Ethical AI for Research and Innovation

Aims of the course

This course aims to equip learners with the knowledge and skills to critically evaluate, design, and apply AI-enabled qualitative and quantitative research methods in user experience and strategic foresight contexts, understanding when and how AI should complement traditional research approaches.

It also aims to empower participants to integrate AI responsibly and ethically into real-world research workflows by designing inclusive, transparent, and methodologically rigorous processes that address fairness, explainability, bias, and overall research integrity.

Course content overview

This course explores how Artificial Intelligence (AI) can support, enhance, and ethically transform qualitative and quantitative research within user experience and strategic foresight contexts.

With a focus on inclusion, ethics, and methodological rigour, learners will explore the principles and value of qualitative and quantitative inquiry, critically evaluate AI-based research tools, and understand their opportunities and limitations as the course progresses.

The goal of this course is to empower learners to confidently integrate AI into user research in ways that are ethical, inclusive, and methodologically sound, while improving the quality, reliability, and impact of research outcomes.

Target audience

No prior coding or advanced technical skills are required to attend this course. 

This course may be of interest to: Project managers and product leads seeking to use AI-supported research insights to inform strategic planning, prioritisation, and evidence-based decision-making; User experience researchers, service designers, and product designers interested in applying AI ethically to qualitative and quantitative research, including interviews, surveys, diary studies, and usability evaluations; Architects, urban designers, and engineers working in physical, digital, and hybrid environments who want to incorporate human-centred AI into design research, performance evaluation, and post-occupancy studies; Data analysts and applied researchers in social sciences, humanities, and interdisciplinary fields who want to augment traditional analysis methods with AI; Professionals interested in the ethical dimensions of AI, including issues of bias, inclusion, explainability, and trust, especially in user research, data analysis, strategic insights, and long-term foresight; Practitioners looking to expand their use of AI-based tools for both qualitative and quantitative data analysis, strategic insights, and long-term foresight; Early-career researchers seeking practical guidance on integrating AI into research workflows in a responsible and academically sound way.

Welcome week: Week 0

Purpose:

• personal introductions
• introducing the course
• useful reading
• personal objectives

Learning outcomes:

By studying this week, the students should have:

• become familiar with navigating around the Virtual Learning Environment (VLE) and from VLE to links and back
• tested their ability to access files and the web conferencing software and sort out any problems with the help of the Technology Enhanced Learning team
• learned how to look for, assess and reference internet resources
• contributed to a discussion forum to introduce themselves to other students and discuss why they're interested in the course and what they hope to get out of their studies 

Teaching Week 1: Introduction to Ethical AI  for User Experience Research

Learning outcomes:

In this teaching week, we will explore:

• what AI is, and what it can and cannot do, for researchers, including common failure modes and how to recognise and manage hallucinations
• the evolution of AI in qualitative and quantitative research methods, and how these approaches are now being augmented by AI-based tools
• a user-centred research process for designing rigorous strategies to collect and analyse qualitative and quantitative data

Teaching Week 2: Qualitative Research: History, Tools, and Methods

Learning outcomes:

In this teaching week we will explore:

• foundations of qualitative research, including key methods and thematic analysis strategies used in user research
• how AI tools integrate into qualitative research workflows
• benefits and limitations of AI-assisted qualitative analysis, including issues of bias, validity, and reliability

Teaching Week 3 Ethical Qualitative Workflows Using AI: Examples and Practice

Learning outcomes:

In this teaching week we will exlore:

• case studies from architecture and design: how ethical AI shapes qualitative research across built-environment, UX, and organisational research contexts
• practical walkthroughs of qualitative data analysis with AI, including interview coding, thematic analysis, and synthesis
• example prompts and tools (e.g., ChatGPT, Claude, NVivo AI, Otter.ai): strengths, limitations, and responsible-use considerations

Teaching Week 4: Quantitative Research: History, Tools, and Methods

Learning outcomes:

In this teaching week we will explore:

• foundations of quantitative research, including surveys, sampling, measurement, and key statistical approaches used in user research
• how AI tools integrate into quantitative research workflows
• benefits and limitations of AI-assisted quantitative analysis, including issues of bias, validity, and reliability

Teaching Week 5: Ethical Quantitative Workflows Using AI: Examples and Practice

Learning outcomes:

In this teaching week we will explore:

• case studies from architecture, and design: how AI supports quantitative research across built-environment, UX, and organisational research contexts
• practical walkthroughs of quantitative data analysis with AI, including data cleaning, descriptive and inferential statistics, with highlights of risks such as overfitting, hallucinated outputs, and misinterpreted correlations
• example prompts and tools (e.g., ChatGPT, Claude, SPSS with AI plugins, Excel Copilot, Python notebooks with AI assistants): strengths, limitations, and responsible-use considerations

Week 6 - what next?

• assessment of student learning
• assessment of student satisfaction
• encouragement of further study

This course is open to everyone, and you don’t need any previous knowledge or experience of the subject to attend.  

Our short courses are designed especially for adult learners who want to advance their personal or professional development. They are taught by tutors who are expert in both their subjects and in teaching students of all ages and experiences.  

Please note that all teaching is in English. You should have near-native command of the English language to get the maximum benefit from the course.

Each week of an online course is roughly equivalent to 2-3 hours of classroom time. On top of this, participants should expect to spend roughly 2-3 hours of self-study time, for example, reading materials, although this will vary from person to person.

While they have a specific start and end date and will follow a weekly schedule (for example, week 1 will cover topic A, week 2 will cover topic B), our tutor-led online courses are designed to be flexible and as such would normally not require participants to be online for a specific day of the week or time of the day (although some tutors may try to schedule times where participants can be online together for web seminars, which will be recorded so that those who are unable to be online at certain times are able to access material).

Unless otherwise stated, all course material will be posted on the VLE so that they can be accessed at any time throughout the duration of the course and interaction with your tutor and fellow participants will take place through a variety of different ways which will allow for both synchronous and asynchronous learning (using discussion boards etc).

Fees

The course fee includes access to the course on our VLE, personal feedback on your work from an expert tutor, a certificate of participation (if you complete work and take part in discussions), and access to the class resources for two years after your course finishes.

Concessions  

For more information, please see our concessions information page. 

Alison Fordham Bursary

University of Cambridge Professional and Continuing Education is proud to offer the Alison Fordham bursary, which is awarded to students who wish to study on one of our short online courses via our VLE, reducing the fee paid by 50%. The bursary is limited to a single award for each set of online courses.

Application criteria:

• applicants should set out their personal learning motivations since priority will be given to those who are returning to learning after an extended break, or have not previously engaged with fully online learning, or are seeking to use the online short course as a bridge towards undergraduate award-bearing study
• applicants who can demonstrate financial need  

For more information, please see our bursaries information page.

A certificate of participation and a digital credential will be awarded to those who contribute constructively to weekly discussions, exercises and assignments for the duration of the course.