailored learning and research support.

‍

The second challenge has been introduced by AI itself. Many universities are now trying to build very similar things, often at the same time.

‍

AI tutors. Planning assistants. Feedback tools.

‍

Some of this work is excellent. Much of it is rushed. And a significant amount consumes time and resources that could be better spent elsewhere. Within institutions, different faculties may be developing parallel tools that do not connect or scale, making long-term improvement difficult to achieve.

‍

This is part of what Lecture aims to address. It offers modular components and supports flexible use of different models depending on the task. Our aim is to help universities create right-sized solutions without having to build everything from the ground up.

‍

Sometimes that means freeing up faculty time by easing elements of course design or feedback. It might mean enabling scaffolded critical thinking exercises that respond to where a student is in their understanding. Or simply reducing the burden of everyday tasks such as translation, documentation, or program mapping. In higher education, these benefits can also extend to research support and faculty development.

‍

Lecture is being developed with colleagues across schools, universities, and education companies. It is designed to run within an institution’s own infrastructure, with no vendor lock-in. This gives organizations full control over data, deployment, and integration with existing systems. For higher-ed leaders, this means an approach to AI that respects institutional autonomy, academic data governance requirements, and the varied needs of departments and programs.

Lecture has been shaped through early collaborations supported by AWS. One example is the University of Luxembourg, where Lecture was integrated into the university’s existing learning platform, allowing instructors to generate auto-graded assessments, scaffold feedback, and design personalized learning content. Faculty were involved from the start through co-creation workshops and responsible AI training, ensuring the work aligned with pedagogical goals and academic standards. Following a successful pilot, the university is now scaling Lecture for broader use.

‍

We want to move past simple one-to-one interactions such as an instructor using a tool or a student working with an AI tutor. These uses are not problematic on their own, but they are limited. Instead, we are exploring more dynamic approaches, where faculty judgment, student progress, and system design shape one another. Lecture is being used to test this in practice, through co-designed courses, scaffolded assessment preparation, structured feedback, and adaptive materials. These approaches do more than save time. They create opportunities to deepen teaching, learning, and inquiry.

‍

In higher education, this can support more personalized pathways within large and diverse cohorts, and help instructors guide richer forms of analysis, collaboration, and problem solving.

‍

Lecture is one step toward a shared foundation of generative AI for education. We are continuing to develop it with new partners, including a recent collaboration with the University of Bath. We are committed to sharing what we learn, so together we can build faster, go further, and strengthen the collective knowledge of the sector.

‍

As we navigate these questions, we want to work alongside others who are asking the same ones, across universities, schools, and the wider EdTech community. If this resonates, let us find ways to build together.

‍

The sector’s response is understandable: pilots, new partnerships, committees, strategy groups, and tool experiments everywhere. Yet the results are fragmented. People working hard, but largely in isolation. Little shared visibility into what is helping, what is hindering, or what should be reconsidered.

‍

There is also the risk of superficial use cases. A chatbot here, an automated writing prompt there. They are easy to implement, but what has actually been gained? Or lost? In higher education, this can lead to uneven adoption across departments and uncertainty about whether these tools truly strengthen teaching, learning, or research.

‍

In the midst of so much activity, it becomes important to pause and ask what value is actually being delivered for students, faculty, and the wider academic mission.

‍

As we support universities with the advent of AI, one of the ideas the EDT&Partners team returns to is the importance of foundations. Before choosing a tool, what if we began by asking what universities actually need as the basis for building well?

‍

That is what we have tried to do with Lecture. It is not a platform to adopt. It is a set of modular, open-source tools that form an evolving framework. A framework that can serve as a foundation for building AI responsibly in higher education. It includes components that support course design, translation, assessment preparation, and feedback, but these are only starting points.

‍

What sets Lecture apart is that it is designed for education, shaped with pedagogy, responsible use, and academic realities in mind. For universities, this means alignment with academic integrity standards, supporting discipline-specific approaches to teaching and learning, and respecting the autonomy of faculty who develop courses, teach, and evaluate progress. We are also working with institutions to establish governance structures, risk protocols, and clear academic and technical guardrails. The aim is to help universities introduce AI with confidence and clarity.

‍

In this landscape, we see two different but related challenges emerging.

‍

The first is longstanding: how to strengthen teaching and learning, reduce administrative burden, and create more equitable and effective learning environments. Generative AI, used thoughtfully, can support this work by freeing up time, revealing patterns in data, and enabling more tailored learning and research support.

‍

The second challenge has been introduced by AI itself. Many universities are now trying to build very similar things, often at the same time.

‍

AI tutors. Planning assistants. Feedback tools.

‍

Some of this work is excellent. Much of it is rushed. And a significant amount consumes time and resources that could be better spent elsewhere. Within institutions, different faculties may be developing parallel tools that do not connect or scale, making long-term improvement difficult to achieve.

‍

This is part of what Lecture aims to address. It offers modular components and supports flexible use of different models depending on the task. Our aim is to help universities create right-sized solutions without having to build everything from the ground up.

‍

Sometimes that means freeing up faculty time by easing elements of course design or feedback. It might mean enabling scaffolded critical thinking exercises that respond to where a student is in their understanding. Or simply reducing the burden of everyday tasks such as translation, documentation, or program mapping. In higher education, these benefits can also extend to research support and faculty development.

‍

Lecture is being developed with colleagues across schools, universities, and education companies. It is designed to run within an institution’s own infrastructure, with no vendor lock-in. This gives organizations full control over data, deployment, and integration with existing systems. For higher-ed leaders, this means an approach to AI that respects institutional autonomy, academic data governance requirements, and the varied needs of departments and programs.

Lecture has been shaped through early collaborations supported by AWS. One example is the University of Luxembourg, where Lecture was integrated into the university’s existing learning platform, allowing instructors to generate auto-graded assessments, scaffold feedback, and design personalized learning content. Faculty were involved from the start through co-creation workshops and responsible AI training, ensuring the work aligned with pedagogical goals and academic standards. Following a successful pilot, the university is now scaling Lecture for broader use.

‍

We want to move past simple one-to-one interactions such as an instructor using a tool or a student working with an AI tutor. These uses are not problematic on their own, but they are limited. Instead, we are exploring more dynamic approaches, where faculty judgment, student progress, and system design shape one another. Lecture is being used to test this in practice, through co-designed courses, scaffolded assessment preparation, structured feedback, and adaptive materials. These approaches do more than save time. They create opportunities to deepen teaching, learning, and inquiry.

‍

In higher education, this can support more personalized pathways within large and diverse cohorts, and help instructors guide richer forms of analysis, collaboration, and problem solving.

‍

Lecture is one step toward a shared foundation of generative AI for education. We are continuing to develop it with new partners, including a recent collaboration with the University of Bath. We are committed to sharing what we learn, so together we can build faster, go further, and strengthen the collective knowledge of the sector.

‍

As we navigate these questions, we want to work alongside others who are asking the same ones, across universities, schools, and the wider EdTech community. If this resonates, let us find ways to build together.

‍