AI can support inquiry, preparation and writing, but people still need to shape the questions, interpret what is produced and remain responsible for how it is used.
Generative AI tools are increasingly being used in research, evaluation, facilitation and other forms of professional practice. They can help people explore an issue, test alternative framings, prepare questions, organise material and develop drafts. For individuals and small teams, they may also make forms of analysis or preparation possible that available time and resources would otherwise place beyond reach.
A useful output, however, does not show by itself that the wider work has improved. AI can influence how a problem is framed, which perspectives remain visible, where interpretation occurs and how much scrutiny a polished result receives. In participatory and applied settings, these changes matter because relationships, power, lived experience and collective sense-making are part of the work, not simply context around it.
Using AI well therefore involves more than learning how to prompt a tool. It requires attention to purpose, source material, confidentiality, interpretation, disclosure and responsibility. It also means deciding which parts of the work AI may support, where human or collective judgement remains essential, and how experience will be reviewed over time.
This page brings together selected resources on AI-supported inquiry, qualitative research, writing, evaluation and ethical practice. The emphasis is not on adopting AI for its own sake, but on using it where it contributes to better work while keeping inquiry, judgement and responsibility visible.
AI as a support for thinking and practice
AI as a thought partner: reflections from practice
In this post, Will Allen reflects on how AI tools like ChatGPT can support participatory, systems-focused research and practice. Rather than centring the technology, he emphasises its role as a background aid—helping surface assumptions, test framings, and support reflection. At heart, the post argues that what matters is not the tool itself, but how we use it – and who we use it with – in the real work of collaboration, learning, and systems change.
AI prompts for shared thinking: a light framework for purposeful prompting
This post introduces a simple framework for using AI to support preparation, reflection and shared thinking. It keeps purpose, context, relationships and responsibility in view rather than treating prompting as a purely technical skill.
AI in qualitative and participatory research
When respondents use AI: what qualitative researchers need to know
This 2025 MERL Tech event recap by Isabelle Amazon-Brown explores a fast-emerging challenge: research participants using generative AI to complete surveys and group discussions. Drawing on field experience in Africa and practitioner reflections, it highlights how AI responses can blur authenticity, raise new ethical dilemmas, and complicate data quality. For practitioners, the piece offers insight into why respondents might use AI — from language barriers to speed — and outlines strategies to adapt qualitative research practice in response.
Beyond binary positions: Making space for critical and reflexive GenAI integration in qualitative research
This paper by Susanne Friese and colleagues responds to calls to exclude generative AI from reflexive qualitative research. Rather than taking a pro or anti position, it argues for a more considered middle ground, where AI is used critically, transparently, and under researcher control. The authors suggest that reflexive qualitative practice is not inherently at odds with these tools, and that careful, researcher-led use can support rather than replace interpretation. It is a useful contribution for those exploring how to engage with emerging technologies while maintaining reflexivity, ethical responsibility, and methodological integrity.
Research writing, integrity and disclosure
Can academics use AI to write journal papers? What the guidelines say
In this 2024 article for The Conversation, Hannah Forsyth unpacks how academic journals are responding to AI use in writing. She outlines emerging policies, disclosure requirements, and ethical concerns providing a useful orientation for researchers navigating this evolving space.
Using AI tools ethically and responsibly – SciSpace
This short 2024 guide by Anu Sridharan offers practical advice on responsible AI use in academic research and writing. It outlines common concerns – like plagiarism, bias, and transparency – and suggests concrete steps to stay within ethical boundaries. A helpful primer for students and researchers navigating emerging norms around AI-assisted scholarship.
AI in evaluation and organisational learning
AIDA – Using AI to surface insights from evaluation reports
AIDA is a practical example of generative AI applied in real-world MEL settings. Developed by UNDP, this chatbot draws from almost 7,000 evaluation reports to answer questions and cite specific evidence. While the quality of underlying evaluations varies, AIDA offers a glimpse of how AI can support evidence access, reflection, and learning in large organisations. For those working in complexity-aware, systems-informed practice, it’s a tool worth exploring—with curiosity and critical awareness.
Guidelines for the Best-Practice Use of Generative AI in Research
A concise guide for researchers (with a focus on New Zealand) from the Royal Society Te Apārangi. It emphasises ethical integrity, data sovereignty (especially for Indigenous data), and human accountability. Outlines how to use AI transparently, avoid naming AI as a co-author, and maintain responsibility for research outputs. Use as a checklist when planning, conducting, or publishing research involving AI tools.
MERL Tech NLP Community of Practice
An open community exploring how natural language processing (NLP) tools can support monitoring, evaluation, research, and learning. Offers regular sessions and curated resources on ethics, use cases, and practical applications of AI in development contexts.
Wider ethical questions
Navigating ethical challenges in generative AI‑enhanced research
This ArXiv pre-print by Douglas Eacersall and colleagues (2024) introduces a practical seven‑principle ETHICAL framework for responsibly using GenAI in research. It moves from abstract ethical concerns to concrete steps like policy review, social impact analysis, output validation, and transparency.
Mapping the ethics of Generative AI: A comprehensive scoping review
A rigorous scoping review mapping 378 ethical issues associated with generative AI across 19 topic areas. This study by Thilo Hagendorf offers a comprehensive overview for scholars, practitioners, or policymakers, condensing the ethical debates surrounding fairness, safety, harmful content, hallucinations, privacy, interaction risks, security, alignment, societal impacts, and others. Exposes fragmentation across disciplines and highlights gaps in applied or participatory ethical interventions.
For a wider framing of ethical questions across structural, organisational and practice levels, see Seeing the wider ethical picture around AI development and use and AI in context: the wider picture.
This page forms part of the Learning for Sustainability resources on AI and professional practice. Explore the AI for professional practice, research and collaboration hub for related essays, organisational guidance and curated resources.
[* Image by Jintana / Adobe Stock]
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