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Triangulating on Possible Futures: Conducting User Studies on Several Futures Instead of Only One

Antti Salovaara, Leevi Vahvelainen

TL;DR

Futures studies in HCI often rely on single-prototype studies, risking limited plausibility. The paper introduces and experiments with triangulation across two AI-enabled futures (MyData and CorpoCapitalist) using a within-subjects design with two prototypes to observe cross-futures patterns. It contributes a three-type typology of future-oriented triangulation (F1-F3), demonstrates cross-checking and deeper insight across futures, and offers methodological guidance for more future-proof HCI research. The work enhances the robustness and anticipatory value of HCI studies addressing AI-augmented knowledge work.

Abstract

Plausible findings about futures are inherently difficult to obtain as they require critical, well-informed speculations backed with data. HCI scholars tackle this challenge via user studies wherein futuristic prototypes and other props concretise possible futures for participants. By observing participants' actions, researchers then can 'time travel' to see that future as reality, in action. However, such studies may yield particularised findings, inherent to study's intricacies, and lack broader plausibility. This paper suggests that triangulation of possible futures may help researchers disentangle particularities from more generalisable findings. We explored this approach by conducting a study on two alternative futures of AI-augmented knowledge work. Some findings emerged in both futures while others were particular to only one or the other. This approach enabled cross-checking of plausibility and simultaneously afforded deeper insight. The paper discusses how triangulating possible futures renders HCI studies more future-proof and provides means for reflective anticipation of possible futures.

Triangulating on Possible Futures: Conducting User Studies on Several Futures Instead of Only One

TL;DR

Futures studies in HCI often rely on single-prototype studies, risking limited plausibility. The paper introduces and experiments with triangulation across two AI-enabled futures (MyData and CorpoCapitalist) using a within-subjects design with two prototypes to observe cross-futures patterns. It contributes a three-type typology of future-oriented triangulation (F1-F3), demonstrates cross-checking and deeper insight across futures, and offers methodological guidance for more future-proof HCI research. The work enhances the robustness and anticipatory value of HCI studies addressing AI-augmented knowledge work.

Abstract

Plausible findings about futures are inherently difficult to obtain as they require critical, well-informed speculations backed with data. HCI scholars tackle this challenge via user studies wherein futuristic prototypes and other props concretise possible futures for participants. By observing participants' actions, researchers then can 'time travel' to see that future as reality, in action. However, such studies may yield particularised findings, inherent to study's intricacies, and lack broader plausibility. This paper suggests that triangulation of possible futures may help researchers disentangle particularities from more generalisable findings. We explored this approach by conducting a study on two alternative futures of AI-augmented knowledge work. Some findings emerged in both futures while others were particular to only one or the other. This approach enabled cross-checking of plausibility and simultaneously afforded deeper insight. The paper discusses how triangulating possible futures renders HCI studies more future-proof and provides means for reflective anticipation of possible futures.
Paper Structure (25 sections, 6 figures, 1 table)

This paper contains 25 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Triangulation
  4. Triangulation in HCI
  5. Triangulation in HCI's Future-Oriented Methodology Papers
  6. A Study of Triangulation across Possible AI Futures
  7. Research Design
  8. Analysis
  9. The Findings
  10. Interaction Patterns
  11. Experiences
  12. AI as a Knowledge Work Companion.
  13. Performance Tracking Gamified the CorpoCapitalist Prototype.
  14. Model Contributor Role Led to Reflections on Work's Wider Impact.
  15. Peer Comparisons Foregrounded Learning in the MyData Prototype.
  16. ...and 10 more sections

Figures (6)

  • Figure 1: Triangulation of possible futures in our user study.
  • Figure 2: Differences in the main screen between the prototypes.
  • Figure 3: How the prototypes' evaluation confirmation popups differed.
  • Figure 4: The interactions involved in evaluating a news article, in the MyData condition in its UI's pale-colours version.
  • Figure 5: A demonstration of triangulation's value: our findings from triangulation across two possible futures, sorted by the future(s) in which the relevant pattern was observed.
  • ...and 1 more figures