Learn how to apply generative AI to research — to build better prompts, synthesize data, and simulate user insights.

What you'll learn

Learn to apply AI in research using assistive, autonomous, and collaborative approaches.

Learn to create effective prompts for solving research tasks using CRISPR and meta-prompting.

Learn to use AI tools for analyzing interviews, literature, and both qualitative and quantitative data.

Learn to work with synthetic respondents and understand the limitations of AI-generated insights.

Learn to turn research findings into visual presentations and product prototypes with tools like Gamma and Uizard.

Learn to automate research workflows using AI APIs and compare tool performance across tasks.

Requirements

No special background is required — the course is suitable for both beginners and more experienced learners.

Access to Claude, ChatGPT, or other generative AI tools is recommended for hands-on practice.

Description

Looking to speed up your UX or product research process using AI? his course provides a practical, research-focused approach to using generative AI tools such as ChatGPT, Claude, Gamma, and others to enhance efficiency, reduce manual workload, and support deeper, more structured insights.Designed for researchers, designers, analysts, and product managers, this course guides you through applying AI across the research cycle — from planning and data collection to analysis and presentation. You will learn how to build effective prompts, analyze qualitative and quantitative data, simulate synthetic respondents for early testing, and translate research findings into clear, actionable outputs such as reports, presentations, and prototypes.Throughout the course, you will engage with real-world examples, tool demonstrations, and comparative analyses to better understand the practical role of AI in research. We will also critically examine the limitations, risks, and ethical considerations of using AI in research contexts, fostering a balanced and reflective approach.No prior experience with AI is required. This course is beginner-friendly yet designed for professionals seeking to enhance their research practice with AI-supported methods. By the end, you will have a practical toolkit to help you streamline repetitive tasks, strengthen insight generation, and focus more time and energy on analysis, decision-making, and delivering value to your team or organization.

Overview

Section 1: Introduction to AI for Research and Prompt Engineering

Lecture 1 Introduction to AI for Research

Lecture 2 Key tools for AI in Research

Lecture 3 Demo on Gamma

Lecture 4 Demo on UIzard

Lecture 5 CRISPR prompting

Lecture 6 Meta prompts

Section 2: Using AI for Literature Review and Source Analysis

Lecture 7 Working with Literature: tools

Lecture 8 Working with Literature: structuring insights

Section 3: Quantitative Research with AI: Analysis and Automation

Lecture 9 Quantitative analysis: demo & reliability

Lecture 10 Quantitative analysis: overview

Lecture 11 Quantitative analysis: using API

Section 4: Interview-Based Research: Transcription, Parsing, and Thematic Analysis

Lecture 12 Tools for interviewers: transcription & summarisation + demo

Lecture 13 Tools for interviewers: building background through parsing

Lecture 14 Qualitative interview analysis

Section 5: Synthetic Respondents for Research Prototyping

Lecture 15 Introduction to Synthetic Respondents

Lecture 16 Synthetic Respondents: tools and use of API

Lecture 17 Synthetic Respondents: Respondent Marketplace

Lecture 18 Synthetic Respondents: TraIning your Model

This course is designed for UX researchers, designers, analysts, marketers, and product professionals who want to integrate AI into their research workflows to save time, uncover insights faster, and improve the quality of their outcomes.,It is also a great fit for early-career researchers and educators who want to automate routine tasks and focus more on meaning-making rather than manual operations.,This course is ideal for those who already work with qualitative and/or quantitative data, user interviews, surveys and research synthesis, stakeholder presentations, and research-to-design handoffs.