Thoughtful use of AI starts with people, not technology. It works best when AI becomes part of professional and collaborative work in ways that support ongoing reflection, conversation and shared learning.*
These essays explore what happens when generative AI becomes part of research, evaluation, facilitation and other forms of professional practice. The opening essay introduces the wider question of what better work with AI would mean. Those that follow examine how this question plays out in individual practice, shared work, interpretation, participation, evaluation, organisational learning and the wider ethical landscape.
Together, they ask what needs to remain visible, discussable and open to judgement when AI becomes part of how work is framed, produced and reviewed. Their focus is not primarily on the technology itself, but on how practitioners, teams and organisations can use it in ways that strengthen rather than weaken participation, judgement, learning and professional practice.
The essays can be read individually or followed as a developing series.
Essays in the collection
What would better work with AI look like?
Introduces the central question running through the collection: whether AI is contributing to better work, rather than simply producing useful-looking outputs. It considers purpose, participation, interpretation, responsibility and the organisational conditions that help people question practice and learn where AI should continue, change or stop.
AI as a thought partner: reflections on collaborative practice and systems work
Begins with individual practice. Drawing on personal experience, this essay considers how AI can support early thinking, test alternative framings and help prepare ideas and material for further work, while leaving interpretation, relationships and responsibility with people.
AI prompts for shared thinking: a light framework for purposeful prompting
Builds on the previous essay by introducing a simple prompting framework that keeps purpose, context, relationships and intended use in view. It treats prompts as part of a wider thinking process rather than instructions for producing a finished answer.
Working with AI in the room: authorship, responsibility, and collective judgement
Moves from individual use into shared work. It examines what changes when AI-generated material enters meetings, workshops, strategy discussions and collaborative writing, including questions about authorship, trust, responsibility and how collective sense-making is held.
AI and the authority of the first synthesis
Looks more closely at the power of early interpretation. It considers how an AI-assisted summary, set of themes or draft strategy can shape what a group notices and treats as settled, and why the first synthesis needs to be examined rather than simply polished.
AI can produce the record of participation without the participation
Extends the argument into participation and representation. It asks what is missing when AI can generate transcripts, themes and reports that resemble the products of engagement, but not the influence, relationships, disagreement and learning through which meaningful participation occurs.
How do we know whether AI is improving the work?
Turns to evaluation. It shows why output checks alone are insufficient. Drawing on established approaches to indicators, evaluative rubrics and monitoring, evaluation and learning, it explores how AI-supported work can be followed into later discussion, decisions and consequences.
Rethinking education, training and professional learning for AI-supported practice
Extends the question of better AI-supported work into education and professional learning. It asks what people still need to understand, practise and experience for themselves, how judgement is developed and maintained, and where that judgement may move as AI becomes part of the work.
Working with AI: where to begin
Offers a practical starting point for organisations, teams and programmes wanting to engage with AI more deliberately. It sets out five areas for teams and programmes to work through, beginning with current use and moving through purpose, participation, responsibility and regular review rather than assuming that a complete framework must come first.
AI in place-based practice: what is shifting
Applies the collection’s ideas to place-based collaboration, where relationships, long timeframes and multiple organisations make questions of judgement, participation and learning especially important. It considers how AI may influence preparation, issue framing and collective sense-making in these settings.
Seeing the wider ethical picture around AI development and use
The first of a two-part series, this post steps back from the practice-focused essays to place their questions within a broader ethical landscape. It distinguishes structural, organisational and practice-level concerns, showing why different perspectives on AI often arise because they address different parts of the same system, and why each level calls for different kinds of responsibility and response.
Designing AI for the wider world: six principles for better development
Builds on the wider ethical framing in Part 1 by asking what those concerns might mean for the way AI is designed, developed and deployed. It brings together six principles around worthwhile purposes, human agency, fair sharing of benefits and burdens, planetary limits, wider system effects, and accountability and adaptation.
These essays form one part of the Learning for Sustainability resources on AI and professional practice. The accompanying AI for professional practice, research and collaboration hub brings together practical guidance, organisational resources and curated links alongside the essay collection.
[* Image by Jintana / Adobe Stock]