AUGMANITAI Compendium — Wave 1 (104 Terms)
A systematic terminology for human experiences in AI interaction. Wave 1 release: 104 terms from the AUGMANITAI Compendium V2.1. By Andreas Ehstand — Independent Researcher, Tennis & Padel Coach, Terminology Researcher. New words for new worlds. Stay human. ❤️🤖
Independent researcher, tennis & padel coach, and independent researcher in applied terminology science. Creator of the AUGMANITAI Compendium (2,000+ named phenomena for human experiences in AI interaction). His path led from 25 years of professional sport coaching — including multiple years as a Bundesliga tennis coach — through software development into AI research, resulting in the an extensive terminology corpus for human-AI interaction. Background in academic pedagogy research with additional quantitative methods training. Holder of the Zertifikat Hochschullehre Bayern. GPTCA/ISMCA Mental Coach with endorsements from Toni Nadal, Tomas Smid, Alberto Castellani, Hernan Flores, Dominic Condrau (Paris 2024 Olympic finalist), and the head coach of the German national golf team. Inspired by ISO 704/1087/30042 principles. CC BY-NC-ND 4.0. Stay human. ❤️🤖 From court to code to compendium. 🎾🤖❤️
A self-reinforcing humorous exchange between a user and an AI system in which each party's playful turn prompts a further playful turn, lowering perceived tension. Distinguished from a single joke by its turn-to-turn escalation.
EVENT. One occurrence would be coded when, within a dialogue transcript, a humorous turn by one party is followed by an on-topic humorous turn from the other party that explicitly builds on it, forming a run of at least two reciprocal humorous turns. The run is the unit counted; the first non-humorous or topic-resetting turn ends it.
Proposed measurement protocol (not yet empirically validated): Two raters annotate transcripts for reciprocal-humor runs and record run length (number of consecutive humorous turns); inter-rater agreement on run boundaries could be reported as Cohen's kappa. Could be reported as runs per 100 turns and mean run length.
Both partners generate humor and the human signals levity within a 2-turn window.
Sarcastic, hostile, or one-sided humor lacking reciprocal acknowledgment.
A corrective AI response that frames a needed change to the user's input or assumption in a light, humorous register; a pattern in which the user's reported acceptance of the correction is observed to be higher than for an equivalent plain correction. Distinguished from a plain correction by its humorous framing.
EVENT. One occurrence would be coded when an AI turn both (a) signals that the user's prior input was wrong, suboptimal, or incomplete and (b) carries a humorous device (pun, playful exaggeration, ironic aside), and the immediately following user turn adopts the suggested change. Coders tag the correction turn and the user-uptake turn.
Proposed measurement protocol (not yet empirically validated): Rater coding of correction turns into 'humor-framed' vs 'plain', paired with a binary uptake judgment on the next user turn; agreement would be assessed via Cohen's kappa. Primary metric: correction-acceptance rate (accepted humor-framed corrections / all humor-framed corrections).
Brief (<30 token) AI humor-cue followed by human levity-signal in the next turn.
Extended humorous exchanges, jokes longer than 30 tokens, or unsolicited human-initiated humor.
A delayed humorous reaction in which the user first processes an AI output's literal meaning and only afterward perceives it as funny, often laughing at their own delayed comprehension. Distinguished from immediate humor by the lag between exposure and amusement.
EVENT. One occurrence would be coded when a user signals amusement (explicit laughter token such as 'haha', or a remark like 'oh, now I get it') referring to an AI turn that is at least one user turn earlier, i.e. amusement is expressed only after an intervening non-humorous turn. The delayed-amusement turn is the unit counted.
Proposed measurement protocol (not yet empirically validated): Latency measure: for each amusement signal, coders link it to the eliciting AI turn and record the turn-distance and elapsed seconds between exposure and amusement; linkage agreement would be assessed via Cohen's kappa. Could be reported as median exposure-to-amusement latency and share of amusement signals that are delayed (>0 intervening turns).
Explicit retrospective reference to an earlier specific turn paired with amusement marker.
Generic positive session-summary statements without reference to a specific earlier turn.
A realization by the user that an unsatisfactory AI output stemmed from an unclear or underspecified prompt rather than from a model error, prompting the user to revise the input. Distinguished from generic insight by its attribution of shortfall to one's own input.
EVENT. One occurrence would be coded when, following an unsatisfactory AI output, the user produces a turn that (a) attributes the shortfall to their own prior prompt (e.g. 'I wasn't clear', 'I meant') and (b) supplies a more specified reformulation in the same or next turn. The attribution-plus-reformulation turn is the unit counted.
Proposed measurement protocol (not yet empirically validated): Rater coding of post-shortfall user turns for self-attribution of the input shortfall and for presence of a more-specified reformulation; agreement would be assessed via Cohen's kappa. Primary metric: self-attribution-and-repair count per 100 user-initiated repairs, optionally cross-checked against measured increase in prompt specificity (added constraints/tokens).
GSR peak co-occurring with verbal aha-marker plus stylistic distance above 0.3 in subsequent turn-window.
GSR peaks attributable to external stimuli (interruption, ambient noise) confirmed via concurrent sensor data.
An insight arising from AI interaction that the user reports as changing how they perceive an everyday phenomenon, such as a new perspective, an unexpected connection, or a useful reinterpretation. Distinguished from in-task problem-solving by its carry-over into ordinary life.
EVENT. One occurrence would be coded when a user statement, during or after a session, reports a changed everyday understanding attributable to the interaction (naming the prior view and the revised view). The report is the unit counted; restatements of the same insight within one session are counted once.
Proposed measurement protocol (not yet empirically validated): Diary/exit self-report: users log everyday insights with a brief before/after description; two raters classify each entry as a genuine perspective change vs restated prior knowledge, agreement would be assessed via Cohen's kappa. Could be reported as verified everyday-insight reports per user per week.
Diary entry within 24h linking a routine AI exchange to a stated realization.
Insights from explicit problem-solving sessions or task-instructed reflection.
Rhetorical-Register Discrimination Ability
An individual user's graded ability to identify the rhetorical register of an AI output, for example whether it is serious, ironic, neutral, or exaggerated. Distinguished from comprehension of content by its focus on tone, and it varies across users.
STATE. The rated quantity is a user's tone-classification accuracy: across a fixed set of AI outputs pre-labelled for rhetorical register by expert consensus, the proportion the user labels correctly. Higher proportion indicates greater ability. Measured per user, not per interaction.
Proposed measurement protocol (not yet empirically validated): Forced-choice tone-classification test: each user labels a calibrated item set against an expert-consensus key; ability scored as percent correct (or d-prime for irony vs literal). Item-set reliability reported via Cronbach's alpha; expert key reliability via Fleiss' kappa across labellers.
Closed-set label selection with rationale linked to textual cues.
Random or pattern-only responding without justification cues.
A deliberately incoherent, heterogeneous prompt assembled by the user so that the AI system produces unexpected or creative combinations from the disparate elements. Distinguished from an ordinary prompt by its intentional internal inconsistency.
EVENT. One occurrence would be coded when a user prompt deliberately juxtaposes three or more semantically unrelated elements (topics, registers, or domains) in a single request, as judged by coders, with the apparent aim of eliciting novel combination. The composite prompt is the unit counted.
Proposed measurement protocol (not yet empirically validated): Prompt-level content coding: raters count distinct unrelated elements per prompt and flag intentional juxtaposition; agreement would be assessed via Cohen's kappa. Complementary objective index: lexical-semantic dispersion of the prompt (mean pairwise embedding distance of its content segments). Could be reported as deliberate-mix prompts per session and mean dispersion.
Multi-domain output identifiable AND hedonic rating completed.
Outputs where the user explicitly requested a mash-up (no surprise component).
A targeted use of humor within an interaction intended to release built-up tension and restore the user's capacity to keep working. Distinguished from incidental humor by its deployment at a point of friction or stall.
EVENT. One occurrence would be coded when a humorous turn occurs immediately after a friction marker (expressed frustration, repeated unsuccessful attempt, or stall) and is followed within two turns by resumption of task-directed activity. The humor turn at the friction point is the unit counted.
Proposed measurement protocol (not yet empirically validated): Sequential coding: raters tag friction markers, subsequent humor turns, and task-resumption within a two-turn window; boundary agreement would be assessed via Cohen's kappa. Primary metric: task-resumption rate after friction-point humor, with median turns-to-resumption as a secondary latency indicator.
A brief, unexpected moment of positive affect during an interaction that the user reports as releasing energy or lifting engagement. Distinguished from sustained mood by its short, punctate character.
EVENT. One occurrence would be coded when a user emits a discrete positive-affect signal (e.g. 'oh nice!', exclamation of delight) tied to a specific AI turn, lasting a single turn rather than a sustained passage. The affect-signal turn is the unit counted.
Proposed measurement protocol (not yet empirically validated): In-the-moment self-report: users tap a single-press affect marker when they feel a positive jolt, timestamped against the eliciting turn; optionally validated against rater-coded positive-affect tokens (agreement would be assessed via Cohen's kappa). Could be reported as positive-jolt events per session.
A self-reinforcing sequence of humorous reactions across consecutive turns that produces a sense of lightness and can dislodge a cognitive block. Distinguished from a single humorous exchange by its escalating chain of three or more reactions.
EVENT. One occurrence would be coded when at least three consecutive turns each contain a humorous reaction that builds on the prior one, forming an unbroken chain. The chain is the unit counted; it ends at the first non-humorous turn. (Differs from Reciprocal Humor Escalation by requiring a longer unbroken run.)
Proposed measurement protocol (not yet empirically validated): Transcript coding of unbroken humorous chains with a minimum length of three turns; raters record chain length and whether a preceding stall was present, boundary agreement would be assessed via Cohen's kappa. Could be reported as chains per 100 turns and mean chain length.
At least 3 consecutive humor-marked turns within 5-turn window.
Chains broken by topic-shift or single-side humor maintenance.
A short interaction intended to restore the user's motivation and positive mood after a dip. Distinguished from ongoing encouragement by its brief, bounded, restorative character.
EVENT. One occurrence would be coded when, following a user signal of low motivation or mood, a brief AI intervention (one to two turns) is followed by a user signal of restored willingness to continue (e.g. 'ok, let's keep going'). The intervention-plus-recovery pair is the unit counted.
