| Lifecycle: Planning |
AI is used to support research planning, study design, research questions, or method selection before research begins. |
| Lifecycle: Recruitment |
AI is used to recruit, screen, match, or simulate participants. |
| Lifecycle: Data collection |
AI is used while collecting research data, including interviews, surveys, moderation, or participant interactions. |
| Lifecycle: Analysis |
AI is used to organise, code, analyse, synthesise, or interpret research data. |
| Lifecycle: Reporting |
AI is used to communicate findings through reports, presentations, summaries, or storytelling. |
| Lifecycle: Research operations |
AI supports research management, repositories, documentation, workflows, or knowledge management. |
| AI role: Automation |
AI completes repetitive or manual tasks that previously required researcher effort. |
| AI role: Assistance |
AI supports the researcher in completing a task, but the researcher remains in control. |
| AI role: Augmentation |
AI expands what researchers are capable of doing, enabling new or larger-scale ways of working. |
| AI role: Simulation |
AI simulates users, participants, behaviours, or environments (e.g. synthetic participants, digital twins). |
| Benefits: Efficiency |
AI saves time or reduces manual effort in the research process. |
| Benefits: Scale |
AI enables research with more participants, data, or scenarios than would otherwise be possible. |
| Benefits: Cost reduction |
AI reduces the financial cost of conducting research. |
| Benefits: Democratisation |
AI lowers barriers to conducting research, enabling more people or teams to carry out research activities. |
| Benefits: Accessibility |
AI makes research more accessible or inclusive for participants or researchers with different abilities, languages, or communication needs. |
| Benefits: Better decision making |
AI provides evidence, insights, or feedback that helps improve research or business decisions. |
| Risks: Hallucination |
AI generates incorrect, fabricated, or unsupported information. |
| Risks: Bias |
AI produces biased or unrepresentative outputs due to data or model limitations. |
| Risks: Privacy |
Concerns around protecting participant, organisational, or personal data. |
| Risks: Loss of nuance |
AI oversimplifies or misses context, emotion, or complexity in human experiences. |
| Risks: Over-reliance |
Researchers or organisations rely on AI outputs without sufficient human review or validation. |
| Risks: Trust |
Concerns about whether AI-generated outputs are reliable or trusted by researchers or stakeholders. |
| Skills: AI literacy |
Understanding AI capabilities, limitations, and appropriate use. |
| Skills: Prompting |
Writing effective prompts or instructions for AI systems. |
| Skills: AI evaluation |
Assessing, validating, and critically reviewing AI outputs. |
| Skills: Critical thinking |
Questioning evidence, identifying limitations, and making informed judgements about AI-generated work. |
| Skills: Research judgement |
Deciding what research questions to ask, what evidence matters, and when human judgement is required. |
| Skills: Strategic thinking |
Connecting research, AI capabilities, and business goals to inform decisions. |
| Skills: Storytelling |
Communicating research findings in a compelling, human-centred way. |
| Future concepts: Digital twins |
AI representations designed to model an individual person's characteristics, preferences, or likely responses. |
| Future concepts: Synthetic participants |
AI-generated participants or simulated po |