Overview

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This course explores how Ethical Artificial Intelligence (Ethical-AI) can support, enhance, and responsibly transform qualitative and quantitative research within user research, innovation and strategic foresight contexts. With a focus on ethics, inclusion, and methodological rigour, learners will learn the principles and value of qualitative and quantitative inquiry, critically evaluate AI-enabled research tools and workflows, and understand their opportunities and limitations as the course progresses. The goal of this course is to empower learners to confidently integrate Ethical AI into research and innovation processes in ways that are ethical, inclusive, and methodologically sound, while improving the quality, reliability, and impact of research outcomes.

Syllabus

Introduction to Ethical AI for research and innovation
- Explore what Ethical AI is, and what it can and cannot do for researchers, including common failure modes and how to recognise and manage hallucinations. You'll examine the evolution of Ethical AI for qualitative and quantitative research methods, and how these approaches are now being augmented by AI-enabled tools. You'll also focus on a human-centred research process for designing rigorous strategies to collect and analyse qualitative and quantitative data.
Qualitative research: history, methods, and tools
- Learn about the foundations of qualitative research, including key methods and data analysis approaches. You'll explore how Ethical AI tools integrate into qualitative research workflows. You'll also evaluate the benefits and limitations of Ethical AI-enabled qualitative analysis, including issues of bias, validity, and reliability.
Qualitative research using Ethical AI in practice
- Study cases from the creative industry looking at how Ethical AI shapes qualitative research across Architecture, Engineering, and Experience Design contexts. You'll be taken on practical walkthroughs of qualitative data analysis with Ethical AI, including interview coding, thematic analysis, and data synthesis. You'll consider example prompts and tools (e.g., ChatGPT, Claude, NVivo AI, Otter.ai) for their strengths, limitations, and responsible-use considerations.
Quantitative research: history, tools, and methods
- Investigate the foundations of quantitative research, including key methods and data analysis approaches. Explore how Ethical AI tools integrate into quantitative research workflows. You'll also weigh up the benefits and limitations of Ethical AI-enabled quantitative analysis, including issues of bias, validity, and reliability.
Quantitative research using Ethical AI in practice
- Examine case studies from the creative industry that look at how Ethical AI supports quantitative research across across Architecture, Engineering, and Experience Design contexts. Take practical walkthroughs of quantitative data analysis with Ethical AI, including data cleaning, descriptive and inferential statistics, with highlights of risks such as overfitting, hallucinated outputs, and misinterpreted correlations. You'll also look at example prompts and tools (e.g., ChatGPT, Claude, SPSS with AI plugins, Excel Copilot, Python notebooks with AI assistants) in terms of their strengths, limitations, and responsible-use considerations.