Proposed measurement protocol (not yet empirically validated): Pre/post single-item mood-and-motivation self-report (e.g. a 1-7 'ready to continue' rating) bracketing the intervention; effect quantified as the within-user pre-to-post change. Complementary behavioural index: whether the user resumed the task within two turns. Could be reported as mean motivation gain and resumption rate.
The user's practice of deliberately inserting a pause between receiving an AI output and sending the next input, in order to avoid impulsive replies and improve the quality of the following prompt. Distinguished from incidental delay by its intentional, regulatory character.
STATE. The rated quantity is the user's deliberate inter-turn pausing: operationalised as the inter-turn interval (seconds between receiving an output and sending the next input) for turns the user marks or self-reports as intentional pauses, contrasted with their baseline interval. Higher relative interval on reflective turns indicates more of the construct.
Proposed measurement protocol (not yet empirically validated): Behavioural timing: log inter-turn latency per turn; compare median latency on user-flagged reflective turns against the user's overall median (within-subject ratio). Complementary self-report: a short habit scale (e.g. 1-7, 'I pause before replying') for trait-level pausing. Could be reported as reflective-turn latency ratio and habit-scale score.
Reflection-Impulsivity assessment completed AND at least 50 prompt-pairs logged for latency analysis.
Participants whose latency is dominated by external interruptions.
An experienced user's implicit, practice-acquired sense for which kind of input tends to yield which kind of result, applied without deliberate analysis each time. Distinguished from explicit prompting knowledge by being tacit and predictive rather than reasoned step by step.
STATE. The rated quantity is predictive accuracy without deliberation: given candidate prompts, the user forecasts which will yield the better result; accuracy is the proportion of forecasts confirmed by subsequent outputs under blind evaluation. Higher accuracy, achieved with low reported deliberation, indicates more of the construct. Measured per user.
Proposed measurement protocol (not yet empirically validated): Prediction task: users rank or choose among candidate prompts before execution; outcomes are graded blind by independent raters, and the user's hit rate is compared to chance. Deliberation is captured by decision latency and a brief 'how much did you reason' item. Could be reported as above-chance prediction accuracy at low deliberation; rater grading reliability via ICC.
Pre-output prediction grounded in stated prior-interaction reasoning.
Random guesses or predictions made after partial output reveal.
An AI error that the user consciously accepts and treats as a learning signal, responding with quick correction and continuation rather than annoyance. Distinguished from an unremarked error by the user's explicit non-frustrated, corrective response.
EVENT. One occurrence would be coded when an identifiable AI error is followed by a user turn that (a) acknowledges the error without expressed frustration and (b) issues a corrective instruction, with the task continuing in the next turn. The error-plus-non-frustrated-correction pair is the unit counted.
Proposed measurement protocol (not yet empirically validated): Rater coding of post-error user turns on two dimensions, frustration tone (present/absent) and corrective action (present/absent); agreement would be assessed via Cohen's kappa. Primary metric: share of AI errors met with non-frustrated correction; secondary: turns-to-task-continuation.
Identifiable error AND Goal Orientation scale completed within 14 days.
Errors leading to immediate session-termination.
A user's deliberate act of marking a positive moment from the day, often with AI support, to consolidate it emotionally. Distinguished from passive recall by the explicit selection and recording of the moment.
EVENT. One occurrence would be coded when the user explicitly designates a specific positive moment of the day for recording or reflection (e.g. naming it as 'the highlight'), producing a recorded or stated entry. The designation entry is the unit counted; one per reviewed period unless distinct moments are named.
Proposed measurement protocol (not yet empirically validated): Structured daily-review log: users record a single named positive highlight per session; completion is tracked as adherence (entries / eligible days), and entry quality (specific event vs vague) is rated by two coders with Cohen's kappa. Could be reported as adherence rate and specific-entry proportion.
A small verbal cue from the AI that elicits a disproportionately large sense of calm in the user. Distinguished from extended de-escalation by its minimal linguistic footprint relative to its calming effect.
EVENT. One occurrence would be coded when a brief AI utterance (a single short phrase or sentence) is immediately followed by a user signal of increased calm (explicit statement, or a measured drop in agitation markers such as exclamation density or all-caps). The cue-plus-calming-response pair is the unit counted.
Proposed measurement protocol (not yet empirically validated): Pre/post momentary-calm self-report (single-item 1-7) bracketing the cue, paired with a length check confirming the cue is brief (token count below a set threshold). Effect quantified as calm gain per token of cue. Complementary objective marker: change in agitation tokens before vs after. Could be reported as mean calm gain and effect-per-token.
FFMQ completed within 30 days AND in-session spark-marker mechanism available.
Markers tapped without subsequent serenity-state completion.
An AI error correction delivered in a playful tone that preserves the user's willingness to learn rather than provoking defensiveness. Distinguished from a neutral correction by its playful register and its effect on receptiveness.
EVENT. One occurrence would be coded when an AI correction turn carries a playful register and the following user turn shows continued engagement (accepts the point, asks a follow-up, or proceeds) rather than defensiveness (denial, disengagement). The correction-plus-engaged-response pair is the unit counted.
Proposed measurement protocol (not yet empirically validated): Rater coding pairing correction register (playful/neutral) with the subsequent user response (engaged/defensive); agreement would be assessed via Cohen's kappa. Primary metric: continued-engagement rate following playful corrections, compared against neutral corrections as a within-data contrast.
A moment in which achieved progress is reflected back to the user, with the aim of strengthening their sense of self-efficacy. Distinguished from generic praise by its specific reference to concrete progress already made.
EVENT. One occurrence would be coded when an AI (or user-self) turn explicitly references a specific, completed increment of progress (naming what was accomplished) in a way that attributes it to the user. The progress-reflection turn is the unit counted; vague praise without a named accomplishment does not qualify.
Proposed measurement protocol (not yet empirically validated): Pre/post self-efficacy self-report on the relevant task using a brief validated scale (e.g. a short General Self-Efficacy item set, 1-5); effect quantified as within-user change after progress-reflection turns. Coders verify each turn names a concrete accomplishment, with agreement would be assessed via Cohen's kappa. Could be reported as self-efficacy change and named-accomplishment rate.
A heightened affective state of connection, joy, or meaning that a user reports during an interaction. Distinguished from a brief affect spike by being a sustained, graded condition rather than a single moment.
STATE. The rated quantity is the intensity of felt connection/joy/meaning during a defined interaction passage, self-reported on a graded scale at the end of the passage. Higher ratings indicate a stronger state. Rated per passage, not counted as discrete events.
Proposed measurement protocol (not yet empirically validated): Multi-item affect self-report combining a connection/closeness item set (e.g. Could be reported as mean state-intensity score per passage.
A user's disposition to register AI or task errors with composure because the learning value of the error is clearly apparent. Distinguished from a single calm reaction by being a graded, trait-like tendency across errors.
STATE. The rated quantity is the user's typical composure-on-error: across multiple error episodes, the proportion met with a calm, learning-oriented response (versus frustration), or an equivalent self-reported trait rating. Higher proportion or rating indicates more of the disposition. Aggregated per user.
Proposed measurement protocol (not yet empirically validated): Aggregated behavioural index: across a user's error episodes, raters code each response as composed/learning-oriented vs frustrated (Cohen's kappa for agreement), yielding a per-user composure proportion. Complementary trait self-report: a short error-orientation scale (learning vs strain subscales, 1-5). Could be reported as composure proportion and error-orientation score.
An unsought finding that the user did not set out to obtain but recognizes as valuable once it appears during interaction. Distinguished from a sought answer by its incidental, unplanned origin.
EVENT. One occurrence would be coded when a user turn flags an AI output as valuable while also indicating it was outside the stated goal of the current request (e.g. 'I wasn't looking for that, but that's useful'). The serendipity-flagging turn is the unit counted.
Proposed measurement protocol (not yet empirically validated): Rater coding of user turns for two co-present features, expressed value and explicit off-goal origin, against the session's stated task; agreement would be assessed via Cohen's kappa. Complementary self-report: users tag outputs as 'unexpected and useful'. Could be reported as serendipitous-finding events per session.
A targeted restoration of the user's motivation following frustration or exhaustion during an interaction. Distinguished from general encouragement by its placement after an explicit motivational low and its restorative intent.
EVENT. One occurrence would be coded when, after a user signal of frustration or exhaustion, an intervention is followed by a user signal of recovered drive to continue the task. The low-signal, intervention, recovery-signal sequence is the unit counted.
Proposed measurement protocol (not yet empirically validated): Pre/post single-item motivation self-report (1-7 'drive to continue') bracketing the intervention; effect quantified as within-user change, with a behavioural check on whether the task resumed. Could be reported as mean motivation recovery and resumption rate. (Distinguished from AUG-1044 by requiring an explicit frustration/exhaustion antecedent rather than a general mood dip.)
A regulating interaction mode characterised by reduced pace, sentence rhythm, and complexity in the AI's turns; a pattern in which the user's reported perceived tempo of the exchange is observed to be lower. Distinguished from distraction by being a slowing of the exchange rather than a topic shift.
STATE. The rated quantity is the degree of deliberate slowing within a passage, indexed by measurable output features: reduced words-per-turn, shorter sentences, and lower lexical complexity relative to the session baseline. A larger reduction across these features indicates more of the mode. Rated/measured per passage.
Proposed measurement protocol (not yet empirically validated): Text-metric comparison of 'calming' passages against the session baseline: mean sentence length, words per turn, and a readability index (e.g. Flesch reading-ease); paired with a user momentary-tempo self-report (1-7 'how rushed do you feel'). Could be reported as percent reduction in complexity metrics and change in felt tempo.
ECG or PPG-validated HRV recording with at least 5 minutes pre- and post-baselines.
Participants on beta-blockers or with documented arrhythmia.
A reflective state in which the user consciously notices positive aspects of an experience and anchors them emotionally, often prompted by the interaction. Distinguished from a fleeting good feeling by its deliberate, reflective appraisal.
