{"id":"776793db-d0de-40a0-9a76-a106939b7247","arxiv_id":"2606.05055","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"The paper proposes an ESM study with dyads to evaluate generative AI sketches for balancing privacy and awareness needs in inter-generational caregiving.","lead":"This paper describes a planned 10-day ESM study using generative AI to create abstract visual summaries of daily activities for older adults and their adult child caregivers. A smart generalist might read it to understand privacy-preserving approaches in family caregiving technologies.","discovery_kind":"unclear","skeptic_critique":{"model":"grok-4.3","headline":"Self-reports on willingness to share pre-generated sketches may not predict real-world boundary behaviors","rationale":"The reader's weakest assumption directly identifies the core methodological risk for a protocol paper whose claims are prospective. No stronger internal inconsistency or assumption failure is evident from the described design.","tokens_in":1687,"tokens_out":235,"duration_ms":16543,"concrete_test":"After data collection, compute correlation between ESM willingness scores and any observed sharing behaviors or post-study deployment logs; if Pearson r < 0.5, the mismatch quantification and guidelines rest on an unvalidated proxy.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The study's central aim—to quantify generational privacy mismatch and derive design guidelines—rests on ESM prompts where participants report context and rate pre-generated AI sketches for sharing/receiving willingness, plus follow-up interviews. This design implicitly assumes prompted self-reports will validly proxy authentic, consequential privacy decisions. However, the protocol uses pre-generated (not context-matched) sketches and lacks real-time data capture or actual sharing consequences, creating a gap between measured responses and the real caregiving scenarios the guidelines target.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript presents a study protocol for a 10-day Experience Sampling Method (ESM) investigation with older adult–adult child dyads. Daily smartphone prompts ask participants to report context and rate pre-generated generative-AI sketches for willingness to share or receive; follow-up interviews explore boundary-setting. The stated aims are to quantify generational privacy mismatch and produce actionable design guidelines for visual-abstraction techniques in AI-mediated caregiving tools.","tokens_in":1758,"tokens_out":420,"duration_ms":31667,"significance":"If executed and the measured responses prove predictive, the work could supply empirical grounding for privacy-preserving visual summaries in informal caregiving—an area of clear societal relevance. The dyadic, two-generation design and explicit focus on both awareness and dignity are constructive. Because the manuscript contains no collected data, the significance remains prospective and hinges on whether the protocol’s measurement approach can support the intended claims.","major_comments":[{"comment":"The protocol’s central claim—to quantify the privacy mismatch and derive design guidelines—rests on the assumption that ESM self-reports of willingness to share or receive pre-generated sketches will validly proxy real-world boundary-setting behavior. The described method uses non-context-matched, pre-generated images and contains no actual sharing consequences or real-time capture, creating a gap between the measured responses and the consequential caregiving scenarios the guidelines are intended to address.","section":null}],"minor_comments":[{"comment":"The method description does not specify the source, selection criteria, or generation parameters for the pre-generated sketches, leaving unclear how visual abstraction levels will be controlled or varied across prompts.","section":null},{"comment":"No sample-size justification, power analysis, or recruitment targets for the dyads are provided, which affects the feasibility of the planned quantification of generational differences.","section":null}],"recommendation":"major_revision","confidential_remarks":"The manuscript is a methods-only protocol paper. Depending on the journal’s policy for publishing study protocols versus completed empirical work, this may affect fit; the design-validity concern above is the primary substantive issue."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their constructive review of our study protocol manuscript. The major comment raises an important point about the validity of our measurement approach, which we address directly below. We have revised the manuscript to better articulate the scope and limitations of the proposed method.","responses":[{"response":"We acknowledge that self-reported willingness ratings on pre-generated sketches cannot fully replicate the dynamics of real-world boundary-setting, where actual consequences, real-time capture, and matched contexts influence decisions. This is an inherent limitation of any perception-based protocol that prioritizes participant safety and ethical constraints over direct observation of sharing. Our design uses ESM to sample responses in participants' natural environments while they report their current context, providing a degree of ecological grounding; pre-generated sketches ensure stimulus consistency and avoid privacy risks from live capture. Follow-up interviews are intended to probe the reasoning behind ratings. We agree the original framing overstated the direct applicability to consequential scenarios. The revised manuscript now (1) explicitly states that the study measures reported willingness rather than observed behavior, (2) qualifies the resulting design guidelines as preliminary and perception-informed, and (3) adds a dedicated limitations subsection discussing the proxy gap and calling for future validation with deployed systems.","revision_made":"yes","referee_comment":"The protocol’s central claim—to quantify the privacy mismatch and derive design guidelines—rests on the assumption that ESM self-reports of willingness to share or receive pre-generated sketches will validly proxy real-world boundary-setting behavior. The described method uses non-context-matched, pre-generated images and contains no actual sharing consequences or real-time capture, creating a gap between the measured responses and the consequential caregiving scenarios the guidelines are intended to address."