2026· Poster Volume 0007 The 2026 Twenty-Second International Conference on Intelligent Computing July 23-26, 2026 Toronto, Canada· pp. 2462-2486· 0 citations
Abstract
Existing dialogue resources for minors mainly focus on general psychological support, adolescent positive mental health promotion, or broad emotional companionship, and therefore do not adequately support the task of everyday emotion coaching for Chinese children. To address this gap, we construct ChildEC, a developmentally stratified multi-turn dialogue corpus for everyday emotion coaching, targeting Chinese children aged 8--12. The corpus contains 1,709 multi-turn dialogues spanning two developmental stages and five core themes. To support corpus construction and evaluation, we further propose the Theory-Grounded Emotion Coaching Framework (TGEC). Within this framework, TGEC-Synth operationalizes the five-step emotion coaching model and Socratic dialogue into a multi-stage dialogue synthesis pipeline, while TGEC-Eval assesses the quality of child emotion coaching dialogues along six key dimensions. Experimental results show that models fine-tuned on ChildEC consistently outperform their corresponding base models under both automatic metrics and LLM-as-a-Judge evaluation, demonstrating the downstream training value of the corpus for child emotion coaching. Overall, ChildEC provides a specialized data resource and an evaluation reference for developmentally sensitive and theory-consistent research on Chinese child emotion coaching. Our dataset will be publicly released upon acceptance of the paper.
EmoTrace, a multi-turn dialogue corpus generation framework centered on modeling seekers' emotional trajectories, is proposed, which outperforms existing approaches in terms of emotional richness and empathy quality.
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.
Ming Wang, Jiaqi Wu Young, Wenfang Wu et al.· 0 citations
Recent advances in artificial intelligence have enabled the development of more natural and effective human-machine interactions. In this study, we propose an enhanced BART-based dialogue generation framework that integrates emotion labels and keyword annotations to support emotionally aware end-to-end conversations. The proposed system combines an independent RoBERTa-based emotion classification module with a terminology-aware annotation mechanism, in which emotional labels and key semantic terms are explicitly injected into the dialogue context. The framework is evaluated on a merged dataset constructed from two benchmark dialogue corpora, Empathetic Dialogues and DailyDialog, which have been relabeled into nine unified emotion categories. Automatic evaluation is conducted using BLEU, ROUGE, and perplexity metrics. Experimental results demonstrate that the proposed approach consistently outperforms baseline models, including GPT-2, DialoGPT, T5, UniLM, and the original BART model. In particular, the incorporation of keyword annotations significantly improves response relevance and informational completeness, while emotion labels further enhance emotional alignment. These findings suggest that explicitly modeling emotional cues and semantic focus can effectively improve the quality of emotional dialogue generation, offering a promising direction for the development of emotionally intelligent conversational agents.
Within research on early childhood cognitive development, schema accommodation—a core Piagetian construct—refers to the cognitive restructuring process through which individuals revise existing schemas or form entirely new ones when encountering information that contradicts their prior knowledge. This mechanism is essential for cognitive growth, as it enables the child to resolve conflicts between entrenched mental frameworks and novel experiences. For instance, a child who initially believes that "all flying things are birds" must adjust that criterion after discovering that bees fly but are not birds. This adjustment involves not merely adding new facts but fundamentally altering the classification rule, thus exemplifying accommodation as a deep reorganization of the original cognitive structure, rather than simple assimilation. This study explores how questioning-based and descriptive-only parent-child dialogue affect preschoolers' schema accommodation and its age differences. Forty children aged 3–5 joined a randomized pretest-posttest experiment. Results show questioning-based dialogue better promotes schema accommodation, especially for 4–5-year-olds, and guides children to focus on functional features. The findings offer evidence-based guidance for parents' daily cognitive guidance.
Pinyan Li· Lecture Notes in Education P...· 0 citations
Large Language Models (LLMs) have substantially advanced persona-based dialogue agents for emotion-sensitive role simulation in healthcare, education, counseling, customer service, and interactive storytelling. However, two related lines of work leave a key gap. Persona-based dialogue systems often encode emotions as static traits or surface-level stylistic cues, and affective dialogue research has largely focused on empathetic response generation toward users rather than modeling the agent persona's own evolving emotional state. As a result, trigger-driven emotional evolution within a character remains underexplored. To address this limitation, we draw inspiration from the Component Process Model (CPM), a psychological theory that views emotion as a dynamic process shaped by the appraisal of external events. We propose CPM-MultiAgent, a CPM-grounded emotion evolution multi-agent framework for supporting emotional changes in persona-based dialogue. Instead of treating a character's emotion as a fixed attribute, CPM-MultiAgent represents it as a latent state that is continuously reshaped by dialogue triggers. Through affective trigger extraction, CPM-based collaborative appraisal, and emotion state updating, the framework enables more emotionally consistent role simulation in multi-turn interactions.Experiments with baseline comparisons, ablation studies, human evaluation, and case analyses demonstrate that CPM-MultiAgent effectively models dynamic emotional evolution in emotionally sensitive role-simulation settings.
Jingyao Cai, Shuaijun Liu, Abdul Rehman et al.· 0 citations
Despite significant advances in instruction-following and auditory comprehension, the evaluation of Emotional Intelligence (EI) in Spoken Language Models (SLMs) remains confined to rudimentary paralinguistic perception, lacking a systematic, theory-driven cognitive framework. We introduce EmoSBench, the first comprehensive EI evaluation benchmark for SLMs constructed upon the four-branch theoretical model, covering Perceiving, Understanding, Using, and Managing Emotion across ten sub-tasks. Preliminary assessments on EmoSBench reveal a substantial gap: even leading proprietary models like GPT-4o-Audio achieve only 52.6%, significantly trailing human baselines. To bridge this gap, we develop EmoS, a specialized evaluator model optimized via Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO). To facilitate its effective training, we curate EmoDialogue, a bilingual dataset providing necessary fine-grained supervision through response pairs with rigorously defined EI gradations. Concurrently, we introduce a reward mechanism integrating a Steep Exponential Accuracy Reward (SEAR) and a Rationale Fidelity Reward (RFR) to enforce precise ordinal scoring and valid reasoning. Experiments demonstrate that EmoS reaches 83.8% accuracy, approaching human-level performance. Furthermore, evaluations on authentic, unconstrained spoken interactions validate its robust real-world generalization, establishing a foundational framework for advancing emotionally intelligent dialogue systems.
Junyu Wang, Siyuan Zhang, Peiyuan Jiang et al.· 0 citations