UX Reseach with AI on Discovery Phase

Course Objective

Teach UX researchers, product designers, and product managers how to use artificial intelligence to radically accelerate and deepen the Product Discovery phase. This course demonstrates how to use AI to automate routine data analysis, uncover hidden insights in gigabytes of interview data, and test hypotheses without overspending the budget.

This course is designed for

UX/UX Researchers (Mid-level/Senior) who want to scale their research and process qualitative data five times faster.
Product Designers who independently lead the Discovery phase on projects.
Product Managers & Business Analysts who aim to validate product hypotheses with minimal budget and time-to-market (TTM).

Learning Format

Live online lectures
Practical case studies, workshops, and interactive sessions
Support for participants during the course via a private chat

What Will Graduates Gain?

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The problem the course solves

Research “just for show”: Due to tight deadlines, the Discovery phase is often cut short, and the product is built on assumptions. AI allows you to conduct in-depth foundational research in a matter of days instead of weeks.
“Drowning” in qualitative data: Transcribing and analyzing hours of interviews (interview transcripts) takes a lot of effort. The course teaches you to delegate this routine to AI, which extracts key pain points and insights from the text in minutes.
Lack of budget for user recruitment: It’s difficult to find respondents in the early stages. We’ll teach you how to use “synthetic users” (AI Personas) for initial testing of ideas and questions before reaching out to a real audience.

Program

Session 1. AI as a Research Assistant: Methodology and Ethics

The evolution of Product Discovery: how AI is transforming data-driven research.
Overview of AI tools for researchers (Claude, ChatGPT, Consensus, Perplexity).
Ethical frameworks and data security: how to work with user data without violating GDPR or NDA requirements.

Session 2. Automating Desk Research and Market Analysis

Rapid collection and analysis of industry trends using AI.
Competitive analysis: automatically identifying competitors’ strengths and weaknesses based on public data and customer reviews.
Prompting techniques for structuring fragmented and unorganized market information.

Session 3. Research Design and Hypothesis Generation

Creating a Research Plan with AI support (goals, metrics, and methodology selection).
Formulating and prioritizing product hypotheses using ICE/RICE frameworks enhanced by LLMs.
Generating discussion guides for customer interviews (CustDev) and surveys.

Session 4. Working with Synthetic Users (AI Personas)

Building highly detailed personas based on real market reports and research data.
Conducting simulated interviews with AI personas to validate edge cases and extreme scenarios.
Understanding the limitations of synthetic data: where AI helps and where it may mislead through hallucinations.

Session 5. Automating Qualitative Research Analysis

Fast transcription and coding of audio and video interviews using AI.
Automatic tagging and pattern detection (Thematic Analysis) in Claude and ChatGPT.
Creating Empathy Maps based on real user quotes and feedback.

Session 6. Quantitative Data Analysis with AI — No SQL Required

Analyzing large datasets (survey results, product analytics) using ChatGPT Advanced Data Analysis.
Visualizing user behavior and identifying anomalies.
Turning raw numbers into actionable business insights.

Session 7. Synthesizing Findings: CJM and Jobs-to-be-Done

Building automated Customer Journey Maps (CJMs) from interview analysis.
Identifying key Jobs-to-be-Done (JTBD) and core product usage scenarios.
Creating User Story Maps with AI-powered tools (e.g., Miro AI, Relume).

Session 8. Delivering Insights to Stakeholders

How to package research findings so they get read: from long-form reports to AI-powered dashboards.
Creating compelling reports and presentations with AI.
Final presentation of Discovery case studies by participants, followed by feedback and discussion.

FAQ

What major research problems does this course address?

The course helps to avoid “tick-box” research due to tight deadlines, allowing for in-depth analysis in a matter of days with the help of AI. It also solves the problem of "drowning" in qualitative data by automating the transcription of hours of interviews, and teaches how to test ideas on "synthetic users" (AI Personas) without recruiting costs.

Who is the program designed for and what tools are studied?

The program is designed for UX researchers, product designers, product managers and business analysts. You will master the tools Claude, ChatGPT, Consensus and Perplexity. In practice, you will learn how to automate Desk Research, build CJMs, generate hypotheses, and analyze quantitative data without knowledge of SQL.

In what format does the training take place and how long does it take?

The course lasts 4 weeks and consists of 8 intensive classes. Training takes place in the format of live online lectures with a maximum focus on practice: students analyze practical cases, participate in workshops and interactive sessions. Throughout the training, participants receive support in a closed chat.

What do I get in the end and how do I join the group?

In the final session, you will learn how to package insights into compelling reports and AI dashboards, followed by the defense of your Discovery cases. To accelerate the Product Discovery phase and scale your research, simply click the "Apply" button on this page and fill out the short form.