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arxiv: 2606.10861 · v1 · pith:WLBWG5V6new · submitted 2026-06-09 · 💻 cs.SE · cs.AI· cs.HC

From Perception to Action: Can UI Interventions Foster Sustainable LLM Chatbot

Nitish Patkar , Pooja Rani , Jack Gl\"asser , Simon L\"uscher , Martin Kropp This is my paper

Pith reviewed 2026-06-27 12:28 UTC · model grok-4.3

classification 💻 cs.SE cs.AIcs.HC
keywords LLM chatbotssustainabilityuser interface interventionsenergy awarenessbehavior changepersuasive technologyfield studychoice architecture
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The pith

Sustainability-oriented UI interventions can improve awareness and support more energy-responsible interaction patterns in LLM chatbots.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines whether features added to the chatbot interface, such as mode choices and energy displays, can raise user awareness of energy consumption and prompt shifts toward lower-energy options. A survey of 77 people found high awareness but frequent misestimation of costs and limited willingness to accept slower performance. The authors built a prototype offering Energy-efficient, Balanced, and Performance modes plus real-time estimates and a dashboard, then tested it over five days with 11 users. In that test the efficient mode was chosen for more than half of prompts, and most participants reported switching modes deliberately when full accuracy was not needed. The work positions these interface changes as a complement to model-level efficiency improvements.

Core claim

Sustainability-oriented UI interventions can improve awareness and support more energy-responsible interaction patterns in LLM chatbots. The interventions consist of a three-mode switch, per-response energy feedback, pre-send estimates, a usage metrics dashboard, and energy analogies. In the field study Energy-efficient mode was selected for 55.8 percent of prompts, participants did not shorten their inputs, and 90.9 percent reported choosing the efficient mode when high accuracy was unnecessary.

What carries the argument

the three-mode switch (Energy-efficient, Balanced, Performance) together with per-response energy feedback, pre-send estimates, usage dashboard, and energy analogies, which together allow users to see and act on consumption differences at the moment of interaction.

If this is right

  • Users select the Energy-efficient mode for roughly 56 percent of prompts when accuracy demands are low.
  • Mode switching rather than shorter prompts becomes the main way users reduce energy use.
  • Adding these UI elements does not reduce reported usability.
  • The approach supplies behavioral estimates that can sit alongside backend efficiency improvements.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same mode-and-feedback pattern could be tested in other conversational AI systems where energy cost is a concern.
  • Longer deployments would be needed to check whether the observed mode choices persist beyond the initial five days.
  • Pairing the UI signals with actual server-side energy measurements rather than estimates would tighten the link between choice and impact.

Load-bearing premise

A five-day study with eleven participants produces generalizable evidence of lasting behavioral change and that self-reported mode choices plus prototype energy estimates accurately capture real energy-responsible use without hidden performance costs.

What would settle it

A controlled comparison in which users given the same prototype but without the mode switch, feedback, or dashboard show no increase in selection of lower-energy options, or in which measured task accuracy drops when the efficient mode is chosen.

read the original abstract

LLM-powered chatbots are increasingly embedded in everyday workflows, raising sustainability concerns due to their energy use. Most mitigation strategies emphasize model or infrastructure efficiency, while the user-interface (UI) layer remains underexplored despite its potential to shape interaction behavior. We investigate whether sustainability-oriented UI interventions can increase users' energy awareness and encourage more energy-responsible chatbot use without reducing usability. We first conducted a baseline survey with 77 participants to assess awareness and receptiveness to intervention concepts. Guided by prior work on persuasive technology and choice architecture, we implemented a web-based chatbot prototype with a three-mode switch (Energy-efficient, Balanced, Performance), per-response energy feedback, pre-send energy estimates, a usage metrics dashboard, and energy analogies. We then evaluated the prototype in a five-day field study with 11 participants. In the baseline survey, 94.8% of respondents reported at least some awareness of AI energy use, yet 88.3% misestimated actual consumption. Although concern about environmental impact was high, only 39.0% indicated willingness to accept a performance trade-off for lower energy use. In the field study, Energy-efficient mode accounted for 55.8% of logged prompts, while 90.9% self-reported actively choosing Eco-mode when high accuracy was not required. Participants did not reduce prompt length, suggesting mode switching as the primary behavioral mechanism. Sustainability-oriented UI interventions can improve awareness and support more energy-responsible interaction patterns in LLM chatbots. These effects are best interpreted as behavioral and model-based estimates that complement backend efficiency work, and the provided prototype and replication package support further research on energy-aware conversational AI design.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims that sustainability-oriented UI interventions (three-mode energy switch, per-response feedback, pre-send estimates, dashboard, and analogies) in LLM chatbots can increase users' energy awareness and encourage more energy-responsible interaction patterns without harming usability. This is supported by a baseline survey (n=77) showing high awareness but misestimation of consumption and low willingness for performance trade-offs, plus a 5-day field study (n=11) where Energy-efficient mode was used in 55.8% of logged prompts (with 90.9% self-reporting active choice) and no reduction in prompt length.