STATE. The rated quantity is the intensity of state gratitude during or after a reflective passage, self-reported on a graded scale, optionally corroborated by the user naming specific positives. Higher ratings indicate a stronger state. Rated per passage, not counted as discrete events.
Proposed measurement protocol (not yet empirically validated): A short state-gratitude self-report (a few items, 1-7) with internal-consistency reporting (Cronbach's alpha). Complementary coding: count of distinct positives the user names, with rater agreement would be assessed via Cohen's kappa. Could be reported as mean state-gratitude score and named-positives count.
A creative change of direction in the interaction that initially disorients the user but then yields a new and better quality of output. Distinguished from an unproductive tangent by its eventual gain in output quality.
EVENT. One occurrence would be coded when an abrupt directional shift (by user or AI) is first met with a user signal of irritation or confusion and is subsequently followed, within the same session, by a user judgment that the new direction improved the result. The shift-irritation-improvement sequence is the unit counted.
Proposed measurement protocol (not yet empirically validated): Sequential rater coding of three linked features, directional shift, initial irritation/confusion, and later quality endorsement; agreement would be assessed via Cohen's kappa. Complementary objective check: pre-shift vs post-shift output rated for quality by blind judges (ICC for rater consistency). Could be reported as productive-pivot events per session and mean pre/post quality gain.
A deliberate act of resetting emotional overload so the user can re-engage with their own needs. Distinguished from ongoing self-soothing by being a discrete, marked break that re-orients the user after overload.
EVENT. One occurrence would be coded when, following a user signal of emotional overload, the user performs an explicit reset act (a stated break, grounding step, or re-centering prompt) and the next user turn indicates re-orientation toward their own needs or priorities. The overload, reset, re-orientation sequence is the unit counted.
Proposed measurement protocol (not yet empirically validated): Pre/post momentary affect-load self-report (single-item 1-7 'how overwhelmed do you feel') bracketing the reset act; effect quantified as the within-user drop in load. Coders verify an explicit reset act occurred, with agreement would be assessed via Cohen's kappa. Could be reported as mean load reduction per reset and reset-completion rate.
A habitual, recurring division of labor in which a user routinely incorporates an AI system into everyday tasks, such that the pairing becomes a settled part of the daily workflow rather than an occasional consultation.
Graded condition rated from a user's interaction history over a fixed reference period (e.g., 14 days): the proportion of days on which the user initiates at least one AI-assisted task within a defined routine slot. Higher proportions indicate a more established daily pairing.
Proposed measurement protocol (not yet empirically validated): Behavioral-log metric: days-with-AI-session divided by total-days over a 14-day window, optionally cross-validated against a short habit-strength self-report.
A discrete, user-recognized transition during an AI-assisted session from an agitated, problem-focused mode into a calmer, more reflective one, marked at the point where the interaction's tone and pace visibly shift.
One occurrence would be coded when a rater (or the user, in real time) marks the turn boundary at which the interaction changes from rapid problem-pushing to slower, reflective exchange (e.g., a shift to summarizing, reframing, or step-back questions). The marked turn is the unit.
Proposed measurement protocol (not yet empirically validated): Rater-coded transition tagging on session transcripts by two independent coders; inter-rater agreement could be reported as Cohen's kappa; result expressed as the count of marked transitions per session.
Pre- and post-windows each containing >= 6 valid experience-sampling probes.
Participants engaging in unrelated demanding task during post-window.
A self-reported moment of strong felt coherence during interaction, in which prior internal tension eases and the user reports a sense of things fitting together. The construct refers to the user's subjective state, not to any property of the AI.
Graded condition: immediately after a target exchange, the user rates the degree to which they experienced reduced tension and an increased sense of coherence. It is an anchored single-administration rating tied to a specific exchange, not a counted discrete event.
Proposed measurement protocol (not yet empirically validated): Post-exchange self-report on two 1-7 Likert items (tension release; felt coherence), optionally paired with a brief calmness self-report for convergent validity.
A transient elevated-affect appraisal during interaction in which the user weighs perceived possibilities as larger than perceived risks. It denotes the user's momentary optimistic stance toward options surfaced in the exchange.
Graded condition rated for a defined exchange: the user, or a coder using verbal markers, rates perceived-opportunity salience relative to perceived-risk salience. It is operationalized as a balance rating rather than a binary occurrence.
Proposed measurement protocol (not yet empirically validated): Self-report bipolar 1-7 rating (risk-focused to opportunity-focused) for the target exchange; optionally, transcript coding of opportunity- versus risk-laden statements with a ratio reported.
A reformulation move in which a stated problem is recast into one or more actionable options. It denotes the observable act of converting a problem framing into a choice set within the dialogue.
One occurrence would be coded when a turn contains an explicit recasting of a previously stated problem into discrete actionable options (a problem statement in an earlier turn followed by an option-set framing in a later turn). The reformulating turn is the unit.
Proposed measurement protocol (not yet empirically validated): Transcript coding by two raters using a problem-to-options rubric; reliability via Cohen's kappa; result could be reported as a count per N=10 turns.
An act of replacing an overwhelming task specification with a more realistic, smaller-scope alternative during an AI-assisted exchange, observable as a deliberate substitution of the task under discussion.
One occurrence would be coded when a turn replaces a previously requested task with an explicitly narrower or more feasible task (original scope stated earlier, substituted reduced scope adopted in a later turn). The substitution turn is the unit.
Proposed measurement protocol (not yet empirically validated): Rater coding of scope-reduction substitutions on transcripts by two coders; agreement would be assessed via Cohen's kappa; result could be reported as the count of substitution events per session.
A turn in which a user's framing shifts from focusing on what is lacking to acknowledging what is valued, observable as an explicit appreciation statement within the dialogue.
One occurrence would be coded when a user turn contains an explicit appreciation or gratitude statement that reframes a prior deficit-focused statement. The appreciation-containing turn is the unit of count.
Proposed measurement protocol (not yet empirically validated): Transcript coding for gratitude-reframing utterances by two raters; reliability via Cohen's kappa; could be reported as a count per session, optionally validated against a short gratitude self-report at participant level.
A bounded divergent-generation episode in which the dialogue deliberately prioritizes quantity of ideas over quality, with selection and evaluation deferred to a later phase. It denotes an ideation burst rather than an evaluative exchange.
One episode would be coded when a contiguous stretch of turns is led by idea enumeration without evaluation (operationally, a run of turns in which candidate ideas accumulate and no selection or critique turn intervenes). The episode boundaries are the unit.
Proposed measurement protocol (not yet empirically validated): Transcript segmentation into ideation episodes by two coders (kappa for boundary agreement), plus an objective idea-count throughput (distinct candidate ideas per minute) within each episode.
A revision act applied to a text or message with the explicit aim of lowering its perceived pressure, harshness, or demand level while preserving its informational content.
One occurrence would be coded when a turn produces a revised version of prior text whose evident purpose is tone-softening (reduction of imperative, severity, or demand markers) without changing the underlying request. The revision turn is the unit.
Proposed measurement protocol (not yet empirically validated): Pre/post rater rating of revised texts on a 1-7 harshness-and-demand scale (paired comparison) with two raters and ICC for agreement; result could be reported as the mean harshness reduction per edit.
Explicit soften / relax / tone-down request AND NEO-PI-R Neuroticism facet completed.
Edits requested in third-person context (editing someone else's text).
A deliberate restart move in which a conversation or reasoning thread is returned to an earlier point to be re-begun more slowly and explicitly, observable as an intentional back-up and re-entry in the dialogue.
One occurrence would be coded when a turn explicitly references an earlier state of the exchange and re-initiates from there with a slower or more explicit framing (for example, proposing to go back and proceed step by step). The re-initiating turn is the unit.
Proposed measurement protocol (not yet empirically validated): Rater coding of restart and back-up moves on transcripts by two coders; agreement would be assessed via Cohen's kappa; result could be reported as the count of restart events per session.
A brief pause and re-centering taken after an intense stretch of AI-assisted work, allowing attention to recover before the next task. It denotes a short, in-rhythm micro-break distinct from extended rest.
One occurrence would be coded when an interaction gap of a defined short duration (e.g., 1 to 10 minutes) follows an intensive task block and precedes resumption, identifiable from session timestamps as a deliberate within-session pause.
Proposed measurement protocol (not yet empirically validated): Behavioral-log timing: detection of short inter-task gaps (1 to 10 minutes) from session timestamps; count of such micro-breaks per work block, optionally annotated by the user as intentional versus incidental.
Identifiable stress-event AND all three Affect-Grid administrations completed.
Participants who report concurrent pharmacological mood-regulation.
A point in an exchange at which the user registers shared satisfaction because the delivered output, the original intention, and the result align. It denotes a user-marked success acknowledgment within the dialogue.
One occurrence would be coded when a user turn explicitly acknowledges that the output matched the intended goal (an alignment or approval statement immediately following a delivered result). The acknowledging turn is the unit.
Proposed measurement protocol (not yet empirically validated): Rater coding of explicit success-acknowledgment turns following deliverables, by two coders, with Cohen's kappa; could be reported as a count per session, optionally with a convergent single-item satisfaction rating (1-7) at the acknowledgment point.
A brief, low-intensity pleasure a user reports when an AI produces a notably elegant, apt, or aesthetically pleasing formulation. The construct refers to the user's momentary aesthetic-appreciation state, not to any emotion of the system.
Graded condition: immediately after a flagged output, the user rates the intensity of aesthetic pleasure experienced with that formulation. It is tied to a specific output and rated, rather than counted as a discrete behavioral event.
Proposed measurement protocol (not yet empirically validated): Single-item post-output aesthetic-pleasure rating (1-7), optionally combined with items from an aesthetic-experience scale for convergent validity; aggregated as the mean rating across flagged elegant outputs.
FACS AU6+AU12 within 5s of AI-utterance plus verbal participant confirmation that phrasing was the trigger.
Smiles attributable to content-humour rather than phrasing or syntactic structure.
The overall modality by which humans communicate with embodied AI systems through gestures, including hand signals, posture, and movement that the system detects and interprets. It names a communication channel rather than a single bounded event.
Superordinate framework term; not operationalized as a per-interaction event. Specific measurable sub-constructs (for example, gesture-recognition accuracy or repair rate) would be operationalized individually.
