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REVIEW 3 major objections 4 minor 185 references

What Does Success Look Like? Catalyzing Meeting Intentionality with AI-Assisted Prospective Reflection

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Meetings keep failing because the technology around them never asks why they exist; this paper shows that a generative-AI conversation before a meeting, one that probes purpose, success conditions, and challenges, can make intentions…

desk verdict A new, well-reported probe of GenAI for pre-meeting reflection, but the observed impacts are co-produced with a researcher, so the AI-specific claim is real but still conditional. read the letter →

arxiv 2505.14370 v1 pith:CLER7IKK submitted 2025-05-20 cs.HC

Ava Elizabeth Scott , Lev Tankelevitch , Payod Panda , Rishi Vanukuru , Xinyue Chen , Sean Rintel This is my paper
classification cs.HC
keywords meetingsmeetingintentionalityprospectivereflectiongenerativeAIconversationaluserinterfacetechnologyprobeparticipatorypromptingworkplace
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Most meeting technology optimizes the "how" of meetings: scheduling, coordination, in-meeting engagement, and post-meeting summaries. This paper argues the missing piece is the "why" — prospective reflection before a meeting, where participants think about why the meeting is needed, what success looks like, and what might go wrong. To test this, the authors built the Meeting Purpose Assistant (MPA), a generative-AI chatbot that walks users through exactly that reflection and then produces a structured Reflection Summary, and had 18 employees of a global technology company use it on three of their real upcoming meetings each. They report that the reflection clarified and prioritized purposes, shifted perspectives, improved preparation and communication, and in several cases led participants to change or even cancel meetings, with some effects confirmed after the meetings actually took place. The paper also catalogues why people resist such reflection — time cost, confidentiality concerns, the awkwardness of making social goals explicit, and the expectation that AI should give answers rather than ask questions.

What carries the argument

The carrying object is the Meeting Purpose Assistant (MPA), a technology probe built on GPT-4 Turbo behind an enterprise firewall, consisting of a chat interface and a one-click Reflection Summary pane. The MPA works through three linked components: a title assistant that extracts the meeting name, a purpose assistant that conducts the reflective conversation under a meta-prompt instructing it to use active listening, single open-ended questions, and a divergence-then-convergence arc (first exploring purpose and challenges broadly, then asking users to prioritize), and a summary assistant that distills the thread into bullet points under three fixed headings: why we are meeting, what success looks like, and what could prevent success. The Reflection Summary is the load-bearing artifact: it converts private reflection into a shareable work object, which is how reflection reaches the meeting itself through invitations, attendee preparation, and agenda building. The second piece of machinery is the participatory prompting methodology, in which a researcher sits beside each participant and guides their interaction with the AI, eliciting reasoning and resolving technical confusion, at the cost of confounding human and AI contributions to the reflection.

What would settle it

Run the same protocol in three conditions — the full MPA, a static questionnaire asking the identical purpose, success, and challenge questions without adaptive follow-up, and no tool at all — and compare preparation actions, meeting changes, and post-meeting effectiveness ratings; if the static questionnaire matches the MPA's effects, adaptive AI probing is not the active ingredient. A second test compares MPA sessions with and without a researcher present: if reported impacts vanish when no researcher is in the room, demand characteristics or human guidance rather than the AI explain the effect.

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Extended reading notes

Core claim

Meeting intentionality — a clear, articulated sense of why a meeting is happening and what success looks like — is largely unsupported by current meeting technology, and the paper argues this absence cascades into inefficiency, fatigue, and derailed discussions. The central claim is that a generative-AI conversational assistant can fill this gap by coaching users through prospective reflection: asking open questions about a meeting's purpose, probing for challenges and uncertainties, prompting prioritization, and finally distilling the conversation into a structured Reflection Summary that can be shared with attendees or pasted into a meeting invitation. With 18 employees reflecting on three real upcoming meetings each, the paper reports impacts in thinking — implicit goals made explicit, priorities clarified, unknown variables surfaced, other attendees' perspectives taken, anxiety reduced — and in action: participants shared agendas ahead of time, asked attendees to prepare, communicated potential challenges tactfully, changed recurring meeting formats, and sometimes concluded that a meeting should be canceled or that their own attendance was unnecessary. Follow-up surveys after the meetings had occurred confirmed that several of these intended changes were realized in practice. The paper presents this as an early, exploratory demonstration that GenAI can act as a thought-provoking coach in workplace reflection, while also cataloguing the social, temporal, and technological barriers that any real deployment would have to navigate.

