Aug 2026· Symmetry· Vol 18, pp. 1409· 0 citations· 34 references
TL;DR
The Reflective Conversational Journal operationalizes a complementary human–AI role structure in which AI supports facilitation and recording while the user retains the reflector role, providing preliminary evidence of design validity and implementation fidelity rather than objective evidence of cognitive-load reduction or clinical effectiveness.
Abstract
Journaling can support mental health and self-reflection, but traditional journaling requires users to act simultaneously as reflector, facilitator, and recorder, which may increase cognitive load and potentially hinder sustained practice or contribute to rumination. This study proposes the Reflective Conversational Journal (RCJ), an AI-based system in which AI supports facilitation and recording while users focus on reflection. Grounded in cognitive load theory, Rogers’ person-centered counseling principles, and Socratic questioning, RCJ was designed around three principles: contextual connectivity, structured recording, and empathy and questioning. A prototype integrating an AI agent, a template engine, and a client application was developed as a context-aware human–AI interaction system. Four experts in journaling and psychological counseling evaluated RCJ over one week and completed a post-use evaluation comprising Likert-scale items and open-ended questions. The mean score across the nine design-validity and implementation-fidelity items was 4.67/5 (SD = 0.48). Experts perceived contextual linking as useful for recognizing behavioral patterns and automatic structuring as helpful for reducing recording burden. However, limited depth in questions and interaction fatigue from frequent questioning were identified as areas for improvement. The findings provide preliminary evidence of design validity and implementation fidelity rather than objective evidence of cognitive-load reduction or clinical effectiveness. RCJ operationalizes a complementary human–AI role structure in which AI supports facilitation and recording while the user retains the reflector role.
The advent of Large Language Models has accelerated interest in empathetic conversational agents. Despite a surge in empirical research, artificial empathy remains deeply fragmented, often serving as a catch-all term for diverse interactional phenomena. Addressing this conceptual gap, we systematically review 89 empirical studies to map how human-machine empathy is operationalized. Our synthesis reveals that empathy is highly situated and driven by functional goals, like health and well-being, transactional service, social interaction, and learning support. Within these contexts, we classify affective responsiveness by its directional flow, detailing how agents project, elicit, or mediate empathy. We structure the literature into a cohesive framework spanning linguistic, paralinguistic, identity, and architectural strategies. Furthermore, our methodological evaluation reveals a reliance on adapted clinical metrics, a scarcity of longitudinal studies, and a disproportionate focus on text-based over voice-based interfaces. Ultimately, this review equips researchers and practitioners with an actionable foundation for designing, measuring, and implementing contextually appropriate and empathetic agents.
Supriya Khadka, Smit Desai· International Conference on...· 0 citations
Conversational AI is widely explored for supporting brainstorming and creative collaboration, yet its potential to guide groups through convergence, where differing perspectives must be reconciled into a shared direction, remains largely unexamined. We introduce KNIT-C, a conversational system that operationalises computational boundary objects within synchronous multi-user dialogue, eliciting individual viewpoints, synthesising group perspectives, and guiding discussion toward negotiated outcomes. Across four public workshop sessions (14 non-expert participants), we find that conversational engagement deepened individual reflection but created pacing and coordination challenges in group interaction. AI-generated summaries anchored discussion, though their similarity demanded careful comparison rather than simplifying choices. Participants frequently reported feeling heard without fully endorsing outcomes, pointing to productive disagreement as a form of convergence that preserves visible tension rather than resolving it. We discuss implications for adaptive pacing, context-sensitive stance-shifting, and evaluation frameworks that assess whether differences were genuinely engaged rather than suppressed.
Echo Wan, C. Mougenot, Malak Sadek· International Conference on...· 0 citations
The rapid adoption of conversational AI among adolescents has sparked growing debate about its implications for development and well-being. This article applies a Self-Determination Theory lens to explore how and under which conditions adolescents' conversational AI usage supports or frustrates their basic psychological needs for competence, relatedness, and autonomy. Conversational AI may enhance competence through learning assistance, relatedness through emotional and relational support, and autonomy through increased independence. At the same time, it may also promote the risk of superficial competence, displace human relationships, and constrain authentic autonomy. Overall, the role conversational AI plays in adolescents' development is unlikely to be universally positive or negative for all adolescents, but instead depends on how, why, and in which context these technologies are used, highlighting the need for nuanced research and developmentally sensitive design.
Lauren C. Dedecker, Y. Chen, Gaëlle Vanhoffelen et al.· Child and Adolescent Mental...· 1 citation
Skilled facilitation supports inclusive small-group dialogue, but deliberate practice is hard to scale: it depends on expert coaches, live practice partners, and iterative feedback. We present FaciliTrain, a voice-based training system in which learners step into the facilitator role of an AI-simulated multi-participant conversation, apply five evidence-based techniques, and receive structured AI feedback to support reflection. We report findings from a mixed-methods study with 24 participants, conducted as a formative study (N = 12) and a controlled pilot (N = 12; 6 treatment, 6 control). Both conditions achieved comparable accuracy on a live evaluation task, though treatment participants'self-rated comfort declined significantly while control participants'comfort improved (p = .018). Reflexive thematic analysis identifies four themes: the taxonomy externalizes implicit facilitation intuitions; Making Connections is the most cognitively demanding technique; voice acts as a deliberate-response forcing function; and participants overwhelmingly preferred AI feedback over self-practice. We discuss design implications for voice-based, AI-supported interpersonal skill training at scale.
Hang Jiang, Yuanxin Zhu, Diyi Yang et al.· 0 citations
A LINE-deployed conversational AI system grounded in the WHO iSupport for Dementia framework, built around an Assessment-Before-Intervention dialogue mechanism, which offers practical implications for the design of AI-assisted care systems in dementia and other emotionally sensitive healthcare contexts.
ObjectiveThis narrative review examines the cognitive, metacognitive, and team competency requirements that may contribute to productive and reliable collaboration between human and AI to address two questions: What capabilities make AI a competent collaborator? What makes humans ready for AI collaboration?BackgroundAs AI systems are increasingly integrated into workplaces and framed as teammates rather than tools, humans face challenges that include maintaining situation awareness, calibrating trust, and working with systems that may surpass them cognitively. We analyzed Human-Agent Teaming (HAT) readiness around two complementary levels: operational team competencies (communication, coordination, and adaptability) and regulatory capacities (trust calibration and metacognitive awareness).MethodWe conducted a structured narrative review of literature from 2010 through January 2026, searching Google Scholar, Scopus, PsycINFO, IEEE Xplore, ACM Digital Library, and Semantic Scholar, complemented by forward citation tracking. After screening 572 records, 192 articles were included for synthesis.ResultsCommunication inflexibility, limited shared understanding, and trust miscalibration emerge as recurring barriers to HAT, while regulatory capacities (trust calibration and metacognitive awareness) represent particularly critical dimensions of HAT readiness that remain to be fully operationalized.ConclusionHAT requires mutual readiness, with both humans and AI developing metacognitive and adaptive capabilities. Despite methodological heterogeneity limiting clear conclusions, cross-training and co-learning methods offer a promising avenue for building shared understanding and calibrated collaboration.ApplicationThis review provides practical principles for designing AI systems that support calibrated collaboration and for preparing humans to work adaptively with AI, thereby enhancing team effectiveness, reliability, and resilience in collaborative work environments.
Sébastien Tremblay, Delphine De Hemptinne, Gabrielle Teyssier-Roberge et al.· Human Factors· 0 citations