AI for Research: Prompt, Analyze, Prototype

Name: AI for Research: Prompt, Analyze, Prototype
Rating: 4.3 (10 reviews)

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

Created byNata Kumpan, Svetlana, Vladimir Portas

Last updated 5/2025

English

What you'll learn

Use AI to speed up every step of your UX or product research process.
Write powerful prompts that get better answers, faster — even for complex tasks.
Analyze interviews, surveys, and data using tools like ChatGPT and Claude.
Test early ideas with AI-generated user responses and know when to trust the results.
Turn your research insights into clear slides, reports, or product mockups.
Automate repetitive tasks and compare top AI tools to find what works best.

Course content

5 sections • 18 lectures • 1h 55m total length

Introduction to AI for Research5:18
This lecture provides an essential overview of the role of artificial intelligence in contemporary research. It highlights three principal ways AI is integrated into research practices: as an assistive tool to support repetitive tasks, as an autonomous system capable of processing large-scale data independently, and as a collaborative partner that enhances critical thinking and creativity. Emphasis is placed on how AI accelerates research processes without replacing human intellectual engagement.
* For the visuals please find slides 1-8 in the presentation attached.
Key tools for AI in Research4:09
This lecture extends the introductory content by presenting practical applications of AI in various research tasks. It examines the role of AI in formulating research questions, summarizing literature, analyzing qualitative data, and supporting data visualization and reporting. Demonstrations illustrate the integration of AI tools across different stages of research workflows, emphasizing AI’s function as an embedded assistant rather than a replacement for human reasoning.
* For the visuals please find slides 9-12 in the presentation attached.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni
Demo on Gamma5:13
This lecture introduces Gamma as a tool for transforming research materials into structured, visually engaging presentations and reports. The session demonstrates how Gamma can be used to compile research summaries, organize key insights, and communicate findings in a clear, accessible format. Emphasis is placed on the role of AI in automating presentation design and enhancing the visual communication of complex information.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni
Demo on UIzard5:27
This lecture explores Uizard as a platform for creating rapid prototypes based on research insights. The session showcases how Uizard can translate research findings, sketches, or conceptual ideas into interactive UI prototypes, enabling early-stage visualization and testing of design solutions. The lecture highlights the integration of AI in streamlining the prototyping process and supporting iterative design informed by research.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni
CRISPR prompting6:17
This lecture focuses on the application of the CRISPR framework for prompt engineering in AI-driven research tasks. The framework—comprising Context, Role, Instruction, Style, Purpose, and Result—provides a structured method for creating precise and effective prompts. The content emphasizes the impact of structured prompting on improving AI-generated outputs and reducing ambiguity in responses, illustrated through comparative demonstrations.
* For the visuals please find slides 1-10 in the presentation attached.
Meta prompts9:10
This lecture introduces meta-prompting as an advanced prompting strategy that supports the creation of other prompts. The content explores how meta-prompts can facilitate the development of reusable prompt libraries, adapt prompts to specific research contexts, and support iterative refinement of prompt design. The lecture includes examples and demonstrations showing how meta-prompting improves the effectiveness and adaptability of AI-assisted research processes.
* For the visuals please find slides 11-15 in the presentation attached.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni

Working with Literature: tools6:52
This lecture introduces AI-based tools designed to enhance the literature review process by automating search, filtering, and initial synthesis tasks. The lecture focuses on addressing challenges such as information overload, difficulty in identifying relevant sources, and inefficiencies in traditional literature scanning. Participants are introduced to a set of AI tools that serve as a workflow “toolchain,” each supporting a distinct step in locating and evaluating research materials.
* For the visuals please find slides 1-8 in the presentation attached.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni
Working with Literature: structuring insights6:10
This lecture focuses on transforming literature findings into structured insights by using AI to synthesize, compare, and map academic sources. Key emphasis is placed on summarizing known knowledge, identifying research gaps, and uncovering tensions or disagreements between studies. AI tools are positioned as accelerators in organizing scattered information into coherent narratives while maintaining the need for critical evaluation by the researcher.
* For the visuals please find slides 9-12 in the presentation attached.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni

Quantitative analysis: demo & reliability5:17
This lecture focuses on demonstrating AI’s capabilities in processing quantitative data, including summarizing tables, classifying responses, comparing groups, and detecting errors or anomalies. The lecture also addresses evaluating the reliability and accuracy of AI-generated outputs and emphasizes critical interpretation and validation of findings.
* For the visuals please find slides 10-13 in the presentation attached.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni
Quantitative analysis: overview6:16
This lecture provides an overview of how artificial intelligence can support quantitative research by automating data processing, summarizing large datasets, and identifying patterns. Emphasis is placed on AI’s role in enhancing rather than replacing traditional statistical methods, enabling faster initial analysis while maintaining the need for researcher oversight and interpretation.
* For the visuals please find slides 1-9 in the presentation attached.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni
Quantitative analysis: using API7:00
This lecture introduces API-based integration of AI into quantitative research workflows. Participants learn how to send structured prompts to AI models via API calls, process large datasets programmatically, and automate repetitive analysis tasks. Key considerations such as data privacy, cost management, output consistency, and rate limits are also addressed to support responsible API use.
* For the visuals please find slides 14-17 in the presentation attached.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni

Tools for interviewers: building background through parsing4:47
This lecture focuses on techniques and tools for gathering and parsing background information prior to conducting interviews. Emphasis is placed on approaches for extracting relevant narratives, identifying themes, and framing research contexts from existing online materials such as reports, news articles, and forum discussions. Automated parsing tools and manual strategies are presented as complementary methods for preparing informed and targeted interview guides.
* For the visuals please find slides 7,8 in the presentation attached.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni
Tools for interviewers: transcription & summarisation + demo9:54
This lecture introduces digital tools and platforms designed to support transcription and summarization tasks in interview-based research. The content outlines how AI-powered transcription services can convert audio recordings into text, enhance the efficiency of note-taking, and generate initial summaries to assist in early analysis. Key features such as accuracy, export options, integration capabilities, and language support are reviewed across multiple tools.
* For the visuals please find slides 1-6 in the presentation attached.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni
Qualitative interview analysis6:33
This lecture explores strategies for analyzing qualitative interview data with the support of AI tools. Core topics include the use of AI-assisted coding, thematic categorization, and clustering of insights from transcribed interviews. The lecture addresses both the opportunities and limitations of integrating AI into qualitative analysis workflows, highlighting the role of human interpretation in contextualizing and validating AI-generated outputs.
* For the visuals please check the presentation attached.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni

Introduction to Synthetic Respondents4:39
This lecture introduces the concept of synthetic respondents, presenting how AI-generated personas can be used to simulate user feedback in research. It outlines key applications such as usability testing without recruitment, scenario simulation, and early validation of design assumptions. The lecture provides an overview of benefits, limitations, and the potential role of synthetic respondents in supplementing traditional research methods.
*For the visuals please find slides 1-7 in the presentation attached.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni
Synthetic Respondents: tools and use of API6:19
This lecture focuses on the tools and methods used to generate synthetic respondents through AI APIs. It explains how prompts can be designed to represent diverse user profiles, how API requests are structured, and how responses can be collected for analysis. The lecture provides practical examples of generating varied personas and discusses factors to consider when using API-based approaches in research.
*For the visuals please find slides 8-16 in the presentation attached.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni
Synthetic Respondents: Respondent Marketplace7:30
This lecture examines the concept of respondent marketplaces, platforms that provide access to AI-generated personas modeled on aggregated behavioral data. It describes how these marketplaces operate, what types of simulated users they can represent, and how they can be applied in research for rapid testing and early feedback. Differences between marketplace-generated and custom-generated synthetic respondents are highlighted.

*For the visuals please find slides 16-22 in the presentation attached.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni
Synthetic Respondents: TraIning your Model8:32
This lecture explores the process of fine-tuning AI models to create synthetic respondents tailored to specific domains or research needs. It outlines the steps involved in preparing training data, configuring the training environment, and evaluating the results of a fine-tuned model. The lecture discusses when fine-tuning may be necessary, how it compares to prompt-based methods, and what resources are required to implement it effectively.
*For the visuals please find slides 23-31 in the presentation attached.
We also have a Telegram channel, which is our hub with all the useful information, details to connect directly with instructors, and links to our other services. You can check it out for more insights and direct communication here: https://t.me/+7uUjaU49fpNiY2Ni

Requirements

No experience with AI or research tools needed — perfect for beginners and pros alike.
Access to ChatGPT, Claude, or similar AI tools is helpful for hands-on practice.
Just bring curiosity and a willingness to try new workflows and tools.
A laptop and internet connection — that’s all you need to get started.

Description

Boost Your Research with AI – Practical Skills for UX and Product Teams

Looking to speed up your research process without losing depth or quality? This course gives you a hands-on, practical approach to using AI tools like ChatGPT, Claude, and Gamma to enhance every stage of UX and product research.

Designed for researchers, designers, product managers, and analysts, it covers how to use AI for planning, data collection, synthesis, and storytelling. You'll learn to write effective prompts, analyze qualitative and quantitative data, simulate user responses, and turn insights into reports, presentations, and prototypes — with far less manual effort.

Through real-world examples and live tool walkthroughs, you'll see exactly how to apply AI in your day-to-day workflow. You’ll also get a critical look at the risks and ethical questions around AI, helping you make informed and responsible choices.

No technical experience required — just curiosity and a desire to work smarter. By the end, you’ll have a toolkit that saves time, boosts insight quality, and helps you focus on strategy and impact instead of repetitive tasks.

If you're ready to modernize your research practice and get more value out of your time, this course will show you how — clearly, quickly, and with real results.

Who this course is for:

This course is for UX researchers, designers, product managers, and analysts who want to use AI to work smarter, not harder.
It’s perfect for anyone who handles user interviews, surveys, data analysis, or research presentations.
If you’re tired of spending hours on manual tasks and want to focus more on insights and impact — this is for you.
Beginner or experienced, if you're curious about using AI to speed up research and make better decisions, you’ll get real value here.

AI for Research: Prompt, Analyze, Prototype

What you'll learn

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Course content

Introduction to AI for Research and Prompt Engineering6 lectures • 36min

Using AI for Literature Review and Source Analysis2 lectures • 13min

Quantitative Research with AI: Analysis and Automation3 lectures • 19min

Interview-Based Research: Transcription, Parsing, and Thematic Analysis3 lectures • 21min

Synthetic Respondents for Research Prototyping4 lectures • 27min

Requirements

Description

Who this course is for: