FOR STUDENTS AND RESEARCHERS
Collaborate with EdgeLab
Have a research question about learning, work, AI or change? EdgeLab can help you explore it through autopoietic ecology (AE): an approach that asks what makes an activity possible and how it changes what people can do next.
We can work with you to clarify your question, understand and apply AE, connect ideas with evidence, and explore how AI could support the inquiry. The AE Engine can help you prepare a first approach and contribute to an agreed collaboration.
How we can help and what you could gain · An example · Explore our research
How we can help—and what you could gain
Understand and apply AE
We can explain the ideas through your own example, discuss relevant concepts and compare them with other approaches.
The aim: an explanation you can use, and a clearer judgement about whether AE helps with your question.
Turn an interest into an inquiry
We can help narrow a topic and map the people, activities, resources and decisions involved. This can bring overlooked work, constraints and perspectives into the discussion.
The aim: a focused question and a clearer account of what you need to investigate.
Connect theory with evidence
We can discuss suitable methods, what would support or challenge an explanation, and how to distinguish an interesting idea from a finding.
The aim: a workable study or analysis plan, with its assumptions and limits made clear.
Use AI to support shared thinking
We can help develop prompts, compare interpretations and examine how AI suggestions enter research or learning. People check sources, revise claims and agree decisions.
The aim: purposeful AI use that supports participation, critical thinking and a record of how the work developed.
These are purposes for a collaboration, not established performance gains from using AE or the engine. We would review what the approach adds in your setting.
What could we work on?
- A student research project: discuss a question, proposal or case connected with your studies and our research.
- A research partnership: explore a conceptual argument, comparative study, collaborative inquiry or piece of writing.
- AE in a practical setting: investigate learning, participation, decisions or recurring problems in an organisation or community.
- AI and AE Engine evaluation: develop a small test of how the tool supports understanding, questioning and correction.
Possible formats include a focused discussion, workshop, proposal feedback or a jointly scoped project. What fits depends on the question and available capacity. Explore our research interests and published and developing work.
Four steps towards a collaboration
1. Bring your question
Describe something you want to understand or change. You do not need to arrive with a finished proposal or expert knowledge of AE.
Find a useful starting point
Tell us your setting, what prompted the question and why it matters to you. Identify a connection with EdgeLab’s work, or explain what you would like help exploring.
2. Prepare with the AE Engine
If useful, use the engine to explore your question and draft a short brief. Review it in your own words before sharing it.
Copy a prompt to prepare your approach
“I would like to explore a collaboration with EdgeLab. My question is and my setting is [setting]. Ask me what I want to understand and what I can contribute. Using the EdgeLab material I provide, suggest possible connections, assumptions to examine and a manageable first inquiry. Draft a short brief separating my ideas from your suggestions. Do not assume that EdgeLab has agreed to the work.”
3. Send a short brief
Email Steven Watson with a few paragraphs about your idea and the kind of collaboration you have in mind.
What to include
- Your question and why it matters.
- Your study, research or practical setting.
- The connection you see with EdgeLab’s work.
- What you could contribute and what help you are seeking.
- Any important timescale or project requirements.
- How AI helped prepare the brief, if you used it.
4. Agree a suitable next step
An initial discussion can explore the fit and what is feasible. Any ongoing work needs an agreed purpose, scope, contributions and responsibilities.
What we would clarify together
This could include the research question, methods, expected outputs, meeting arrangements, credit, data access and how progress will be reviewed. Student work should fit its existing university and assessment arrangements. An enquiry does not itself establish a supervision arrangement or project place.
The engine opens in ChatGPT and requires sign-in. Using it does not send a proposal to EdgeLab. Email the material you choose to share; you can also contact us directly without using AI.
An example: turning a concern into research
Illustrative proposal: a student wants to understand why written feedback sometimes goes unused. They use the AE Engine to explore possible explanations. It suggests motivation; the student notices that timing and opportunities to discuss feedback may also matter.
How EdgeLab could assist
The student sends that question to EdgeLab. A discussion could help trace how feedback is understood, when students can act on it, and how it affects the next piece of work. AE gives the inquiry a way to connect experience, teaching routines, tools and decisions.
Together, we could refine the question, consider alternative explanations and identify suitable evidence. A first output might be an inquiry map or a small study plan. If a collaboration is agreed, the participants would establish responsibilities and the appropriate research arrangements.
The potential benefit is a more precise inquiry that takes the student’s experience seriously and connects theory to something that can be investigated. This is a proposed example, not a report of a completed EdgeLab study.
How AI could help while we work together
AI can help prepare a meeting, explain an unfamiliar idea, compare supplied texts or identify questions for discussion. Students and researchers then examine the response together and decide what to use, change or reject.
In meetings and between meetings
In a meeting, one person can operate the engine while everyone comments on the response. Between meetings, participants can use separate sessions and bring checked extracts and unresolved questions to the group. Make room for people who cannot access AI or prefer not to use it.
Keep a record that people can correct
A simple shared research record
- The question and each person’s contribution.
- Sources checked, with links or page references.
- AI suggestions used, changed or rejected.
- What remains uncertain or disputed.
- Decisions, corrections and which documents need updating.
- The next action, who will take it and when to review it.
Agree how AI fits the project
Agree permitted uses of AI, how people’s contributions are credited and how AI assistance is acknowledged. Check original sources. Use public, fictional or appropriately approved material when exploring an idea, and project-approved arrangements for confidential data. Review whether AI is helping understanding and participation, and how much checking work it creates.
Research behind the approach
This guide proposes a way to develop and evaluate collaboration. It does not report a tested AE Engine intervention.
Read the research and guidance
Watson, Morgner and Brezovec (2026). Their critical review examines how AI shapes the recognition and use of learners’ contributions, including questions of inclusion, verification and correction. Read the article in AI & Society.
Miao and Holmes (2023), UNESCO. Guidance for generative AI in education and research sets out an approach centred on people, meaningful use and data protection. Read the guidance.
Bring us an idea to explore
Email steve.watson84@gmail.com with your question and what you would like to work on together.
Cambridge EdgeLab 