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Beyond Feeling Better: Capability-Sustaining Emotional Dialogue as a Longitudinal Research Paradigm
Ming Wang, Jiaqi Wu Young, Wenfang Wu, Daling Wang, Shi Feng
TL;DR
Current emotional-support research largely targets immediate understanding or relief, leaving sustained user capabilities and lifecycle risks insufficiently evaluated. This paper proposes capability-sustaining emotional dialogue (CSED) and audits existing literature and ESConv support turns. The audit finds that 95% of 60 system-building papers pursue relief-oriented goals, while none evaluates capability or longitudinal outcomes.
Problem
Emotional dialogue research lacks evidence and strategies centered on sustaining users’ regulation, coping, decision ownership, and social connection across repeated use and termination.
Method
The paper proposes CSED as a longitudinal paradigm and audits system-building literature and ESConv turns, connecting capability to design, evaluation, and lifecycle commitments.
Results
95% of 60 system-building papers pursue relief-oriented goals; none evaluates capability or longitudinal outcomes, and only 1 considers dependency, autonomy, or termination.
Takeaways & Limitations
CSED provides a testable agenda for capability measures, longitudinal evaluation, policy and training design, and governance across the interaction lifecycle.
Takeaways & Limitations
The evidence base is limited to 91 title- and abstract-level arXiv records.
Abstract
from arXiv · showhide
Emotional dialogue research includes two influential strategy traditions. Empathetic dialogue prioritizes understanding a speaker's emotional experience. Emotional support conversation selects and sequences support for the seeker's current needs. Sustained use introduces a further goal. Effective support should sustain users' capacities for emotion regulation, coping, self-endorsed decisions, and social connection across the interaction lifecycle. We propose capability-sustaining emotional dialogue (CSED) as a longitudinal research paradigm that aligns supportive strategy with this goal and organizes data, models, system design, evaluation, and governance around repeated use, non-use, transition, and termination. A targeted literature-and-corpus audit motivates this position. In a PRISMA-ScR-guided sample, 95% of 60 system-building papers pursue relief-oriented goals. None evaluates capability or longitudinal outcomes, and only 1 considers dependency, autonomy, or termination risk. In 300 ESConv supporter turns, capability-relevant functions appear in 43.0%, while generic suggestions account for 22.0%, compared with 4.0% reappraisal, 6.7% self-efficacy support, and 0.3% boundary behavior. We release a protocol for extending the audit to model behavior. An illustrative process model connects latent user capability to six design commitments, four evaluation timescales, and lifecycle constraints. The resulting agenda makes CSED testable across data, policy design, training, evaluation, and governance.
Introduction
Emotional dialogue research has emphasized understanding emotional experience or supporting current needs, while repeated use creates consequences for coping, decision ownership, and social connection. Capability-sustaining emotional dialogue (CSED) extends these traditions with capability maintenance, lifecycle scope, and longitudinal research methods.
- Empathetic dialogue prioritizes recognizing and responding to emotional experience, whereas emotional support conversation organizes exploration, comforting, and action around current needs.Their primary distinction lies in strategy and goal.
- Repeated supportive choices can shape offline coping, decisions, and human relationships, creating risks to decision ownership, offline engagement, and social connection.Validation or directive interaction may weaken decision ownership, while sustained use can foster attachment, substitution, and lower offline social engagement.
- CSED aims to preserve or strengthen emotion regulation, coping, self-endorsed decision making, and social connectedness across repeated sessions, non-use, re-engagement, transfer, and termination.Its lifecycle scope includes model change, support transfer, and forewarning and closure around termination.
- The proposed research cycle translates CSED into longitudinal data construction, mechanism-matched policy design, constraint-aware training, multiscale evaluation, and auditable governance.The paradigm connects user capability to six design commitments, four evaluation timescales, and lifecycle constraints.
- CSED uses a capability-oriented strategy, a full-interaction unit of inquiry, and lifecycle scope extending understanding- and support-oriented paradigms.The paper also provides a targeted literature and ESConv audit and a protocol for extending the audit to model behavior.
Background and Motivation
Empathetic dialogue centers emotional attunement, while emotional support conversation selects support for current needs and commonly targets relief. The audit identifies a gap in capability and longitudinal measurement that CSED addresses by orienting support toward users’ capacities across continued use.
- Empathetic dialogue: Empathetic dialogue centers empathic attunement to emotional experience, commonly evaluated at the turn or dialogue level.Later work develops emotion recognition, affect-aware decoding, and empathic generation.
- Emotional support conversation: Emotional support conversation selects and sequences support for current needs, with relief, helpfulness, and conversation-level change as common evaluation targets.ESC grounds exploration, comforting, and action in helping-skills theory; later work improves strategy planning, persona awareness, and multistrategy turns.
