Research fundamentals
Frame useful research questions, choose an appropriate method and create a plan that connects learning to decisions.
UX research and responsible AI training
Practical, facilitated learning for teams that need to conduct better research, make digital services easier to use or adopt and evaluate AI more responsibly.
Generic training is easy to forget. Narrata’s workshops are designed around the decisions, tools and constraints participants face in their own work.
Teams learn by practising: framing a research question, improving an interview guide, evaluating a digital journey, testing AI outputs or working through a responsible-use scenario. The aim is not to turn everyone into a specialist. It is to give people enough understanding, structure and confidence to make better choices.
Sessions can stand alone or form part of a capability-building programme that combines training, templates, coaching and applied project work.
Build a shared foundation for listening to users, making sense of evidence and communicating what the team should do next.
Frame useful research questions, choose an appropriate method and create a plan that connects learning to decisions.
Ask neutral questions, listen for meaning, probe effectively and create the conditions for a useful conversation.
Move from notes to credible themes, keep claims connected to evidence and communicate findings clearly.
Recognise common usability problems, run a focused usability test and understand core accessibility considerations.
Write clearer questions, choose suitable response options and recognise common sources of bias.
Turn findings into decision-ready stories and facilitate stakeholders from evidence to action.
Understand where AI may help, where it can fail and how to evaluate an AI-enabled workflow with more discipline.
A practical view of capabilities, limitations, risk and value so leaders can ask better questions before approving an initiative.
How generative AI works at a useful level, clearer prompting, safe handling of information and critical review of outputs.
Define realistic tasks and criteria, build test sets, examine failure modes and use scorecards to make quality visible.
Evaluate how people understand and use AI search, summaries, recommendations or automated replies—and where trust can break down.
Work through consent, retention, human review and data-handling questions in the context of real use cases.
Create shared prompt patterns, review routines and practical ownership so useful practices do not remain with one person.
Format, depth and technical detail are adjusted to the participants and the outcome you need.
No. Our workshops focus on practical decisions and everyday tasks. Technical depth is adjusted to the roles and experience of the group.
Yes. We can use your products, research questions, AI use cases, policies or evaluation challenges so participants practise with relevant examples.
Yes. For teams building or adopting AI-enabled services, we can combine user-centred research, AI interaction review and practical evaluation in one programme.
Tell us the capability you want to build, who is in the room and where the learning needs to show up in everyday work.