}],"tokens_in":1258,"tokens_out":363,"duration_ms":25909,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper lays out a 10-day ESM protocol where older adults and adult children rate pre-generated AI sketches for willingness to share or receive, plus interviews on boundaries. The goal is to measure generational privacy differences and generate design guidelines for abstract visuals in monitoring tools.\n\nWhat stands out is the targeted setup: using generative AI for visual summaries in inter-generational pairs, with daily context prompts to tie responses to real situations. The framing of the awareness-privacy tension in aging-in-place is clear and practical.\n\nThe main limitation is that nothing has been run. All claims about quantifying mismatch or producing usable guidelines rest on future execution. The protocol also uses pre-generated sketches rather than ones matched to the moment, and it measures stated willingness instead of actual sharing behavior under consequences. That gap between prompted ratings and real decisions is the obvious risk in the design.\n\nThis is for HCI and elder-care tech researchers who need concrete methods for privacy studies. Someone building similar tools could borrow the ESM structure or the sketch evaluation approach. The thinking is straightforward and engages the right literature on monitoring tech.\n\nIf the venue publishes protocols or methods papers, it is worth sending out for review. Otherwise it should wait for the actual data.","headline":"This is a study protocol for an ESM experiment on GenAI sketches in caregiving dyads, with no data or results yet.","tokens_in":2228,"tokens_out":318,"would_cite":false,"duration_ms":13475,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Generative AI can produce abstract visual summaries that allow adult children to maintain awareness of older adults' daily activities while respecting their privacy.","keywords":["generative AI","privacy preservation","inter-generational caregiving","visual abstraction","experience sampling method","older adults","adult children","awareness tools"],"falsifier":"A finding that participants are unwilling to share or receive the sketches at similar rates as they would raw video feeds, or that the sketches fail to provide enough awareness according to the adult children, would indicate the method does not achieve the intended balance.","tokens_in":2580,"feed_emoji":"","tokens_out":593,"duration_ms":27515,"temperature":0.7,"pith_summary":"This paper investigates the use of generative AI to create abstract sketches of daily activities as a way to resolve the tension between caregivers' need for information and older adults' need for privacy. It describes a planned 10-day experience sampling study involving pairs of older adults and their adult children, where participants evaluate pre-generated AI sketches and report their willingness to share or receive them. The study seeks to measure generational differences in privacy boundaries and to develop concrete design guidelines for AI tools in caregiving. If successful, this approach could replace intrusive video monitoring with less invasive visual abstractions that still convey essential context.","feed_headline":"AI sketches let adult children glimpse parents' days privately","feed_subtitle":"A study uses generative AI to test abstract visual summaries that aim to give awareness without invading privacy in elder care.","key_machinery":"Pre-generated AI sketches evaluated through daily ESM prompts for willingness to share or receive, serving as visual summaries that abstract away raw details.","core_discovery":"The central claim is that generative AI can generate privacy-preserving visual summaries of daily life that support inter-generational caregiving by providing sufficient awareness without compromising the older adult's autonomy and dignity.","pith_inferences":["These guidelines might extend to other family monitoring contexts beyond caregiving, such as checking on distant relatives.","Testing the sketches in actual deployed systems rather than pre-generated ones could reveal additional practical issues.","Broader adoption could shift norms around privacy in aging-in-place technologies."],"forward_implications":["Quantifying the privacy mismatch will reveal specific differences in what each generation finds acceptable.","Actionable design guidelines will emerge for the level of abstraction needed in such tools.","AI-mediated tools using this method could support connection while protecting dignity better than traditional monitoring.","Boundary-setting behaviors identified in interviews will inform how to implement these systems in practice."],"fun_headline_variants":["GenAI sketches protect elder privacy in family caregiving","Abstract AI visuals balance awareness and dignity in elder care","Generative AI summaries enable private inter-generational monitoring","Privacy AI images support child awareness without invading autonomy"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"That participants' self-reports on willingness to share or receive the AI sketches in the study prompts will accurately reflect their real-world boundary-setting behaviors in caregiving situations.","fun_headline_variants_meta":{"raw":{"variants":["GenAI sketches protect elder privacy in family caregiving","Abstract AI visuals balance awareness and dignity in elder care","Generative AI summaries enable private inter-generational monitoring","Privacy AI images support child awareness without invading autonomy"]},"model":"grok-4.3","cost_usd":0.004974,"raw_usage":{"total_tokens":2391,"prompt_tokens":588,"num_sources_used":0,"completion_tokens":52,"cost_in_usd_ticks":49737000,"prompt_tokens_details":{"text_tokens":588,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1751,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":588,"tokens_out":52,"duration_ms":16010,"temperature":1.0,"reasoning_tokens":1751,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-28T04:02:39.867424+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A finding that participants are unwilling to share or receive the sketches at similar rates as they would raw video feeds, or that the sketches fail to provide enough awareness according to the adult children, would indicate the method does not achieve the intended balance.","supporting_citations":[],"review_version":1}