Towards Designing a ChatGPT Conversational Companion for Elderly People
- URL: http://arxiv.org/abs/2304.09866v1
- Date: Tue, 18 Apr 2023 17:24:14 GMT
- Title: Towards Designing a ChatGPT Conversational Companion for Elderly People
- Authors: Abeer Alessa and Hend Al-Khalifa
- Abstract summary: We propose a ChatGPT-based conversational companion system for elderly people.
The system is designed to provide companionship and help reduce feelings of loneliness and social isolation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Loneliness and social isolation are serious and widespread problems among
older people, affecting their physical and mental health, quality of life, and
longevity. In this paper, we propose a ChatGPT-based conversational companion
system for elderly people. The system is designed to provide companionship and
help reduce feelings of loneliness and social isolation. The system was
evaluated with a preliminary study. The results showed that the system was able
to generate responses that were relevant to the created elderly personas.
However, it is essential to acknowledge the limitations of ChatGPT, such as
potential biases and misinformation, and to consider the ethical implications
of using AI-based companionship for the elderly, including privacy concerns.
Related papers
- SocializeChat: A GPT-Based AAC Tool Grounded in Personal Memories to Support Social Communication [9.307700706169515]
SocializeChat generates sentence suggestions by drawing on users' personal memory records.<n>System reuses past experience and tailors suggestions to different social contexts.<n>A user study shows its potential to enhance the inclusivity and relevance of AAC-supported social interaction.
arXiv Detail & Related papers (2025-10-21T18:59:38Z) - How AI Companionship Develops: Evidence from a Longitudinal Study [14.69112262771543]
We studied the psychological pathway from users' mental models of the agent to parasocial experiences, social interaction, and the psychological impact of AI companions.<n>Results suggest a longitudinal model of AI companionship development and demonstrate an empirical method to study human-AI companionship.
arXiv Detail & Related papers (2025-10-11T07:36:47Z) - Towards conversational assistants for health applications: using ChatGPT to generate conversations about heart failure [1.4347098305628967]
We explore the potential of ChatGPT to generate conversations focused on self-care strategies for African-American heart failure patients.<n>We employed four prompting strategies: domain, African American Vernacular English (AAVE), Social Determinants of Health (SDOH), and SDOH-informed reasoning.<n>Conversations were generated across key self-care domains of food, exercise, and fluid intake, with varying turn lengths.<n>While incorporating SDOH and reasoning improves dialogue quality, ChatGPT still lacks the empathy and engagement needed for meaningful healthcare communication.
arXiv Detail & Related papers (2025-05-06T16:21:10Z) - ChatWise: AI-Powered Engaging Conversations for Enhancing Senior Cognitive Wellbeing [5.900798025576862]
AI-based methods have shown promise in providing conversational support, yet existing work is limited to implicit strategy and lacking multi-turn support tailored to seniors.
We improve prior art with an LLM-driven chatbots named ChatWise for older adults.
ChatWise thrives in long-turn conversations, in contrast to conventional LLMs that primarily excel in short-turn exchanges.
arXiv Detail & Related papers (2025-02-19T21:32:09Z) - Welzijn.AI: Developing Responsible Conversational AI for Elderly Care through Stakeholder Involvement [3.257656198821199]
Welzijn.AI is a digital solution for monitoring (mental) well-being in elderly populations.
Three evaluations with different stakeholders were designed to disclose new perspectives on the strengths, weaknesses, design characteristics, and value requirements of Welzijn.AI.
arXiv Detail & Related papers (2025-02-11T21:59:19Z) - Investigating an Intelligent System to Monitor \& Explain Abnormal Activity Patterns of Older Adults [52.40826527071519]
Despite the growing potential of older adult care technologies, the adoption of these technologies remains challenging.
This work conducted a focus-group session with family caregivers to scope designs of the older adult care technology.
We developed a high-fidelity prototype and conducted its qualitative study with professional caregivers and older adults.
arXiv Detail & Related papers (2025-01-30T03:21:14Z) - Towards Privacy-Aware and Personalised Assistive Robots: A User-Centred Approach [55.5769013369398]
This research pioneers user-centric, privacy-aware technologies such as Federated Learning (FL)
FL enables collaborative learning without sharing sensitive data, addressing privacy and scalability issues.
