arXiv subjects

Anandha Gopalan

Publications and source records attributed to Anandha Gopalan.

3 recordsLinked to original sources

arXiv2026

Embodied Empathy: A Multimodal AR and LLM-Powered System for Self-Attachment Psychotherapy with Self-Initiated Humour

The growing global demand for mental health support increasingly exceeds the supply of qualified practitioners, creating an urgent need for scalable digital interventions that can deliver meaningful emotional connection. In response, we present a novel multimodal application that operationalises the Self-Initiated Humour Protocol (SIHP) within a Self-Attachment Technique (SAT) framework. Our mobile application integrates customisable 3D childhood avatars, augmented reality, and an LLM-driven virtual therapist capable of automated emotion mirroring. An eight-day user study (N=16) indicates the system's feasibility and improvements in self-reported mood. Results show that personalised avatars and text-to-speech output strengthen emotional bonding and perceived empathy. Although emotion mirroring boosts engagement, its effectiveness depends heavily on classification accuracy and animation intensity. Moreover, findings indicate a shift in user expectations--from reactive chatbots to proactive conversational facilitators. We conclude with design implications for leveraging AI and AR to cultivate embodied empathy in digital mental health tools.

cs.HC↗

arXiv2023

A Multilingual Virtual Guide for Self-Attachment Technique

In this work, we propose a computational framework that leverages existing out-of-language data to create a conversational agent for the delivery of Self-Attachment Technique (SAT) in Mandarin. Our framework does not require large-scale human translations, yet it achieves a comparable performance whilst also maintaining safety and reliability. We propose two different methods of augmenting available response data through empathetic rewriting. We evaluate our chatbot against a previous, English-only SAT chatbot through non-clinical human trials (N=42), each lasting five days, and quantitatively show that we are able to attain a comparable level of performance to the English SAT chatbot. We provide qualitative analysis on the limitations of our study and suggestions with the aim of guiding future improvements.

cs.CL↗

arXiv2021

CO-STAR: Conceptualisation of Stereotypes for Analysis and Reasoning

Warning: this paper contains material which may be offensive or upsetting. While much of recent work has focused on the detection of hate speech and overtly offensive content, very little research has explored the more subtle but equally harmful language in the form of implied stereotypes. This is a challenging domain, made even more so by the fact that humans often struggle to understand and reason about stereotypes. We build on existing literature and present CO-STAR (COnceptualisation of STereotypes for Analysis and Reasoning), a novel framework which encodes the underlying concepts of implied stereotypes. We also introduce the CO-STAR training data set, which contains just over 12K structured annotations of implied stereotypes and stereotype conceptualisations, and achieve state-of-the-art results after training and manual evaluation. The CO-STAR models are, however, limited in their ability to understand more complex and subtly worded stereotypes, and our research motivates future work in developing models with more sophisticated methods for encoding common-sense knowledge.

cs.CL↗