arXiv subjects

Thammathip Piumsomboon

Publications and source records attributed to Thammathip Piumsomboon.

4 recordsLinked to original sources

arXiv2026

Self++: Co-Determined Agency for Human--AI Symbiosis in Extended Reality

Self++ is a design blueprint for human-AI symbiosis in extended reality (XR) that preserves human authorship while still benefiting from increasingly capable AI agents. Because XR can shape both perceptual evidence and action, apparently 'helpful' assistance can drift into over-reliance, covert persuasion, and blurred responsibility. Self++ grounds interaction in two complementary theories: Self-Determination Theory (autonomy, competence, relatedness) and the Free Energy Principle (predictive stability under uncertainty). It operationalises these foundations through co-determination, treating the human and the AI as a coupled system that must keep intent and limits legible, tune support over time, and preserve the user's right to endorse, contest, and override. These requirements are summarised as the co-determination principles (T.A.N.): Transparency, Adaptivity, and Negotiability. Self++ organises augmentation into three concurrently activatable overlays spanning sensorimotor competence support (Self: competence overlay), deliberative autonomy support (Self+: autonomy overlay), and social and long-horizon relatedness and purpose support (Self++: relatedness and purpose overlay). Across the overlays, it specifies nine role patterns (Tutor, Skill Builder, Coach; Choice Architect, Advisor, Agentic Worker; Contextual Interpreter, Social Facilitator, Purpose Amplifier) that can be implemented as interaction patterns, not personas. The contribution is a role-based map for designing and evaluating XR-AI systems that grow capability without replacing judgment, enabling symbiotic agency in work, learning, and social life and resilient human development.

cs.HC↗

arXiv2025

Investigating AI in Peer Support via Multi-Module System-Driven Embodied Conversational Agents

Young people's mental well-being is a global concern, with peer support playing a key role in daily emotional regulation. Conversational agents are increasingly viewed as promising tools for delivering accessible, personalised peer support, particularly where professional counselling is limited. However, existing systems often suffer from rigid input formats, scripted responses, and limited emotional sensitivity. The emergence of large language models introduces new possibilities for generating flexible, context-aware, and empathetic responses. To explore how individuals with psychological training perceive such systems in peer support contexts, we developed an LLM-based multi-module system to drive embodied conversational agents informed by Cognitive Behavioral Therapy (CBT). In a user study (N=10), we qualitatively examined participants' perceptions, focusing on trust, response quality, workflow integration, and design opportunities for future mental well-being support systems.

cs.HC↗

arXiv2025

Self++: Merging Human and AI for Co-Determined XR Living in the Metaverse

This position paper introduces Self++, a novel nine-level framework for co-determined living in the Metaverse, grounded in Self-Determination Theory. Self++ prioritises human flourishing by progressively cultivating competence, autonomy, and relatedness through dynamic human-AI collaboration in extended reality (XR). Unlike technologically deterministic approaches, Self++ emphasises user empowerment by enhancing competency, mitigating cognitive biases and leveraging XR's immersive capabilities. Key research directions proposed include exploring the boundaries of user-defined AI autonomy, designing for meaningful social connection in XR, and establishing proactive ethical safeguards. Ultimately, Self++ offers a roadmap for creating a human-centred, AI-enhanced Metaverse where technology amplifies, rather than diminishes, human potential.

cs.HC↗

arXiv2024

Large Language Models for Automatic Detection of Sensitive Topics

Sensitive information detection is crucial in content moderation to maintain safe online communities. Assisting in this traditionally manual process could relieve human moderators from overwhelming and tedious tasks, allowing them to focus solely on flagged content that may pose potential risks. Rapidly advancing large language models (LLMs) are known for their capability to understand and process natural language and so present a potential solution to support this process. This study explores the capabilities of five LLMs for detecting sensitive messages in the mental well-being domain within two online datasets and assesses their performance in terms of accuracy, precision, recall, F1 scores, and consistency. Our findings indicate that LLMs have the potential to be integrated into the moderation workflow as a convenient and precise detection tool. The best-performing model, GPT-4o, achieved an average accuracy of 99.5\% and an F1-score of 0.99. We discuss the advantages and potential challenges of using LLMs in the moderation workflow and suggest that future research should address the ethical considerations of utilising this technology.

cs.CL↗