Significance. If the field-study evidence were stronger, the work would be significant for shifting focus from model/infrastructure efficiency to the underexplored UI layer in sustainable conversational AI. The provision of a web-based prototype and replication package is a concrete strength that enables follow-on research. The survey data on awareness gaps and trade-off willingness are useful descriptive contributions.

major comments (2)
  1. [Field study section] Field study section (and abstract): The central claim that the interventions 'support more energy-responsible interaction patterns' rests on a 5-day study with n=11, no control arm, no pre/post baseline, no statistical tests, and no effect sizes. Logged Energy-efficient mode usage is only 55.8% while self-reports reach 90.9%, raising the possibility of demand effects or selection bias that cannot be ruled out.
  2. [Abstract and field study section] Abstract and field study section: Energy consumption is assessed only via prototype estimates rather than objective backend measurements, and the study reports no evaluation of potential hidden performance costs or usability trade-offs despite the survey finding that only 39% of respondents would accept such trade-offs.
minor comments (1)
  1. [Abstract] The abstract states that 'Participants did not reduce prompt length' as evidence for mode switching as the mechanism, but without a control condition this observational pattern cannot be attributed to the UI features.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting key limitations in our field study. We address each major comment below and will revise the manuscript to better reflect the exploratory scope of the work.

read point-by-point responses

  1. Referee: [Field study section] Field study section (and abstract): The central claim that the interventions 'support more energy-responsible interaction patterns' rests on a 5-day study with n=11, no control arm, no pre/post baseline, no statistical tests, and no effect sizes. Logged Energy-efficient mode usage is only 55.8% while self-reports reach 90.9%, raising the possibility of demand effects or selection bias that cannot be ruled out.

    Authors: We agree the study design limits causal claims: n=11 is small, there is no control arm, and no statistical tests or effect sizes are reported. The work was positioned as an initial field exploration of usage patterns rather than a confirmatory experiment. The 55.8% figure is aggregate usage across all prompts, while the 90.9% self-report applies specifically to cases where participants stated high accuracy was not required; we will clarify this distinction. We will revise the abstract and field-study section to emphasize the preliminary nature of the results, add an explicit limitations subsection discussing demand effects and selection bias, and avoid language implying strong causal support for the interventions. revision: partial

  2. Referee: [Abstract and field study section] Abstract and field study section: Energy consumption is assessed only via prototype estimates rather than objective backend measurements, and the study reports no evaluation of potential hidden performance costs or usability trade-offs despite the survey finding that only 39% of respondents would accept such trade-offs.

    Authors: Energy figures rely on published per-token estimates applied within the prototype because direct backend instrumentation was unavailable for this web-based implementation; we will state this explicitly in the methods. The field study used prompt length as a behavioral proxy and found no reduction, but we did not measure output quality, latency differences, or user satisfaction with responses across modes. Given the survey result that only 39% would accept performance trade-offs, we will revise the abstract to qualify or remove the unqualified claim of 'without reducing usability' and expand the discussion to identify direct evaluation of performance costs as future work. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical survey and field study with no derivations or fitted predictions

full rationale

The paper reports results from a baseline survey (N=77) and 5-day field study (N=11) using logged mode usage (55.8% Energy-efficient), self-reports (90.9%), and awareness percentages. No equations, parameters, predictions, or first-principles derivations appear; the conclusion that UI interventions 'can improve awareness and support more energy-responsible interaction patterns' is an observational summary of the collected data, not a reduction to prior fitted quantities or self-citations by construction. Self-citations or prior work on persuasive technology are cited only for design guidance, not as load-bearing uniqueness theorems or ansatzes that force the result.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim depends on the accuracy of the prototype's energy estimates and the assumption that short-term self-selected behavior in a small group reflects broader sustainable use patterns; no free parameters or invented entities are introduced.

axioms (2)
  • domain assumption Prototype energy estimates and feedback accurately reflect real consumption differences between modes
    All interventions (per-response feedback, pre-send estimates, mode switch) rely on these estimates being valid; abstract provides no independent verification method.
  • domain assumption Self-reported mode choices and lack of prompt-length change indicate genuine energy-responsible behavior without hidden costs
    Field-study conclusions rest on this interpretation of the 90.9% self-report and 55.8% usage figures.

pith-pipeline@v0.9.1-grok · 5850 in / 1540 out tokens · 30084 ms · 2026-06-27T12:28:56.916846+00:00 · methodology

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