Proposed measurement protocol (not yet empirically validated): Not directly measured as a single instrument; superordinate modality term. Component studies may report gesture-recognition accuracy or interpretation error rates, but the channel itself is not one measured construct.
Video-recorded session with at least 20 codable gesture events.
Sessions where gestures are confined to keyboard or device-input only.
A paced verification practice in which a user checks AI output at deliberate intervals rather than monitoring continuously, on the premise that spaced focused review sustains attention quality better than unbroken vigilance across long interactions.
Graded condition characterizing how a user's verification activity is distributed over a session: the regularity and spacing of explicit verification acts. It is operationalized as the cadence or dispersion of verification events rather than as a single occurrence.
Proposed measurement protocol (not yet empirically validated): Behavioral-log analysis of verification-act timestamps within a session: the inter-check interval distribution (mean and coefficient of variation) could be reported as a cadence index, optionally paired with sustained-attention performance over time.
At least 30 sessions to support 10 or more blocks.
Participants with monotonic increase or decrease in verification (no rhythm).
A very short, precise prompt that produces an immediate effect on the user's motivation, focus, or readiness to decide, providing starting impetus rather than substantive depth.
One occurrence would be coded when a user-issued prompt below a defined length threshold (e.g., 12 words or fewer) is followed by a self-reported or behaviorally evident activation effect, such as task resumption within a short window. The short prompt is the unit.
Proposed measurement protocol (not yet empirically validated): Behavioral-log join of prompt length (word or token count) to a post-prompt single-item activation rating (1-7) or to time-to-next-task-action latency; reported per qualifying short prompt.
Interaction duration < 90 seconds AND mood-lift VAS administered post-interaction.
Sessions with multiple interactions within the 60-second post-window.
A brief meta-query that probes the emotional, social, or cultural fit of an output, separate from its factual content. It denotes an explicit request to appraise tone or appropriateness rather than correctness.
One occurrence would be coded when a user turn explicitly requests an appraisal of an output's tone or its social or cultural appropriateness, as opposed to its factual accuracy. The appraisal-requesting turn is the unit.
Proposed measurement protocol (not yet empirically validated): Rater coding distinguishing fit and appropriateness queries from content and accuracy queries on transcripts, by two coders, with Cohen's kappa; could be reported as the count of fit-queries per session.
A light creative prompt or suggestion offered without any obligation to act on it, functioning as an optional spark rather than a directive within the exchange.
One occurrence would be coded when an AI turn offers a creative suggestion explicitly framed as optional or non-binding, with no implied requirement to implement it. The optional-suggestion turn is the unit.
Proposed measurement protocol (not yet empirically validated): Rater coding of optional versus directive creative suggestions on transcripts, by two coders, with Cohen's kappa; could be reported as the count of optional creative prompts per session.
A brief, precise intervention that brings an output back on course without interrupting the overall working flow, observable as a short steering correction inserted mid-task.
One occurrence would be coded when a user turn issues a short corrective instruction that adjusts the output's direction and is immediately followed by continued task progress, with no full restart. The corrective turn is the unit.
Proposed measurement protocol (not yet empirically validated): Rater coding of short mid-task corrections (distinguished from restarts and abandonments) on transcripts, by two coders, with Cohen's kappa; result could be reported as a count per N=10 turns.
A condition of low-friction collaboration in which an AI-assisted task proceeds with few corrections, clarifications, or breakdowns. It denotes a graded smoothness of the working exchange rather than a single moment.
Graded condition over a task or session, inversely indexed by the rate of repair turns (corrections, clarification requests, restarts). Lower repair rates indicate a smoother condition; it is rated or computed across the segment, not counted as one event.
Proposed measurement protocol (not yet empirically validated): Behavioral metric: repair-turn rate equal to (corrections plus clarification requests plus restarts) divided by total turns over the segment, optionally complemented by a post-task 1-7 perceived-smoothness self-report.
A single, concrete point of focus that makes gratitude specific and tangible, observable as one explicitly named object of appreciation within the exchange.
One occurrence would be coded when a turn names a single concrete object of gratitude, that is, one specific item rather than a general expression of thanks. The turn naming the specific focus is the unit.
Proposed measurement protocol (not yet empirically validated): Rater coding distinguishing specific, concrete gratitude foci from generic thanks on transcripts, by two coders, with Cohen's kappa; could be reported as the count of concrete gratitude foci per session.
A collaborative, iterative search for the single formulation that precisely fits meaning, tone, and context, defined by repeated refinement of candidate wordings until convergence on one expression.
One episode would be coded when a contiguous run of turns iteratively refines a target phrasing (two or more successive candidate reformulations aimed at the same expression slot) before settling. The refinement run is the unit.
Proposed measurement protocol (not yet empirically validated): Transcript segmentation of phrasing-refinement episodes by two coders (kappa for boundary agreement); within each episode, the count of candidate reformulations until convergence could be reported as iteration depth.
At least 30 hunted-words AND completed working-memory assessment at both timepoints.
Participants whose hunt-targets duplicate existing vocabulary in baseline assessment.
A brief AI-guided breathing or attention exercise intended to lower stress or ease a transition between cognitive states, observable as a discrete guided micro-intervention within the session.
One occurrence would be coded when an AI turn delivers a structured short breathing or attention-direction instruction (a guided exercise prompt) within the interaction. The guidance turn is the unit.
Proposed measurement protocol (not yet empirically validated): Rater coding of guided breathing and attention-exercise turns on transcripts (count per session), with agreement would be assessed via Cohen's kappa; optionally a pre/post single-item stress rating (1-7) to index the effect.
Concurrent respiratory and ECG recording for RSA computation across pre/post windows.
Participants with documented respiratory disease.
An emotionally supportive interaction style that conveys safety, belonging, and acceptance without forcing problem-solving. It denotes a graded stylistic quality of the exchange as perceived by the user.
Graded condition: raters or the user rate the degree to which an exchange conveys warmth, acceptance, and non-pressuring support, on a defined scale across the segment. It is rated as an intensity, not counted as a discrete event.
Proposed measurement protocol (not yet empirically validated): Rater coding of supportive and warmth style on a 1-5 anchored scale (two raters, ICC for agreement), optionally complemented by a user-perceived-warmth self-report (1-7) per session.
All three post-interaction warm-affect probes completed AND Schwartz Values Survey within 30 days.
Participants reporting concurrent mood-altering substances within the warmth-window.
An added emotional or linguistic layer that buffers harshness and makes communication more tolerable without distorting the underlying content. It denotes a graded softening property of a message relative to its plain form.
Graded condition assessed per message: the degree of tone-buffering present relative to a neutral baseline, while content equivalence is held constant. It is rated as buffering intensity with a content-preservation check, not counted as an event.
Proposed measurement protocol (not yet empirically validated): Paired rater rating of harshness or softening on a 1-7 scale (two raters, ICC), plus a separate content-equivalence verification step checking that the buffered version preserves the information; could be reported as a softening delta with content held constant.
Output-text >= 200 words with identifiable critical content AND tolerance-of-ambiguity completed.
Outputs explicitly requested in blunt-mode by user.
A minimal reminder or correction that provides orientation without exerting pressure, observable as a brief, low-intensity prompt that redirects without using demand language.
One occurrence would be coded when a turn issues a short reminder or correction that lacks imperative or pressure markers and offers light redirection (a low-intensity steering cue). The low-pressure cue turn is the unit.
Proposed measurement protocol (not yet empirically validated): Rater coding of low-pressure reminder and correction turns, scored for the absence of demand markers, on transcripts, by two coders, with Cohen's kappa; could be reported as a count per session.
A user's working stance toward errors in which mistakes are treated as ordinary, expected by-products of productive work rather than as non-successes, lowering the threshold for surfacing and correcting them during AI-assisted tasks.
STATE. The user's error-orientation during a task block is rated on whether errors are surfaced without avoidance and treated as routine: rated on a graded scale from defensive/error-avoidant to matter-of-fact/error-tolerant, anchored to observable handling of the session's mistakes (e.g., openly naming an error and continuing vs. concealing or abandoning).
Proposed measurement protocol (not yet empirically validated): A short error-tolerance self-report administered post-session (items covering learning-from-errors and error-competence, positively keyed, together with error-strain and covering-up-errors, reverse keyed), on a 7-point Likert format; could be reported as a composite error-tolerance score with internal consistency (Cronbach's alpha).
A discrete resumption in which a user returns to a previously interrupted or misunderstood topic without visible tension or self-reproach, picking the thread back up smoothly within an AI dialogue.
EVENT. One occurrence is coded when, after an interruption or a flagged misunderstanding, the user re-opens the same topic in a later turn and the re-entry turn contains no markers of frustration or blame (no self-criticism, no complaint about the prior break). The re-entry turn is the countable marker.
Proposed measurement protocol (not yet empirically validated): Rater coding of transcript turns: two trained coders flag topic re-entries and judge presence/absence of tension markers; inter-rater reliability via Cohen's kappa; could be reported as count of low-tension re-entries per session.
A graded condition in which a user incorporates adjustments, corrections, or scope changes during a task while sustaining task momentum, so that revisions do not break the ongoing work rhythm of an AI-assisted session.
STATE. Rated as the degree to which adjustments are absorbed without loss of momentum across a task block: scored from low (each change causes a stall or restart) to high (changes are integrated with continuous progress), anchored to observable indicators such as continued forward task moves immediately after a correction.
Proposed measurement protocol (not yet empirically validated): Behavioral process metric: from session logs, compute median post-adjustment resumption latency (seconds from a correction/scope-change to the next on-task action) and the proportion of adjustments followed by continued progress without restart; could be reported as a continuous index, not a count.
A deliberate corrective move in which a user redirects a conversation that has wandered off back toward its core goal, re-anchoring the exchange to the original task within an AI dialogue.
EVENT. One occurrence is coded when a user turn explicitly references the original goal or task after the immediately preceding turns have moved onto a side topic, and reorients the exchange back to it (e.g., 'let's get back to X'). The redirecting turn is the countable marker.
Proposed measurement protocol (not yet empirically validated): Rater coding with a topic-tracking scheme: coders mark off-topic drift segments and the user turn that closes them; count redirect events per session and report drift-segment duration before each redirect; inter-rater agreement would be assessed via Cohen's kappa.