Load-bearing premise

The load-bearing premise is that the observed reflection benefits came from the AI's questioning rather than from the researcher who sat alongside each participant guiding the session and eliciting reasoning; if the human guide did the work, the AI's specific contribution is overstated, a conflation risk the authors themselves acknowledge.

Editorial extensions

If this is right

  • If prospective reflection works as described, meeting tools should add a pre-meeting reflection step, with the natural implementation point being the invitation: a prompt to articulate purpose and success when scheduling, and a nudge to attendees for important or uncertain meetings.
  • Reflection Summaries are the mechanism that makes reflection actionable; participants pasted them into invitations and chat threads, and several reported in follow-up surveys that sharing them improved attendance, engagement, and meeting focus.
  • Reflection can change whether a meeting happens at all: participants concluded that some meetings were unnecessary, that their own presence was dispensable, or that a recurring series needed format changes such as attendance policies or timed agendas.
  • The optimal timing of reflection tracks the meeting's role and routine: soon after scheduling for organizers, close to the meeting for attendees and for instances of recurring meetings, and periodically at the series level to renew intentionality for the series as a whole.
  • GenAI's value in this context is as a persistent question-asker rather than an answer-giver; participants who expected instant solutions were frustrated, while those who engaged with the questions reported the lasting effects on clarity, preparation, and plans.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the MPA's effects replicate without a researcher present, the strongest product form suggested by this study is not a standalone chat but reflection embedded in the calendar: context-aware prompts at invitation time, with private provocative questioning and a shareable summary offered only when the user opts to go public; the authors gesture at this direction but do not test it.
  • The therapy- and coaching-like responses, including anxiety reduction and feeling validated, hint that part of the measured benefit may be emotional regulation rather than planning quality; a controlled study measuring stress and self-efficacy alongside plan changes could separate these two channels, which the paper does not do.
  • The confidentiality resistance observed in one-on-one and sensitive meetings predicts that privacy-preserving local or on-device reflection, or explicit guarantees about who can access reflective input, is a precondition for adoption in exactly the meetings where reflection is most needed; this is a testable deployment hypothesis the paper leaves open.
  • The finding that attendees with little control over a meeting found reflection less useful suggests the highest-leverage target for such tools is the meeting organizer, or any role with agency over format and agenda; a deployment study randomizing organizer-focused versus attendee-focused prompts would test this directly.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper argues that meeting technologies lack support for prospective reflection (thinking about why a meeting is needed and what might happen) and introduces a generative-AI technology probe, the Meeting Purpose Assistant (MPA), to coach users in articulating meeting purpose, success conditions, and challenges. In a participatory prompting study, 18 employees of a global technology company used the MPA on three real upcoming meetings each, with a researcher present to guide the interaction. Thematic analysis of session transcripts and follow-up surveys identified four groups of findings: the process of reflective interaction (including personalization and AI-generated reflection summaries); impacts on thinking (making purpose explicit, prioritization, reflecting on unknowns, perspective change, preparation); impacts on the meeting itself (sharing intentions, accountability, efficiency, changing meeting series); and barriers plus timing considerations. The paper concludes with design and workflow implications for AI-assisted prospective reflection.

Significance. If the central claim holds, this is a valuable exploratory contribution to meeting science and HCI: it opens a new design space for pre-meeting, GenAI-mediated reflection that goes beyond scheduling and in-meeting support, and it provides a richly documented set of participant responses, barriers, and design considerations. The paper is transparent about its method and limitations, includes a follow-up survey to probe whether effects outlasted the session, and provides full protocol and meta-prompt appendices, which supports reproducibility of the probe even if not of the specific model outputs. The study is appropriately framed as a technology probe rather than a controlled efficacy trial. However, the central claim that the observed impacts are attributable to the AI system is not yet established because the researcher co-produced every reflective session; the paper's own limitation statement (§6.4.2) acknowledges this risk.

major comments (3)
  1. [§5.2–§5.3 and RQ2] The central claim of the paper is that GenAI-assisted prospective reflection produced the observed impacts, but the study design embeds a researcher in every session. As described in §4.3.2, the researcher "could build a better understanding of the individual meeting, whilst surfacing a wider range of topics than the MPA alone," and the analysis does not code which prompts—MPA messages versus researcher utterances—preceded each reported impact. The authors themselves state in §6.4.2 that "the parallel conversations between the participant and AI, and the participant and researcher, risk the conflation of human and AI-driven reflection." Because RQ2 is specifically about "GenAI-assisted prospective reflection," this attribution gap is load-bearing. I recommend adding an attribution analysis: code each impact or reflective episode to the immediately preceding prompt source (MPA message, researcher question, participant-initiated turn) and report the distribution, or explicitly re-scope the claims to "reflection with an AI probe in a researcher-mediated session." Without this, the reported impacts could largely reflect the human guide rather than the AI.
  2. [§4.3.3 / Appendix C.3] The follow-up survey contains a leading question: "How did your interaction with the Meeting Purpose Assistant during the study influence the effectiveness of the meeting, if at all?" This presupposes an influence and, combined with the researcher's active presence during the session, creates a clear demand-characteristic pathway to the positive self-reports quoted in §5.3, such as the claim that the interaction helped meetings stay focused and effective. Please acknowledge this wording in the limitations section and soften the strength of the causal language in the findings (e.g., in §5.3.3) to "participants perceived, when asked, that the interaction influenced effectiveness," or provide neutral follow-up questions in any future iteration.
  3. [§5.2.1 and Appendix E] The finding "Making Purpose Explicit" is partly realized by construction: the MPA's meta-prompts instruct it to ask users to articulate purpose, success conditions, and challenges, and to probe until these are expressed. Observing that participants did articulate these elements is therefore to some extent a check that the probe followed its prompt, not an emergent effect. The more informative evidence for RQ2 is in the action-level impacts in §5.3 (changed plans, shared summaries, altered communication) and the follow-up survey reports. I suggest foregrounding those concrete action and follow-up findings in the abstract and in the answer to RQ2, and re-presenting §5.2.1 as evidence that the interaction elicited the designed reflection rather than as an independent impact.
minor comments (4)
  1. [§5.4.1] There is a typo in "an obvious confabulation by the MPA, wbhich suggested"—"wbhich" should be "which."
  2. [§5 and quotes throughout] The transcript excerpts repeatedly contain the unexplained token "TYPES:" (e.g., §5.2.2, §5.3.1, §5.4.3). This looks like a speech-to-text artifact from the researcher/participant talk. Please explain this notation in a footnote or remove it, as it currently confuses the reader about whether these are MPA-chat messages or spoken remarks.
  3. [§6.4.1] The sentence "Users of may end up talking to and reflecting with AI more than they do other people" is missing a noun after "of"; it should be "Users of such systems may end up..."
  4. [Table 1 and §5] The findings table and section headings refer to "Impact of Reflection: Change in Thinking" and "Impact of Reflection: Changing the Meeting," but the discussion and abstract use "observed impacts." Consider adding a qualifier such as "perceived" or "reported" in these headings and in the summary of findings to match the self-report nature of the evidence.

Circularity Check

1 steps flagged · score 2.0 of 10

No significant derivational circularity; the one near-circular element is that the MPA's meta-prompted function (eliciting purpose, challenges, and success conditions) is partly reported back as an observed 'impact,' while the motivating premise cites the authors' own prior work; neither reduces the central, empirically grounded claims.

  1. self definitional [Appendix E.1 meta-prompt (steps 1–2) vs. Abstract 'Observed impacts' and §5.2.1]
    "First step: Ask the user to reflect upon and articulate the intended meeting purpose, goals, outputs, or deliverables of the meeting... Second step: Only once the meeting purpose or value has been extensively articulated, then prompt the user to articulate any uncertainties or challenges around achieving this purpose."

    The paper reports as an 'observed impact' that participants clarified meeting purposes, challenges, and success conditions (Abstract; §5.2.1), but the MPA's meta-prompt instructs the AI to produce exactly this behavior: ask the user to articulate purpose, then prompt them to articulate uncertainties or challenges. That headline impact is therefore the probe's designed function by construction—a manipulation check presented as a discovery. The circularity is partial: §5.3 behavior changes were confirmed in the post-meeting follow-up survey, and §5.4 barriers (confidentiality, desire for solutions, resistance to social-goal elicitation) were not designed into the prompts, so the central claims do not reduce to the design. The authors also disclaim the human/AI conflation risk in §6.4.2.

full rationale

This is an exploratory qualitative study, not a derivational paper: there are no fitted parameters renamed as predictions, no uniqueness theorems, and no equations whose output equals their input. The central claims—perceived usefulness, changed meeting plans, improved preparation, and the four barrier categories—are grounded in participant self-report during the sessions and, for behavior change, in the post-meeting follow-up survey (§4.3.3, §5.3), which is external evidence collected after the MPA interaction. Several findings contradict or complicate the design intent rather than confirming it: participants resisted explicit social-goal elicitation (§5.4.2), wanted solutions the MPA was meta-prompted not to give (§5.4.3 and Appendix E: 'Do not try to solve or reach the meeting's goal'), and reported a confabulation (§5.4.1). That pattern is evidence the analysis is not a self-fulfilling restatement of the prompts. Two caveats keep the score at 2 rather than 0. First, the abstract's first listed 'observed impact'—clarifying meeting purposes, challenges, and success conditions—is precisely the behavior the MPA's meta-prompt instructs the AI to elicit (Appendix E.1), so that particular finding is partly the probe's designed function by construction; §5.2.1's 'Making Purpose Explicit' is the same prompt-driven process. Second, the motivating premise that meetings lack intentionality and that technologies should support prospective reflection is anchored in the authors' own prior paper [139], and the participatory-prompting method is cited from the authors' own methodology line [49, 134]. These self-citations are real but not circular: [139] is a separate empirical study with different data, the current paper tests rather than assumes its prescription, and the findings stand on new transcript and survey data. The most serious threat is not circularity but attribution, and the authors flag it themselves: 'The parallel conversations between the participant and AI, and the participant and researcher, risk the conflation of human and AI-driven reflection' (§6.4.2), and 'All participants expressed more to the researcher via speech than they did to the MPA via text. This could reflect demand characteristics' (§5.4.1). A researcher actively guided each session (§4.3.2), so the AI-specific contribution to the observed impacts is not cleanly isolated.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper's central claim rests on assumptions about the value of reflection and the capacity of GenAI to facilitate it, but no free parameters are fitted. The technology probe is a design artifact, not a new theoretical entity.

assumptions (3)
  • domain assumption Prospective reflection on meeting purpose can improve meeting intentionality and outcomes.
    Motivated by prior work on reflection (e.g., Boud et al., Schon, Bentvelzen et al.) and used to frame the research questions in Section 3.
  • domain assumption GenAI conversational agents can provide personalized and effective reflection support.
    Based on prior work on GenAI as a tool for thought and reflective question-asking (Section 2.4).
  • domain assumption Participants' self-reported impacts and follow-up survey responses are a valid measure of the intervention's effects.
    The study relies on thematic analysis of self-reports and follow-up surveys; no objective behavioral metrics are collected (Sections 4.4, 5).

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Cite this review

Pith. "Pith review of What Does Success Look Like? Catalyzing Meeting Intentionality with AI-Assisted Prospective Reflection." pith.science (2026). https://pith.science/paper/CLER7IKK

@misc{pith2026250514370,
  author       = {Pith},
  title        = {Pith review of: What Does Success Look Like? Catalyzing Meeting Intentionality with AI-Assisted Prospective Reflection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CLER7IKK}},
  note         = {Machine review of arXiv:2505.14370}
}
read the original abstract

Despite decades of HCI and Meeting Science research, complaints about ineffective meetings are still pervasive. We argue that meeting technologies lack support for prospective reflection, that is, thinking about why a meeting is needed and what might happen. To explore this, we designed a Meeting Purpose Assistant (MPA) technology probe to coach users to articulate their meeting's purpose and challenges, and act accordingly. The MPA used Generative AI to support personalized and actionable prospective reflection across the diversity of meeting contexts. Using a participatory prompting methodology, 18 employees of a global technology company reflected with the MPA on upcoming meetings. Observed impacts were: clarifying meeting purposes, challenges, and success conditions; changing perspectives and flexibility; improving preparation and communication; and proposing changed plans. We also identify perceived social, temporal, and technological barriers to using the MPA. We present system and workflow design considerations for developing AI-assisted reflection support for meetings.

Figures

Figures reproduced from arXiv: 2505.14370 by the authors.

Figure 1
Figure 1. Overview of this paper’s exploration of how a generative AI Meeting Purpose Assistant (MPA) can support prospective [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The Meeting Purpose Assistant technology probe consisted of a chat interface (left), powered by GPT-4 Turbo, that [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. System diagram for the Meeting Purpose Assistant. The client machine (left) interacted with the assistants on OpenAI [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.