Recipient organisationVerint Systems UK LimitedSource-published name: Verint Systems UK Limited
Funding£76K
PeriodFeb 2025 — Feb 2029
In plain English
AI plain-English summary
A chatbot that can tell when a caller is anxious or depressed will learn to adjust its tone and responses accordingly. The CRYSTAL project aims to build conversational AI systems that go beyond simple information retrieval—offering genuine emotional support to vulnerable people, such as ageing adults or those with anxiety, while also handling routine customer service calls with empathy and efficiency. Current chatbots often fail at tasks requiring emotional nuance, leaving users frustrated or unsupported. The researchers will train large language models using novel domain-adaptation techniques and prompt engineering, and will incorporate user state estimation—detecting mood or distress from speech patterns—to guide the dialogue. Strict ethical safeguards will anonymise and encrypt all conversational recordings. If successful, the technology could transform call centres and mental-health helplines, making support more accessible and consistent without replacing human workers. The project also emphasises staff secondments and cross-sector collaboration to ensure the insights transfer beyond the lab.
View original technical description
CRYSTAL (Conversational Systems for Emotional Support and Customer Assistance) will advance research for a next generation of conversational models and systems that implement reliable, friendly and efficient human-machine interactions that can assist humans in tasks beyond automatic information provision, such as emotional support as well as customer assistance. Emotional support provided to vulnerable individuals, such as ageing adults or people experiencing anxiety or depression. Advanced customer assistance provided to call centres, providing customer care and response to customer inquiries, in an empathic, inviting, accurate and efficient manner. Conversational models and systems will be developed by proposing novel learning strategies for domain adaptation, exploring Large Language Models (LLMs) and prompt engineering methodologies. User state estimation will be also considered, and strategies, such as behavioural models and stakeholder goals, will guide the policies for dialogue management. Novel evaluation frameworks will be also designed by including automatic metrics, usability as well as human acceptance of the technology. Strict ethical and AI robustness compliance procedures will be applied, as conversational recordings will be collected, anonymised, annotated and encrypted for collaborative work among the project partners. The project will apply extensive staff secondments and foster sustainable knowledge exchange. It will grow the experiences and insights of the partner organisations’ staff via intersectoral and interdisciplinary collaborations, implemented through secondments as well as training and networking activities. Dissemination, communication and exploitation activities will expand the project’s impact pathways.
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