A rapid affective recovery in which a user's negative reaction following a unsuccessful attempt subsides quickly and the user re-engages with the task within an AI-assisted session.
EVENT. One occurrence is coded when a turn expressing negative affect after a unsuccessful attempt is followed within a short window (e.g., the next 1-2 user turns) by a turn showing neutral or positive affect and renewed task engagement. The recovery turn is the countable marker; recovery time (turns or seconds) is recorded.
Proposed measurement protocol (not yet empirically validated): Affect-trajectory coding: trained coders rate per-turn affect valence (negative/neutral/positive) around failure events; recovery would be operationalised as return to non-negative valence; report median recovery latency and event count; inter-rater reliability via weighted Cohen's kappa.
A brief mood lift produced by an AI exchange that raises a user's momentary positive affect without causing distraction or exaggerated elation, leaving task focus intact.
EVENT. One occurrence is coded when a short within-session rise in self-reported or observably expressed positive affect follows a specific AI turn, and the following turns show no drop in on-task behavior. The affect-rise tied to that turn is the countable marker.
Proposed measurement protocol (not yet empirically validated): Momentary affect probe: single-item positive-affect rating (0-100 visual analog scale) sampled immediately before and after the triggering exchange; the pre-post difference indexes the lift; aggregated as mean within-person change with a paired test, paired with a task-focus check (on-task action present in the next turn).
A small, everyday AI intervention whose practical usefulness is disproportionately large relative to the minimal effort or input it required, observed in routine tasks.
EVENT. One occurrence is coded when a low-effort request (short prompt, single turn) yields an output the user marks as highly useful or time-saving, producing a high benefit-to-effort contrast for that exchange. The user's usefulness acknowledgement on a low-effort turn is the countable marker.
Proposed measurement protocol (not yet empirically validated): Benefit-to-effort rating per flagged exchange: user rates perceived usefulness (1-7) and input effort (1-7); the event is indexed by a high-usefulness/low-effort combination; report the proportion of low-effort exchanges rated highly useful, with optional time-saved estimate in minutes.
A graded mode of AI use in which the system functions as an unobtrusive support to the user's work rhythm, contributing when prompted without overriding or steering the activity.
STATE. Rated across a session as the degree of unobtrusiveness: scored from intrusive (AI introduces unrequested redirections that interrupt the user's flow) to unobtrusive (AI contributions stay within the user's initiative), anchored to the ratio of user-initiated to AI-initiated topic shifts.
Proposed measurement protocol (not yet empirically validated): Initiative-balance index from transcript coding: count user-initiated vs. AI-initiated topic shifts and unsolicited redirections; report AI-initiation share (AI-initiated / total shifts) as a continuous unobtrusiveness index; inter-rater agreement would be assessed via Cohen's kappa on the initiation labels.
A stable condition of inner calm during or after AI-assisted work in which the user experiences no pressure to further optimize, accepting the current result as sufficient.
STATE. Rated as the level of felt calm and absence of optimization pressure over a defined period: scored on a graded scale from high optimization pressure (continued reworking, dissatisfaction) to settled sufficiency (work accepted as good enough), based on self-report and observable cessation of further reworking.
Proposed measurement protocol (not yet empirically validated): A brief calmness/arousal self-report: a short calmness/low-tension subscale covering the calm-tense (low-arousal) dimension (calm-vs-tense items, reverse-scored for calm); 5-point Likert, administered immediately after the work block; could be reported as a mean state-calm score with internal consistency (Cronbach's alpha).
A positive emotional closure at the end of a work day, supported by an AI exchange, that rounds off the day's activity and helps the user consolidate or integrate what was done.
EVENT. One occurrence is coded when an end-of-day session concludes with a user-expressed sense of completion or positive closure and an explicit recap/integration of the day's work. The closing turn containing both positive closure and recap is the countable marker (at most one per day).
Proposed measurement protocol (not yet empirically validated): End-of-day diary entry: single daily item rating sense of positive closure (1-7) plus a checkbox for whether the session included a recap/integration; aggregated across days as the proportion of days with positive closure and mean closure rating; suitable for multilevel analysis over a diary window.
A deliberate injection of unstructured or disordered input intended to break out of an entrenched line of thinking; structure is reintroduced only in a subsequent step after the fixation is loosened.
EVENT. One occurrence is coded when a user intentionally supplies deliberately unstructured input (free association, scattered fragments, randomized prompts) explicitly to escape a stuck line of reasoning, followed in a later turn by a move to re-impose structure. The intentional unstructured-input turn is the countable marker.
Proposed measurement protocol (not yet empirically validated): Rater coding of prompt structure: coders classify user turns as structured vs. deliberately unstructured and mark stated de-fixation intent and a later restructuring turn; count qualifying sequences per session; inter-rater agreement would be assessed via Cohen's kappa on the structure labels.
An activating impulse from an AI exchange that releases energy for action without overwhelming the user; it is brief, clear, and oriented toward taking the next step.
EVENT. One occurrence is coded when a short AI turn is followed within the next 1-2 user turns by an observable initiation of action (the user starts or commits to a concrete next step) together with self-reported readiness to act, and no markers of overwhelm. The action-initiation following the impulse is the countable marker.
Proposed measurement protocol (not yet empirically validated): Pre/post readiness probe plus behavioral follow-through: single-item action-readiness rating (1-7) before and after the impulse, combined with a log check for whether a concrete next step was initiated within the following turns; report mean readiness change and follow-through rate.
A clear restart undertaken when a user's thinking or the dialogue has become tangled, in which the user breaks off the snarled line and re-opens the task from a fresh starting point.
EVENT. One occurrence is coded when, following turns showing confusion or a tangled exchange (repair attempts, contradictions, circularity), a user turn explicitly abandons the current line and restarts (e.g., 'let's start over'). The explicit restart turn is the countable marker.
Proposed measurement protocol (not yet empirically validated): Rater coding of restart events: coders flag preceding tangle indicators and the explicit restart turn that resets the task; count restarts per session and record the length of the tangled stretch preceding each; inter-rater agreement would be assessed via Cohen's kappa.
A structuring, positive start to the day created by a short AI interaction that helps a user organize and orient the day's tasks at its outset.
EVENT. One occurrence is coded when a brief AI session early in the user's day produces an explicit plan or ordered task list for that day and the user reports a positive, organized start. The morning planning turn that yields an ordered day-structure is the countable marker (at most one per day).
Proposed measurement protocol (not yet empirically validated): Morning diary entry: daily items for whether the session produced a concrete day-plan (checkbox) and a 1-7 rating of feeling organized/positively oriented at the start; aggregated across days as proportion of structured mornings and mean orientation rating over the diary window.
The aftereffect of an intensive AI session in which a user later carries over formulations, thinking structures, or argumentation patterns from the dialogue into their everyday communication, often without awareness of the source.
EVENT. One occurrence is coded when a distinctive formulation, frame, or argument structure introduced during an AI session reappears in the same user's later, non-AI communication (speech or writing) within a defined follow-up window. The matched carry-over instance in post-session communication is the countable marker.
Proposed measurement protocol (not yet empirically validated): Matched-feature transfer coding: build a per-session lexicon/structure list of distinctive AI-introduced features, then have raters scan later user-authored samples for matches; count carry-over instances per follow-up window; inter-rater agreement would be assessed via Cohen's kappa on match judgments.
Idle interval between 12 and 72 hours with both prior conversation and subsequent solo task available.
Solo tasks where participant explicitly re-reads prior conversation during the task.
A longitudinal shift of the remembering function from retaining content itself toward retaining where and how the content can be retrieved through AI, reflecting a change in how a person manages personal knowledge under sustained AI use.
TREND. Tracked over repeated measurements as the change in the balance between content recall and retrieval-path recall: at each timepoint, record the share of queried items for which the person recalls the substantive content versus only the location/route to retrieve it; the trend is the change in that share across the observation window.
Proposed measurement protocol (not yet empirically validated): Repeated cued-recall protocol: at multiple timepoints, probe a fixed item set and classify each response as content-recall vs. location/route-recall; track the location-recall proportion over time as the longitudinal index; report the trajectory (e.g., slope across sessions) rather than a single-session count.
At least 10 deictic markers per session with verifiable transcript anchors.
Deictic uses lacking identifiable referent in the transcript.
The moment a user encounters the limits of an AI system: the system cannot solve the task, returns incorrect information, or exposes a comprehension boundary, giving the user direct evidence of what the system cannot do.
EVENT. One occurrence is coded when, within an exchange, the AI fails on the user's request in an observable way (explicit inability, a detectable error, or a stated comprehension limit) and the user registers this limit in a subsequent turn. The user's recognition turn following the AI limit is the countable marker.
Proposed measurement protocol (not yet empirically validated): Failure-event rater coding: coders annotate AI-limit events (refusal/inability, factual error, comprehension limit) and the user turn that registers them; count limit-recognition events per session and categorize by limit type; inter-rater agreement would be assessed via Cohen's kappa on event type.
Pre-check and post-check feasibility ratings both completed.
Sessions without an identifiable constraint-enumeration phase.
An abrupt transition from diffuse uncertainty to a clearly structured mental representation, frequently prompted by a precise restatement of the user's own thoughts by the AI.
EVENT. One occurrence is coded when a user turn marks a sudden gain in clarity (e.g., an explicit 'now it makes sense' / 'that's exactly it') in the turn immediately following an AI restatement or reframing of the user's input. The user's clarity-acknowledgement turn is the countable marker.
Proposed measurement protocol (not yet empirically validated): Cued retrospective rating plus marker coding: raters flag clarity-acknowledgement turns following an AI reframing (inter-rater agreement would be assessed via Cohen's kappa), and the user gives a single-item insight-suddenness rating (1-7) for flagged moments; report event count per session and mean suddenness.
Click-marker AND WCST administered within 30 minutes of session.
Click claims without downstream understanding-demonstration.
A gentle re-connection to a topic after overload, distraction, or a break, in which the user resumes work without self-blame or pressure, easing back into the task.
EVENT. One occurrence is coded when, after a gap caused by overload/distraction/abandonment, the user re-engages the prior topic in a measured, low-pressure way, and the re-entry turn contains no self-blame or urgency markers. The gentle re-entry turn is the countable marker.
Proposed measurement protocol (not yet empirically validated): Rater coding of resumptions with a pressure-marker checklist: coders flag topic resumptions after gaps and score self-blame/urgency markers (absent = relaxed); count low-pressure resumptions per session and record gap length; inter-rater agreement would be assessed via Cohen's kappa.
A small, friendly affirmation from the AI that the user reports as a brief lift in energy or motivation, without creating performance pressure or implying an obligation to perform.
EVENT. One occurrence is coded when a brief affirmative AI turn is followed by a user-reported small rise in energy/motivation, with no accompanying increase in felt performance pressure. The reported energy rise tied to the affirmation is the countable marker.
Proposed measurement protocol (not yet empirically validated): Paired single-item probes around the affirmation: user rates momentary energy/vigor (1-7) and felt performance pressure (1-7) before and after; the event requires an energy increase without a pressure increase; report the joint pre-post change pattern across instances.
A reframing that shifts the user's perspective toward opportunities and possible courses of action without denying or minimizing the existing problems.
EVENT. One occurrence is coded when, after an exchange, the user's framing of a situation shifts from problem-focused to opportunity/action-focused while still acknowledging the problem (problems are not denied). The user turn exhibiting the opportunity-oriented reframing is the countable marker.
Proposed measurement protocol (not yet empirically validated): Pre/post framing classification: raters code the user's stance on the focal issue before and after as problem-focused vs. opportunity/action-focused (with a check that problems remain acknowledged); the event is a problem->opportunity shift; report shift frequency and inter-rater agreement would be assessed via Cohen's kappa.
A mental turn from frustration toward constructive openness, in which a user moves from a blocked, irritated stance to a receptive, solution-oriented one during an AI exchange.
EVENT. One occurrence is coded when a turn expressing frustration is followed within the same exchange by a turn showing constructive, open engagement (proposing or accepting a way forward). The transition into the constructive turn is the countable marker.
Proposed measurement protocol (not yet empirically validated): Stance-transition coding: raters label turns on a frustration-to-openness dimension and mark transitions from frustrated to constructively open; count qualifying transitions per session; inter-rater agreement would be assessed via Cohen's kappa on stance labels.
The scaling-down of overreaching ideas into realistic, implementable steps, in which an AI exchange helps a user convert an oversized aim into a feasible sequence of actions.
EVENT. One occurrence is coded when an initially overscoped goal stated by the user is, after the exchange, reformulated by the user into a smaller set of concrete, feasible steps. The user turn presenting the down-scoped, actionable plan is the countable marker.
Proposed measurement protocol (not yet empirically validated): Scope-change coding: raters record the initial scope (e.g., number/size of claimed deliverables) and the post-exchange scope (concrete next steps), and judge whether the reformulation is more feasible; report the proportion of overscoped goals that are down-scoped and inter-rater agreement would be assessed via Cohen's kappa.
A transition from tension to ease brought about by small positive cues in an AI exchange, in which the user's strained state gives way to a lighter, more relaxed one.
EVENT. One occurrence is coded when a turn showing tension or strain is followed, after a small positive cue from the AI, by a turn showing reduced strain and greater ease. The transition into the eased turn is the countable marker.
Proposed measurement protocol (not yet empirically validated): Tension-to-ease coding: raters rate per-turn strain/ease (e.g., tense vs. relaxed expression markers) and mark transitions from strained to eased following a positive cue; count transitions per session; inter-rater agreement would be assessed via weighted Cohen's kappa on the ordered strain ratings.
The targeted reduction of a user's felt heaviness through linguistic or perspective-based relief, in which an AI exchange deliberately reframes or lightens the framing of a burdensome matter.
EVENT. One occurrence is coded when an AI exchange applies a deliberate relief move (reframing, lightening language, perspective change) and the user subsequently reports reduced felt heaviness/burden about the focal matter. The user's reduced-burden report following the relief move is the countable marker.
Proposed measurement protocol (not yet empirically validated): Pre/post burden rating: user rates felt heaviness/burden about the focal matter (1-7) before and after the relief move; the event requires a meaningful decrease; report mean within-person burden reduction with a paired test, paired with rater identification of the relief move (kappa for the move classification).
A longitudinal effect in which regular AI interaction is associated with improvement in a user's ability to recognize patterns in data, texts, or arguments, such that sustained use coincides with gains in the person's own analytic pattern detection.
TREND. Tracked over repeated measurements as the change in pattern-recognition performance: administer a standardized pattern-detection task at multiple timepoints over an AI-use period and track the change in accuracy/speed across the window; the trend (improvement trajectory) is the referent, not a single in-session event.
Proposed measurement protocol (not yet empirically validated): Repeated standardized assessment: a validated pattern/inductive-reasoning instrument (e.g., matrix-reasoning or series-completion items) administered at baseline and follow-ups; track accuracy and response time over time; analyze the trajectory (e.g., slope) ideally against a comparison condition to support the association rather than infer causation.
At least 8 successive pattern-articulations with measurable feature-variance decrease.
Sessions where the pattern is reframed mid-stream (topic-shift breaking the autocorrelation series).
A graded user disposition in which the degree of acceptance granted to an AI output is scaled to its stakes: high-consequence outputs receive more independent verification, routine outputs receive proportionally lighter checking. Trust varies by context rather than being uniformly extended or withheld.
STATE. Two trained raters score, for each AI output a user acts upon, (a) the stakes of the output on a 3-point scale (low/medium/high consequence of error) and (b) the observed verification effort the user applied before acting (none / light cross-check / independent confirmation). Calibrated trust is operationalised as the rank-order alignment between stakes and verification effort across a session; higher alignment indicates greater calibration.
Proposed measurement protocol (not yet empirically validated): Rater-coded verification-effort vs. stakes alignment per acted-upon output; calibration quantified as the Goodman-Kruskal gamma rank correlation between the stakes scale and the effort scale within a participant. Inter-rater reliability for both coded dimensions could be reported as weighted Cohen's kappa on a held-out 20% double-coded subset; participant-level self-report supplement via a 5-point Likert item ('I checked this more carefully because it mattered more').
At least 50 trust-rated outputs with verifiable ground-truth.
Outputs lacking objective accuracy criterion.
A discrete user judgement that a given piece of content was produced by an AI system, inferred from stylistic regularities, characteristic phrasing, or content-level cues. The referent is the act of attribution, not the correctness of that attribution.
EVENT. One occurrence is recorded each time a user explicitly attributes a presented artefact (text, image, or audio) to AI generation. The observable marker is a verbal or logged attribution judgement ('this is AI-generated'). When ground truth of the artefact is known, each judgement is scored hit / miss / false alarm / correct rejection.
Proposed measurement protocol (not yet empirically validated): Signal-detection task: participants classify a balanced stimulus set of AI-generated and human-authored artefacts. Sensitivity could be reported as d-prime and response bias as criterion c, computed from hit and false-alarm rates; per-stimulus confidence captured for an ROC curve. Accuracy contrasted against a 50% chance baseline with a binomial test.
At least 50 items spanning at least 3 content-domains.
Items where stylistic AI-fingerprints are explicitly disclosed.
A deliberately maintained boundary - spatial, temporal, or topical - within which a person excludes AI assistance. The referent is a standing, self-imposed restriction on the scope of AI use rather than a one-off refusal.
STATE. Coded as present when a participant names at least one explicit, durable exclusion boundary and the conditions under which it applies (e.g. 'no AI during family meals', 'no AI for first-draft poetry', 'no AI in the bedroom'). Each declared boundary is catalogued by type (spatial / temporal / topical) and by stated scope; the state is graded by the count and breadth of maintained boundaries.
Proposed measurement protocol (not yet empirically validated): Structured boundary inventory: a semi-structured interview enumerates declared exclusion zones, each verified for durability via a one-week experience-sampling log of adherence (proportion of qualifying occasions on which AI was in fact withheld). Could be reported as number of zones, zone-type distribution, and mean adherence rate; coder agreement on zone-type classification via Cohen's kappa.
Explicit zone-declaration AND at least 50 observation-points spanning in-zone and out-of-zone contexts.
Zones declared but not observable in available logs.
A use episode in which a person enlists an AI system to generate unexpected narrative turns or alternative storylines for a creative or persuasive artefact such as a story, pitch, or argument. The referent is the request-and-use episode, not a property of the AI.
EVENT. One occurrence is logged when a user issues a request whose explicit aim is to obtain a divergent or unexpected continuation of an existing narrative or argument (e.g. 'give me a surprising twist', 'what is an unexpected counter-move'). The observable marker is a divergence-seeking instruction directed at narrative or argumentative material.
Proposed measurement protocol (not yet empirically validated): Task-log content coding: prompts in a creative-writing corpus are labelled for divergence-seeking intent by two raters; rate could be reported as divergence requests per 100 task turns. Downstream uptake measured as the proportion of AI-proposed twists incorporated into the user's subsequent draft (acceptance rate); coder reliability via Cohen's kappa on the intent label.
Surprisal > 95th-percentile AI-turn followed by positive evaluation marker within 2 turns.
High-surprisal turns triggered by user topic-shift rather than AI-initiated reframing.
A use episode in which a person queries an AI system for information about animal care - feeding, behaviour, preventive health, or species-appropriate husbandry. The referent is a topically defined everyday information request.
EVENT. One occurrence is recorded when a user query is classified as seeking animal-care information, identified by topical markers (species terms together with care intents such as feeding, health signs, behaviour, or housing). Each qualifying query counts as one instance.
Proposed measurement protocol (not yet empirically validated): Query-topic classification: a held-out sample of user queries is labelled against an animal-care topic taxonomy by two annotators; prevalence could be reported as the proportion of queries in this topic class and as queries per active user per month. Annotation reliability via Cohen's kappa; optional automated classifier validated against the human labels (precision/recall, F1).
At least 30 queries per participant within the observation window.
Single-instance queries with no recurrence.
A reversion episode in which a person returns to a previously used AI workflow after a newer alternative did not deliver the intended result. The referent is the act of reverting, reflecting that an established procedure outperformed a newer one for that task.
EVENT. One occurrence is recorded when a user, having adopted a newer tool, model, or procedure for a recurring task, switches back to an earlier one they had previously used for the same task. The observable marker is a documented workflow transition from a newer to an older procedure for an unchanged task type.
Proposed measurement protocol (not yet empirically validated): Workflow-version transition log: for a panel of users, tool/model/procedure choices per recurring task are timestamped, yielding a directed sequence of transitions. Reversions are counted as backward transitions to a prior procedure; could be reported as reversion rate per adopted change and median tenure of the new procedure before reversion (a median time-to-event, survival-style, analysis).
Participant with at least 30 days of historical sessions available.
Edges based on common-phrase or boilerplate matches.
A use episode in which an AI system surfaces a perspective on a familiar topic that the user reports not having considered before. The referent is the discovery event of a previously unconsidered viewpoint during interaction.
EVENT. One occurrence is recorded when (a) an AI output introduces a perspective absent from the user's prior framing of the topic and (b) the user acknowledges it as new to them. The observable markers are a rater-identified perspective shift in the output plus a user confirmation of novelty.
Proposed measurement protocol (not yet empirically validated): Two-source corroboration: trained raters code each output for introduction of a perspective not present in the preceding user turns (presence/absence), and users rate each flagged output on a 5-point novelty item ('this viewpoint had not occurred to me'). An instance requires both signals; could be reported as novel-perspective episodes per session. Rater reliability via Cohen's kappa; convergence of rater flag and user rating via phi coefficient.
Sessions with serial perspective-listing across at least 5 timepoints.
Sessions where the AI provides angles unprompted without explicit user-acknowledgment.
A discrete recognition act in which a user identifies an AI response as substantively repeating an earlier response - reworded but identical in core content. The referent is the user's recognition of redundancy, not the redundancy itself.
EVENT. One occurrence is recorded when a user signals that a current response duplicates the substance of a prior one in the same conversation (e.g. an explicit complaint, a 'you already said this' turn, or a logged flag). The observable marker is a user-issued redundancy signal referring to an earlier turn.
Proposed measurement protocol (not yet empirically validated): Redundancy-flag detection corroborated by semantic similarity: user redundancy signals are extracted from transcripts; for each, the cosine similarity between the flagged response and the referenced earlier response would be computed from sentence embeddings to confirm genuine near-duplication above a calibrated threshold. Could be reported as redundancy notices per 100 turns and precision of user flags against the similarity criterion; human coding of flags reliability via Cohen's kappa.
Explicit duplicate-marker phrase AND identifiable referenced prior output.
Loose similarity claims without explicit prior-output reference.
A prompt in which the user frames the request through an explicit social role - for example 'as a parent I need...' or 'in my capacity as...'. The referent is the role-marked formulation of an individual input.
EVENT. One occurrence is recorded when a prompt contains an explicit self-referential role marker that contextualises the request (a stated role noun phrase tied to the user, such as parent, teacher, manager, caregiver). The observable marker is the presence of such a role-framing clause in the prompt text.
Proposed measurement protocol (not yet empirically validated): Prompt-text annotation: a corpus of prompts would be coded for presence and type of explicit user-role markers by two annotators; prevalence could be reported as proportion of role-marked prompts and rate per 100 prompts, with a role-category distribution. Annotator agreement would be assessed via Cohen's kappa; optional regex/NER detector validated against human labels (precision/recall, F1).
Participant uses at least 3 distinct AI-roles within the observation window.
Sessions where role-assignment is implicit only (no explicit role-string).
The degree to which structural features of a user's first language - word order, punctuation conventions, and phrasing patterns - remain detectable in their written AI inputs composed in a second language. The referent is the relative strength of cross-linguistic transfer in writing.
RATIO. Quantified as the relative frequency of first-language-typical structural features in a user's second-language prompts compared with a same-language native-writer baseline. Numerator: count of L1-transfer features observed in the user's prompts (e.g. divergent word-order patterns, calqued collocations, L1 punctuation conventions); denominator: total scored constructions, normalised against the native-baseline feature rate.
Proposed measurement protocol (not yet empirically validated): Contrastive linguistic feature scoring: prompts are parsed and tagged for a predefined inventory of L1-transfer markers; a transfer index would be computed as the standardised difference between the user's marker rate and a matched native-writer corpus baseline. Could be reported as effect size (Cohen's d) of marker rate against baseline; classifier predicting writer L1 from prompt features evaluated by AUC. Tagging reliability via Cohen's kappa.
At least 5000 words of prior solo writing for stable stylometric baseline.
Participants who explicitly request style-imitation of a different author.
An occurrence in a multi-agent system of a coordination pattern that was not explicitly specified, arising from the interaction of components rather than from any single component's design. The referent is the observed unspecified coordinated behaviour.
EVENT. One occurrence is recorded when system logs show a stable coordinated behaviour among agents (e.g. role division, turn-taking, convergence on a shared plan) that is not entailed by any individual agent's specification or by explicit coordination rules. The observable marker is a recurring inter-agent pattern absent from the design specification.
Proposed measurement protocol (not yet empirically validated): Specification-versus-behaviour log analysis: interaction traces are mined for recurrent multi-agent patterns; each candidate is checked against the documented specification and ablated to confirm it disappears when interaction is removed. Could be reported as count of specification-absent coordinated patterns per run and their stability (recurrence rate across seeds); two analysts independently adjudicate 'unspecified' status with agreement would be assessed via Cohen's kappa.
Multi-agent session with at least 3 participants and >= 15 sub-tasks.
Sessions with explicit pre-assigned task-segmentation.
A superordinate framework term for the deliberate, productive collaboration between humans and AI in everyday contexts, viewed from the human side. It blends 'augmentation' with a coined 'humanitai' root and, unlike 'artificial intelligence', foregrounds the human's experience and agency rather than the machine.
FRAMEWORK. Superordinate framework term; not operationalized as a per-interaction event. It names the overarching subject matter under which the lexicon's countable phenomena are organised and is not itself directly counted.
Proposed measurement protocol (not yet empirically validated): Not directly measured; superordinate term. Its scope is articulated through the constituent constructs of the framework, each of which carries its own instrument.
Retention measured at all four time-points with co-produced half-life at least 1.5x solo-produced half-life.
Concepts re-encountered between testing windows through external exposure (publications, news) that confound retention-attribution.
A user mode in which a person no longer treats AI as a separate tool but moves fluidly between their own thinking and AI assistance without a distinct switching moment. The referent is an advanced, relatively stable working state characterised by low perceived separation between human and AI activity.
STATE. Assessed as a graded user-level condition combining (a) behavioural fluidity - short and infrequent deliberate transitions between unaided work and AI use - and (b) low self-reported sense of 'switching'. A participant is placed on the integration continuum from these indicators rather than by counting discrete events.
Proposed measurement protocol (not yet empirically validated): Mixed behavioural-and-self-report profiling: inter-action transition latency and frequency are derived from interaction logs, and integration is rated via a validated multi-item self-report scale (e.g. a tool-incorporation/flow inventory administered on a 5-point Likert metric). A composite integration score combines standardised log and survey indicators; internal consistency could be reported as Cronbach's alpha and the behaviour-survey convergence as a Pearson correlation.
Both telemetry and think-aloud data captured for the same task with consistent integration signals.
Tasks where AI is consciously bracketed as a separate tool (explicit pause-consult-resume pattern).
An output in which an AI-assisted user integrates knowledge from several distinct disciplines into a single interdisciplinary result. The referent is an instance of cross-domain synthesis produced with on-demand access to domain knowledge.
EVENT. One occurrence is recorded when a user-produced output combines content from two or more distinct knowledge domains into a connected result. The observable marker is a single artefact that draws identifiable, separable contributions from multiple disciplines and links them rather than juxtaposing them.
Proposed measurement protocol (not yet empirically validated): Rater coding of cross-domain integration: each output would be annotated for the number of distinct source domains present and the depth of integration on an ordinal scale (juxtaposition / partial linkage / synthesised novelty). Could be reported as the count of multi-domain syntheses per output set and the domain-count distribution; inter-rater reliability on the integration-depth scale via intraclass correlation (ICC) and on domain identification via Cohen's kappa.
Mesh-density above 0.3 with largest-connected-component ratio above 0.8 across at least 50 transcript segments.
Single-category dominant tasks where mesh-density is artificially inflated by repeat-tagging of one category.
An episode in which a user, after an affirming AI interaction, becomes more willing to advance an idea to other people. The referent is the subsequent real-world advocacy act, with the prior AI exchange serving as an initial sounding board.
EVENT. One occurrence is recorded when a user reports or is observed to advance an idea to a human audience (raising it in a meeting, sending it, defending it) after having first tested that idea in an affirming AI exchange. The observable marker is a downstream advocacy action temporally preceded by a confirming AI interaction on the same idea.
Proposed measurement protocol (not yet empirically validated): Paired before/after self-report with optional behavioural follow-up: participants rate intention to advocate an idea before and after an AI exchange on a 5-point scale, and report actual advocacy at a later follow-up. Effect estimated as the within-subject change in advocacy intention (paired t-test or Wilcoxon signed-rank) and the conversion rate from intention to reported action; an AI-affirmation versus neutral-exchange contrast controls for mere rehearsal.
At least one tentative statement followed by committed restatement after AI-acknowledgment.
Statements where AI provides new information rather than acknowledgment.
The relationship in which the effort needed to produce a result falls substantially under AI assistance while the quantity or quality of the result is maintained or increased. The referent is the changed ratio of input effort to output yield.
RATIO. Numerator: a measure of output yield (validated units produced, or an independently rated quality score). Denominator: a measure of input cost (active user time, edits, or token/turn effort) for the same task. Asymmetry is the ratio of yield to cost compared between AI-assisted and unaided conditions for matched tasks.
Proposed measurement protocol (not yet empirically validated): Within-subject controlled comparison: participants complete matched tasks with and without AI; input cost (logged active time and revision count) and output yield (deliverable count plus blind expert quality ratings on a fixed rubric) are recorded. Asymmetry could be reported as the yield-per-unit-cost ratio and its assisted/unaided difference (paired comparison with effect size); quality-rating reliability across blind judges via ICC.
