Ai For Research: Prompt, Analyze, Prototype
Published 5/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 884.36 MB | Duration: 1h 55m
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.