This work includes developing solutions for smart wheelchair assistance, enhancing user independence and well-being.
arXiv Detail & Related papers (2024-05-23T13:14:08Z) - Persona Extraction Through Semantic Similarity for Emotional Support
Conversation Generation [45.21373213960324]
We propose PESS (Persona Extraction through Semantic Similarity), a novel framework that can automatically infer informative and consistent persona from dialogues.
Our experimental results demonstrate that high-quality persona information inferred by PESS is effective in generating emotionally supportive responses.
arXiv Detail & Related papers (2024-03-07T04:33:11Z) - MemoryCompanion: A Smart Healthcare Solution to Empower Efficient
Alzheimer's Care Via Unleashing Generative AI [8.741075482543991]
This paper unveils MemoryCompanion', a pioneering digital health solution specifically tailored for Alzheimer's disease (AD) patients and their caregivers.
MemoryCompanion manifests a personalized caregiving paradigm, fostering interactions via voice-cloning and talking-face mechanisms.
Our methodology, grounded in its innovative design, addresses both the caregiving and technological challenges intrinsic to this domain.
arXiv Detail & Related papers (2023-11-20T19:41:50Z) - Chatbots as social companions: How people perceive consciousness, human likeness, and social health benefits in machines [0.0]
We studied people who regularly used companion chatbots and people who did not use them.
Contrary to expectations, companion users indicated that these relationships were beneficial to their social health.
We found the opposite: perceiving companion chatbots as more conscious and humanlike correlated with more positive opinions and more pronounced social health benefits.
arXiv Detail & Related papers (2023-11-17T15:53:59Z) - Development and Evaluation of Three Chatbots for Postpartum Mood and
Anxiety Disorders [31.018188794627378]
We develop three chatbots to provide context-specific empathetic support to postpartum caregivers.
We present and evaluate the performance of our chatbots using both machine-based metrics and human-based questionnaires.
We conclude by discussing practical benefits of rule-based vs. generative models for supporting individuals with mental health challenges.
arXiv Detail & Related papers (2023-08-14T18:52:03Z) - LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts [4.6071451559137175]
Low self-esteem and interpersonal needs have a major impact on depression and suicide attempts.
Individuals seek social connectedness on social media to boost and alleviate their loneliness.
We introduce a psychology-grounded and expertly annotated dataset, LoST: Low Self esTeem, to study and detect low self-esteem on Reddit.
arXiv Detail & Related papers (2023-06-08T23:52:35Z) - LonXplain: Lonesomeness as a Consequence of Mental Disturbance in Reddit
Posts [0.41998444721319217]
Social media is a potential source of information that infers latent mental states through Natural Language Processing (NLP)
Existing literature on psychological theories points to loneliness as the major consequence of interpersonal risk factors.
We formulate lonesomeness detection in social media posts as an explainable binary classification problem.
arXiv Detail & Related papers (2023-05-30T04:21:24Z) - ChatGPT for Us: Preserving Data Privacy in ChatGPT via Dialogue Text
Ambiguation to Expand Mental Health Care Delivery [52.73936514734762]
ChatGPT has gained popularity for its ability to generate human-like dialogue.
Data-sensitive domains face challenges in using ChatGPT due to privacy and data-ownership concerns.
We propose a text ambiguation framework that preserves user privacy.
arXiv Detail & Related papers (2023-05-19T02:09:52Z) - Mental Illness Classification on Social Media Texts using Deep Learning
and Transfer Learning [55.653944436488786]
According to the World health organization (WHO), approximately 450 million people are affected.
Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD.
This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
arXiv Detail & Related papers (2022-07-03T11:33:52Z) - Assessing the Severity of Health States based on Social Media Posts [62.52087340582502]
We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state.
The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
arXiv Detail & Related papers (2020-09-21T03:45:14Z) - You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation.
Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
arXiv Detail & Related papers (2020-04-11T12:51:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.