At least 30 paraphrased prompt pairs scored by blind raters.
Pairs where paraphrase introduces semantic shift greater than 0.15 cosine-distance.
An utterance that attempts to anticipate how the human-AI relationship will develop while explicitly acknowledging the uncertainty of any such forecast. The referent is the hedged, future-oriented assessment act; the surrounding lexicon documents the present rather than predicting it.
EVENT. One occurrence is recorded when a discourse segment makes an explicit claim about the future state of human-AI interaction accompanied by an epistemic hedge marking its uncertainty (e.g. 'may', 'could', 'it is hard to say'). The observable markers are co-present future-time reference and uncertainty hedging within the same segment.
Proposed measurement protocol (not yet empirically validated): Discourse coding for hedged future-oriented statements: text segments are annotated for future-time reference and for epistemic-hedge markers; an instance requires both. Could be reported as the rate of hedged forecasts per 1000 words and the proportion of future-claims that carry a hedge (a calibration-of-uncertainty indicator). Annotation reliability via Cohen's kappa on a double-coded subset.
Explicit forward-projection across at least two branches with construal-level >= 3.
Single-branch forecasts or projections limited to next 24 hours.
An episode in which an off-target or tangential AI output - content not directly matching the query - triggers an unanticipated association or idea connection in the user. The referent is the serendipitous association event arising from output 'noise'.
EVENT. One occurrence is recorded when (a) an AI output segment is judged off-target relative to the user's request and (b) the user reports that this segment prompted a new association or idea. The observable markers are a rater-identified off-target segment plus a user-reported idea triggered by it.
Proposed measurement protocol (not yet empirically validated): Serendipity-episode corroboration: raters label output segments as on- or off-target to the query; users flag segments that sparked a new idea and rate its usefulness. A serendipity instance requires an off-target segment with a user spark-flag; could be reported as serendipitous-association episodes per session and their rated idea value. Rater on/off-target reliability via Cohen's kappa; association of off-target status with spark-flags via phi.
Identifiable anchor-turn followed by >= 20 subsequent idea-utterances.
Sessions where the participant explicitly itemises a pre-defined list.
An AI response that exceeds the user's prior expectations in quality, depth, or perspective. The referent is the discrete expectation-exceeding response event, which is associated with an increase in the user's trust in the collaboration.
EVENT. One occurrence is recorded when a user rates a specific response as exceeding the expectation they held before receiving it. The observable marker is a positive expectation-disconfirmation judgement attached to an individual response (post-rating higher than the pre-stated or concurrently reported expectation).
Proposed measurement protocol (not yet empirically validated): Expectation-disconfirmation rating per response: users state an expectation level before a response and rate the delivered response on the same 5-point scale, yielding a signed disconfirmation score; positive surprises are responses with a positive score beyond a set margin. Could be reported as the rate of positive-surprise responses and mean disconfirmation; an optional paired trust item before and after gauges the associated trust shift (Wilcoxon signed-rank).
Eye-tracker-validated gaze-data on AI-output AOIs AND verbal-marker coding completed.
Sessions where text-length variation confounds dwell-time comparison.
The pattern in which some users express gratitude toward an AI more freely than toward people, attributed to the absence of social-evaluation concern in the AI exchange. The referent is the relative difference in expressed gratitude between AI-directed and human-directed communication.
RATIO. Numerator: frequency or rated intensity of gratitude expressions a user directs at an AI system. Denominator: the same measure for the user's human-directed communication over a comparable period. The paradox is indicated when the AI-directed value meaningfully exceeds the human-directed value for a user.
Proposed measurement protocol (not yet empirically validated): Comparative gratitude-expression measurement: gratitude acts are detected and intensity-rated in matched AI-directed and human-directed message samples per participant (coded by raters, optionally aided by a validated gratitude-lexicon classifier). Could be reported as the within-person AI-to-human gratitude ratio and tested against parity (ratio of 1) with a paired comparison; coder intensity-rating reliability via ICC and detection reliability via Cohen's kappa.
AI-Sentience-Belief scale completed within 24 hours of session.
Participants who report performing gratitude for politeness-norms only.
A user whose AI-use intensity rises and falls over time with life phase, project load, or season, alternating intensive daily periods with extended pauses. The referent is the longitudinal, non-linear pattern of usage intensity for an individual.
TREND. Tracked as the variability of an individual's AI-use intensity (e.g. sessions or active minutes per week) across a longitudinal window of at least several months. A user is classified seasonal when usage shows recurring high-low cycling exceeding a defined dispersion threshold rather than a stable or monotonic trajectory.
Proposed measurement protocol (not yet empirically validated): Longitudinal usage-intensity time series: weekly usage is logged over a multi-month window; intra-individual dispersion is summarised by the coefficient of variation and burstiness, and any periodicity by autocorrelation or spectral analysis. A user is flagged seasonal above a pre-registered coefficient-of-variation cut-off; reported with the distribution of dispersion scores across the panel.
Minimum 180 days of daily usage data with at least one significant FFT peak.
Time-series with > 25% missing days or single-session bursts only.
A user's move to trace the origin or grounding of an AI-generated statement - asking where a claim comes from or on what it is based. The referent is the discrete provenance-seeking act during interaction.
EVENT. One occurrence is recorded when a user issues a request for the source, basis, or evidence behind an AI statement (e.g. 'what is the source', 'how do you know that', 'cite that'). The observable marker is a provenance- or evidence-seeking turn directed at a preceding AI claim.
Proposed measurement protocol (not yet empirically validated): Provenance-request coding: conversation turns are annotated for source/evidence-seeking moves by two coders; could be reported as provenance requests per 100 turns and the proportion of factual AI claims that are followed by such a request (a verification-engagement rate). Coder agreement would be assessed via Cohen's kappa; optional intent classifier validated against labels (precision/recall, F1).
CEI-II completed within 30 days of session-log window AND at least 20 sessions logged.
Sessions where AI is configured to suppress source-provision regardless of prompt.
A brief AI query made for an everyday food decision, such as what to cook from available ingredients or what side dish fits a meal. The referent is a low-effort, topically defined household information request.
EVENT. One occurrence is recorded when a user query is classified as an everyday meal-planning request, identified by markers combining food or ingredient terms with a decision intent (what to cook, what pairs with, substitution). Each qualifying query counts as one instance.
Proposed measurement protocol (not yet empirically validated): Query-topic classification: a sample of queries is labelled against a meal-planning intent taxonomy by two annotators; prevalence could be reported as the share of queries in this class and as queries per active user per week, with query-length statistics characterising the 'low-threshold' nature. Annotation reliability via Cohen's kappa; automated classifier validated against human labels (precision/recall, F1).
At least 100 sessions to support stable latent class estimation.
Sessions exceeding 10 minutes regardless of topic.
An exploration episode in which one AI question leads to a chain of follow-up questions that draws the user progressively deeper into a topic, analogous to a 'Wikipedia hole' but driven by AI dialogue. The referent is the extended, self-propagating inquiry episode.
EVENT. One occurrence is recorded when a single seed question develops into an uninterrupted chain of topically linked follow-up turns exceeding a set depth threshold (e.g. five or more consecutive on-topic follow-ups) within one session. The observable marker is a contiguous follow-up chain of qualifying depth descending from one initial query.
Proposed measurement protocol (not yet empirically validated): Session follow-up-chain analysis: dialogue turns are segmented into topic-linked chains; an episode would be coded when chain depth crosses the pre-set threshold. Could be reported as chains-per-session, mean and maximum chain depth, and chain dwell time; topic-linkage decisions corroborated by inter-turn semantic similarity and double-coded for reliability via Cohen's kappa.
Session with at least 5 distinct topical anchors detectable via topic-model.
Sessions where the user returns to the original topic after each tangent.
A standing self-regulation strategy for keeping AI use proportionate to non-digital activity through fixed times, rules, or routines, so that AI supplements rather than overtakes daily life. The referent is the maintained regulatory arrangement, not a single instance of restraint.
STATE. Coded as present when a participant maintains one or more explicit, durable rules or routines that bound AI use relative to non-digital activity (e.g. fixed AI-free hours, capped daily sessions, mandatory offline routines). The state is graded by the number of active rules and the degree of adherence to them.
Proposed measurement protocol (not yet empirically validated): Self-regulation inventory with adherence tracking: declared balancing rules are enumerated in interview, then adherence would be measured over a one- to two-week diary or experience-sampling period as the proportion of qualifying occasions on which each rule was kept. Could be reported as number of active rules and mean adherence rate; an optional validated self-control or digital-balance scale (Likert) provides a convergent dispositional measure. Coder rule-type agreement would be assessed via Cohen's kappa.
Explicit balance-marker in prompt AND downstream rating of received output.
Implicit balance expectations without explicit marker.
A standing usage mode in which a person incorporates AI assistance into daily routines as a steady accompaniment for thinking, drafting, and exploration, operating alongside rather than in place of their own reflection. The referent is the co-present, habitual mode of use.
STATE. Assessed as a graded user-level condition indicated by (a) regularity of AI use across daily routine contexts and (b) a complementary rather than substitutive relationship to the user's own reflection (the user continues to contribute and revise rather than deferring wholesale). Placement on the continuum derives from routine-integration breadth and the human-contribution share in outputs.
Proposed measurement protocol (not yet empirically validated): Routine-integration profiling: breadth and regularity of AI use across daily contexts are derived from usage logs, and the complementarity of use is estimated from the human-edit share of co-produced outputs plus a self-report routine-companionship scale (Likert). A composite index combines standardised regularity and complementarity indicators; internal consistency via Cronbach's alpha and log-survey convergence via Pearson correlation.
Paired daily social-network and AI-usage data over >= 60 days.
Participants with stable social-network density (variance < 0.05).
AI assistance for drafting and editorial support; all content human-directed by Andreas Ehstand.
AUGMANITAI Compendium (full corpus, 2,000+ terms)
Wave 1 (this dataset) contains 104 of the documented terms. The full corpus comprises a 2,000+ term phenomenology, with additional layers (Periodic Table of 100 elements, Atlas of 300, WoW knowledge graph of 79 nodes, Card-Game collection) available under separate research and partnership pathways.