As generative AI tools like ChatGPT and Perplexity become more accessible, researchers and practitioners are exploring how they can support applied research, writing, and reflection. In complex, real-world settings, where collaboration, nuance, and diverse values matter, AI can be a helpful assistant. At the same time its use raises important ethical and methodological questions.
This page shares curated resources and reflections on using AI in practice-based research and other applied settings. It highlights practical ways these tools can support inquiry, sense-making, and writing, while also considering risks, limitations, and the need for human judgment. The materials are especially relevant for those working in participatory, interdisciplinary, and complexity-aware contexts, where relationships, power dynamics, and meaning-making are as important as efficiency or automation.
At the core, the emphasis is not on the tools themselves, but on how we use them, and with whom, to support deeper learning, inclusion, and systems change. Generative AI can contribute to inquiry and reflection, but it is people who frame the questions, interpret the insights, and take responsibility for decisions.
The resources below share perspectives and guidance from across the field, including reflections from my own practice. A final section highlights wider considerations, such as environmental impact and organisational change, to help situate AI within broader systems and responsibilities.
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.
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.
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.
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.
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.
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.
More information on the use of AI can be found through the LfS generative AI landing page. This includes links to the related resource page AI in context: The wider picture alongside the author’s reflective posts – AI as a thought partner: reflections on collaborative practice and systems work and AI prompts for shared thinking: a light framework for purposeful prompting.
[* Image by Jintana / Adobe Stock]
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