# The Time Machine: Future Scenario Generation Through Generative AI Tools
## 2 Materials and Methods
### Methodology for Generating Future Scenarios  
Six iterative trials were conducted with different participant groups, each focusing on a specific foresight challenge (Table 1). The project team classified these trials into two blocks.  
Refinement trials (Trials 1 to 3) are intended to optimise the tools and methods used for scenario generation. These trials focused on transitioning from prototype v.0 to prototype v.2 through incremental adjustments to key elements, including the prompt wording, user interface, and card deck design. Participants engaged in hypothesis creation by combining two or more trends relevant to their chosen domains--such as health, education, resource governance, or inequality. These hypotheses could be either generic (v.0) or geographically oriented (v.1 and onwards), providing a foundation for scenario generation and iterative tool refinement.  
The validation trials (Trials 4 to 6) aimed to assess the final prototype (v.2) while exploring various strategies for hypothesis formation. During these trials, participants worked with three distinct types of hypotheses. The first type, generic-normative, consisted of broad or unstructured statements such as those from the previous trials. The second type, situational hypotheses, were designed to address specific problems or stakeholder groups, outlining the future context in greater detail. Lastly, interventional hypotheses introduce a policy, product, or service within a specific context to assess its impact on future scenarios. These trials helped validate the prototype's utility across diverse hypothesis frameworks.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Session** & **Work Themes** & **Participants/Groups** & **Profiles** & \begin{tabular}{c} **Hypothesis Creation** \\ **Method** \\ \end{tabular} & \begin{tabular}{c} **Hypothesis** \\ **Type** \\ \end{tabular} & **Scenarios** & \
\begin{tabular}{c} **Prompt** \\ **Version** \\ \end{tabular} \\ \hline \multirow{2}{*}{1} & The future of health and education services & \multirow{2}{*}{25/5} & \multirow{2}{*}{Academics} & Based on the combination of two trends & Generic hypothesis & \multirow{2}{*}{32} & v.0 \\ \cline{1-1} \cline{6-6}  & & & & two trends & & & \\ \hline \multirow{2}{*}{2} & The future of resource governance & \multirow{2}{*}{15/3} & \multirow{2}{*}{
\begin{tabular}{c} Environmentalists \\ and politicians \\ \end{tabular} } & Based on the combination of two trends & Generic and & \multirow{2}{*}{42} & \multirow{2}{*}{v.1} \\ \cline{1-1} \cline{6-6}  & & & & two trends & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the trials, including the themes explored, participant profiles, methods of hypothesis creation, types of hypotheses, the number of scenarios generated, and the version of the Time Machine (MdT) prompt used.  
The Iterative Approach to Hypothesis Formulation  
In keeping with the abductive and iterative spirit of the research-through-design approach, the method for formulating hypotheses was intentionally altered across workshops. This progression allowed the research team to test the MdT under varying conditions and observe how changes in hypothesis formulation--from simplistic trend-pairing to designing an intervention--impacted the quality and relevance of the AI-generated scenarios, knowing that prompt structures impact the quality of GAI outcomes [44]. The shifting methods thus served as methodological probes, revealing new insights into how structured vs. unstructured hypotheses affect AI outputs and how participants deem these differences in group-based foresight exercises.