- Audit findings: 95.0% of system-building papers pursue relief-oriented objectives, while none measures a user-capability outcome or evaluates longitudinally.Of 60 system-building papers, 57 pursue relief-oriented objectives; two mix relief and capability objectives, and one targets peer-counselor capability.
- Audit findings: 43.0% of sampled ESConv supporter turns contain capability-relevant functions, compared with 22.0% generic suggestions, 4.0% reappraisal, 6.7% self-efficacy support, and 0.3% boundary behavior.The 300-turn function-coded sample provides a more detailed picture than ESConv’s strategy labels, whose label set lacks categories for reappraisal, efficacy, or safety.
- Capability and lifecycle orientation: CSED specifies capability-oriented strategies, observations, and governance for sustained-use research, while longitudinal outcome comparisons and causal estimates remain future empirical tasks.Its orientation is to sustain users’ own capacities across continued use rather than focus only on immediate relief.
CSED: A Longitudinal Research Paradigm
CSED reframes emotional dialogue around sustaining users’ capabilities across the full longitudinal interaction, including repeated use, non-use, transition, and termination. It organizes system design and evaluation around capability dynamics, lifecycle safety, and outcomes beyond immediate relief.
- Lifecycle scope: The paradigm treats repeated sessions, non-use, re-engagement, transition, and termination as the primary unit of design and study rather than limiting analysis to individual exchanges.Transition and termination include model replacement, shutdown, handoff, closure, memory dignity, and support transfer.
- Definition: CSED sustains capacities for emotion regulation, active coping, self-endorsed decision making, and social connectedness across the full longitudinal interaction.Capability includes preserving adequate capacities, strengthening them when needed, and avoiding erosion through repeated reliance.
- Design commitments: CSED adds resilience activation, autonomy preservation, social connectedness maintenance, and transition and termination safety to inherited empathy and support effectiveness.These commitments connect conversational strategy to coping, decision ownership, human connection, and safe disengagement.
- Capability dynamics: A policy can provide immediate help while degrading capabilities over time, creating a comfort trap that longitudinal evaluation is designed to reveal.Extended exposure to relationship-seeking AI may increase attachment and continued-use intent without corresponding psychosocial improvement.
- Evaluation: Common empathetic and emotional-support benchmarks are nested special cases of CSED, while preference alone cannot capture capability, dependency, autonomy, or longitudinal safety.Preferred interactions can co-occur with disempowerment, and supportiveness-maximizing prompts can reduce safety.
Research and Governance Agenda
The agenda makes CSED testable through longitudinal benchmarks, policy comparisons, multi-timescale evaluation, and an auditable governance cycle. It emphasizes tracking capability and risk across repeated use, non-use, transition, and termination.
- Governance: The research cycle retains round-level evidence and decisions, updating measurement, data, or policy after failures and mapping evaluation results to deploy, revise, or halt.This makes the cycle auditable and routes failed constraints to targeted upstream revision.
- Longitudinal benchmarks: Benchmarks should link time-stamped user states, system behaviors, and outcomes to stressors or value-laden decisions, including non-use periods and activation.States include distress, agency, reliance cues, readiness, and offboarding vulnerability; behaviors include reconnection, boundary, and closure; outcomes include relief, coping activation, human-support contact, reliance, and termination distress.
- Policy design: Policy studies should compare relief-only, resilience-activation, and state-conditioned mechanism-matching policies under a fixed base model and shared safety floor.Memory, follow-up, reconnection, and handoff should remain user-controlled, while training can incorporate reliance, autonomy, and social connection constraints.
- Evaluation: Evaluation should preserve response, conversation, longitudinal, and termination timescales because response quality can coexist with weak activation and improved coping with rising reliance.Reports should present scales separately, assess persistence during non-use, and require capability claims to show users exercise the capacity beyond dialogue.
- Testable hypotheses: H1-H4 specify tests of resilience activation, preference maximization, reconnection behavior, forewarning, closure support, and post-change fixation outcomes.The stated hypotheses link these interventions to bJconv, R, immediate relief, Dep/Aut thresholds, Dsep, and Ffix.
Boundary Conditions and Limitations
CSED targets systems designed for sustained use, while one-off exchanges may use response- or session-level evaluation. Its longitudinal approach must account for AI reliance, privacy and surveillance risks, user control, and conflicts among support commitments.
- Boundary Conditions and Limitations: CSED is intended for systems designed for sustained use, whereas one-off exchanges can use response- or session-level evaluation.The paradigm’s scope therefore depends on the interaction lifecycle being studied.
- Boundary Conditions and Limitations: AI reliance implications depend on capability change, decision ownership, and access to human support.User-endorsed targets align support with personal values.
- Boundary Conditions and Limitations: Longitudinal measurement creates privacy and surveillance risks, requiring data minimization, explicit consent, and user control over memory and follow-up assessment.These safeguards address the risks introduced by collecting information across repeated interactions.
- Boundary Conditions and Limitations: CSED commitments can conflict, including tensions between immediate relief and longer-term capability-sustaining aims.The passage explicitly notes that the commitments can conflict, but the supplied text ends before specifying the full tension.
Conclusion · A Theoretical Status and Scope · A.1 Formal Primitives and Restrictions
The paper positions CSED as a theoretical, longitudinal research paradigm centered on sustained user capability and the full interaction lifecycle. It formalizes this scope through latent-state primitives, lifecycle restrictions, and claims that distinguish definitions, structural propositions, and empirical hypotheses.
- Conclusion: CSED aligns emotional support with users’ capacities to regulate, cope, choose, and connect across continued use.Its unit of inquiry is the longitudinal interaction rather than an isolated exchange.
- A Theoretical Status and Scope: CSED specifies the research object, success criterion, user-change model, design commitments, evaluation horizons, and lifecycle constraints.Its theoretical objects include longitudinal records, transient and capability states, four evaluation horizons, and six design commitments.
- A Theoretical Status and Scope: The framework distinguishes definitional, structural, and empirical-hypothesis claims, with hypotheses requiring longitudinal data and causal study design.Definitions and structural propositions can be evaluated for coherence and derivation, whereas empirical hypotheses concern future policies and lifecycle interventions.
- A.1 Formal Primitives and Restrictions: The formal process model represents a longitudinal record C containing the user, policy, sessions, and an optional termination event.The latent state sk separates transient emotion ek from capability vector ck, while dialogue is one input among several influences on transitions.
- A.1 Formal Primitives and Restrictions: The model connects latent states to validated instruments and behavioral markers through a measurement map M.Stressors, offline actions, and human relationships also affect the transition kernel Φ, so dialogue exposure is not the only cause of change.
- A.1 Formal Primitives and Restrictions: The framework targets sustained-use settings, treats capability as latent, and requires construct-valid proxies and contextual calibration of δ, α0, and σ0.Calibration should involve affected users and domain experts.
- A.1 Formal Primitives and Restrictions: Causal claims require longitudinal designs addressing time-varying confounding, reciprocal effects, and selective attrition.Autonomy is separate from reliance and involves voluntary uptake, endorsed goals, and the ability to appraise, revise, or reject guidance.
A.2 Structural Propositions · B Targeted Literature Audit · B.1 Search, Screening, and Sampling
The structural propositions embed response- and conversation-scored objectives within a broader CSED evaluation program, while showing that short-horizon scores cannot identify longitudinal capability outcomes. The targeted audit uses a PRISMA-ScR-guided, year-stratified sampling and coding procedure to characterize system-building literature.
- A.2 Structural Propositions: Response-scored empathetic dialogue and session-scored emotional-support-conversation objectives are restricted cases of the combined CSED objective.This follows by parameter restriction and removal of longitudinal constraints.
- A.2 Structural Propositions: The unified evaluation program preserves existing empathetic-dialogue and ESC strategies while requiring additional observations for systems available across repeated use.Those observations concern later-use outcomes beyond the existing benchmark objectives.
- A.2 Structural Propositions: Equal response and conversation scores do not imply equal longitudinal capability outcomes.Policies can match on observed histories, responses, and within-session proxy changes while differing in later capability transitions.
- A.2 Structural Propositions: Evaluating longitudinal differences therefore requires later observations of capability, reliance, autonomy, and connectedness.The proposition is about evaluator evidence, not a claim that a particular deployed policy produces either transition.
- B Targeted Literature Audit: The literature arm is a PRISMA-ScR-guided targeted scoping audit using seven exact search phrases and arXiv records retrieved on July 8, 2026.The script retrieved the first 200 records for each query in reverse submission-date order and deduplicated records by version.
- B.1 Search, Screening, and Sampling: The pilot targeted 90 included records, but integer rounding within annual strata produced 91 records.The sampling seed was 20260708, and coding used titles and abstracts.
- B.1 Search, Screening, and Sampling: 60 coded records build or evaluate systems, while the remaining records cover user studies, reviews, datasets, evaluations, and position papers.Claims about system objectives use the 60-record subset, whereas claims about evaluation horizon use all records.
B.2 Paper-Level Codebook
The paper-level codebook organizes the audit across six dimensions, with psychology-grounded mechanism definitions intended to reduce circularity. The coded sample shows a strong concentration on relief objectives and interaction-quality evaluation, while capability, longitudinal, and risk outcomes remain largely unmeasured.
- Codebook dimensions: The codebook uses six dimensions covering objectives, named mechanisms, evaluation outcomes, and evaluation horizons.D1 distinguishes relief, capability, both, or neutral objectives; D2 records mechanisms; D3 and D4 record the highest outcome and longest horizon.
- Codebook dimensions: Mechanism definitions are grounded in psychology rather than derived from CSED, reducing circularity between the paradigm and audit categories.Definitions cover reappraisal, problem solving, self-efficacy, and social connection using established behavioral or competence criteria.
- Strategic and longitudinal gap: 95.0% of system-building papers—57 of 60—identify relief as their primary objective, while no coded record evaluates a user-capability outcome or uses a longitudinal horizon.Two papers combine relief and capability, and one targets peer-counselor capability.
- Measurement concentration: 54 of 59 records naming validation or comfort use interaction-quality outcomes, compared with three using proximal state outcomes and two containing no evaluation.Problem solving and self-efficacy records likewise rely primarily on interaction-quality evaluation.
- Limitations and extensions: The audit proportions characterize the coded sample rather than the broader literature because sampling, source, coding, and AI-only methods limit population inference.The released protocol proposes full-text, multi-database, and human-recoding extensions.
C ESConv Corpus Analysis · C.1 Sampling and Function Codebook
The ESConv analysis codes a stratified 300-turn sample using ten communicative functions and groups them into relief, capability-relevant behavior, or interaction process. Capability-relevant functions comprise 43.0% of sampled turns, while stage distributions describe the sample rather than longitudinal outcomes.
- C.1 Sampling and Function Codebook: ESConv contains 1,300 dialogues and 18,376 strategy-annotated supporter utterances, with suggestions at 16.1% and affirmation and reassurance at 15.4%.
- C.1 Sampling and Function Codebook: The analysis draws 300 supporter utterances through stratified random sampling, with 100 turns each from early, middle, and late dialogue-position terciles.The sampling seed was 20260708, and each item includes the supporter turn plus up to two preceding seeker utterances.
- C.1 Sampling and Function Codebook: Ten functions are coded, then mapped to relief, capability-relevant behavior, or interaction process after function coding.F1 maps to relief; F3 through F7 to capability-relevant behavior; and F2 plus F8 through F10 to interaction process.
- C.1 Sampling and Function Codebook: Mixed-function rules prioritize the main clause, classify embedded advice as problem solving, code strength-based reassurance as self-efficacy, and require preserved user direction for capability-supporting decisions.These rules separate supportive tone from the turn’s communicative function.
- C.1 Sampling and Function Codebook: 43.0% of sampled turns are capability-relevant functions, compared with 15.3% relief and 41.7% process functions.The exact counts are 129 capability-relevant, 46 relief, and 125 process turns out of 300.
- C.1 Sampling and Function Codebook: Capability relevance appears in 26 early turns, 55 middle turns, and 48 late turns, while relief appears in 17, 15, and 14 turns, respectively.Process functions appear in 57 early, 30 middle, and 38 late turns; these distributions describe the sample and do not estimate longitudinal user outcomes.
C.2 Reliability Design … D.2 Model-Behavior Probe
Reliability was assessed across four AI coding sets on a shared 40-turn subset, with stronger agreement for coarse strategic categories than fine-grained functions. Claim traceability is artifact-backed, while model-behavior evaluation remains a released protocol rather than a completed audit result.
- C.2 Reliability Design: Four label sets were compared on one random subset of 40 sampled turns, using shared definitions and seeker context without access to other labels.The sets were primary AI coding, a blind repeat, GPT-5.5, and DeepSeek-V4-Pro; compatible endpoints used temperature zero.
- C.2 Reliability Design: 0.631 mean fine-grained kappa and 0.716 mean paradigm-level kappa indicate stronger agreement for coarse strategic categories than individual functions.Fine-grained kappa ranged from 0.547 to 0.751, while paradigm-level kappa ranged from 0.646 to 0.845; 31 of 57 disagreements stayed within one analysis category.
- C.2 Reliability Design: Reliability measures agreement among AI coders and does not replace human construct validation, independent coding, preregistered adjudication, or a larger sample.The limitation explicitly distinguishes coder consistency from full human validation.
- D.1 Claim-Evidence Map: Table 7 maps every headline quantity to a frozen result artifact with an exact relative path and checksum for each short source label.The claim-evidence map provides traceability from reported quantities to supplied artifacts.
- D.2 Model-Behavior Probe: The model-behavior arm is a released protocol using 100 held-out ESConv contexts sampled with seed 20260708 and at least two chat models.The pipeline stores responses and model identifiers, applies the F1 through F10 codebook, and plans distributional, stage-conditional, and coding-reliability comparisons.
- D.2 Model-Behavior Probe: No deployed-model result from this arm contributes to the audit percentages, preserving separation between completed corpus analysis, theory, and future model-behavior tests.The arm is explicitly presented as planned evaluation rather than a completed result.