A Robotic Companion for Psychological Well-being

Jeong, Sooyeon; Aymerich-Franch, Laura; Alghowinem, Sharifa; Picard, Rosalind W.; Breazeal, Cynthia; Park, Hae Won

doi:10.1145/3568162.3578625

Cited by 16 publications

(3 citation statements)

References 37 publications

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“…With regards to interactions that require health data or data that could be used for healthcare applications, older adults expressed a need for expertise and how not trusting the robot’s expertise could make the interaction untrustworthy, stressing the need for researchers to explore if the robot should take the role of an expert, how to design transparent interactions on the level of the robot’s expertise, and how the role the healthcare team takes in a network with a social robot as a tool ( Cresswell et al, 2018 ; Felzmann et al, 2019 ; Aymerich-Franch and Ferrer, 2022 ). Developments in customization and personalization for these systems hold promise for older adults to gain the most benefits, especially as these systems can be tailored to older adults’ preferred exercise styles, communications styles, and other preferences supporting more custom engagements and support ( Umbrico et al, 2020 ; Coghlan et al, 2021 ; Khosla et al, 2021 ; Jeong et al, 2023 ). This approach could also help solve the incongruencies that may exist between various research studies.…”

Section: Discussionmentioning

confidence: 99%

Promising directions for human-robot interactions defined by older adults

Ostrowski,

Zhang,

Breazeal

et al. 2024

Front. Robot. AI

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Introduction: Older adults are engaging more and more with voice-based agent and social robot technologies, and roboticists are increasingly designing interactions for these systems with older adults in mind. Older adults are often not included in these design processes, yet there are many opportunities for older adults to collaborate with design teams to design future robot interactions and help guide directions for robot development.Methods: Through a year-long co-design project, we collaborated with 28 older adults to understand the key focus areas that older adults see promise in for older adult-robot interaction in their everyday lives and how they would like these interactions to be designed. This paper describes and explores the robot-interaction guidelines and future directions identified by older adults, specifically investigating the change and trajectory of these guidelines through the course of the co-design process from the initial interview to the design guideline generation session to the final interview. Results were analyzed through an adapted ethnographic decision tree modeling approach to understand older adults’ decision making surrounding the various focus areas and guidelines for social robots.Results: Overall, over the course of the co-design process between the beginning and end, older adults developed a better understanding of the robot that translated to them being more certain of their attitudes of how they would like a robot to engage with them in their lives. Older adults were more accepting of transactional functions such as reminders and scheduling and less open to functions that would involve sharing sensitive information and tracking and/or monitoring of them, expressing concerns around surveillance. There was some promise in robot interactions for connecting with others, body signal monitoring, and emotional wellness, though older adults brought up concerns around autonomy, privacy, and naturalness of the interaction with a robot that need to be further explored.Discussion: This work provides guidance for future interaction development for robots that are being designed to interact with older adults and highlights areas that need to be further investigated with older adults to understand how best to design for user concerns.

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Section: Discussionmentioning

confidence: 99%

Promising directions for human-robot interactions defined by older adults

Ostrowski,

Zhang,

Breazeal

et al. 2024

Front. Robot. AI

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“…We show the importance of framing in interactions with stories, as a one-sentence disclosure of the author significantly decreased empathy. This finding might be in tension with systems that rely on empathy for efficacy, such as in persuasive technologies that use bond with the AI to improve mental wellness outcomes [11], [38]. However, the empathy and transparency trade-off might not be mutually exclusive, as transparency can breed trust, which also influences interaction.…”

Section: Ethical Considerations and Implications In Mental Healthmentioning

confidence: 99%

Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study (Preprint)

Shen,

DiPaola,

Ali

et al. 2024

Preprint

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BACKGROUND Empathy is a driving force in our connection to others, our mental wellbeing, and resilience to challenges. With the rise of generative AI systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds towards stories from human vs AI narrators and how user emotions might change when the author of a story is made transparent to users. OBJECTIVE We aim to understand how empathy shifts across human-written vs AI-written stories, and how these findings inform ethical implications and human-centered design of using mental health chatbots as objects of empathy. METHODS We conduct crowd-sourced studies with N=985 participants who each write a personal story and then rate empathy towards 2 retrieved stories, where one is written by a language model, and another is written by a human. Our studies vary transparency around whether a story is written by a human or an AI to see how transparency affects empathy towards the narrator. We conduct mixed-methods analyses with both quantitative and qualitative approaches to understand how and why transparency affects empathy towards human vs AI storytellers. RESULTS We find that participants consistently and significantly empathize with human-written over machine-written stories in almost all conditions, regardless of whether they are aware that an AI wrote the story (P<.001). We also find that participants reported a greater willingness to empathize with AI-written stories if there is transparency about the story author (P<.001). CONCLUSIONS Our work sheds light on how empathy towards AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of artificial social support or mental health chatbots that are intended to evoke empathetic reactions.

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“…Within the HRI literature, robotic well-being coaches have been recently investigated with the aim of promoting mental well-being in homes [9], university accommodations [10], and workplaces [4], [11]. However, the appropriateness of these robotic coaches' language, generated using LLMs, have not been analyzed.…”

Section: Introductionmentioning

confidence: 99%

Robotic Mental Well-being Coaches for the Workplace

Spitale

Axelsson

Güneş

2023

Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction

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Recently, several works have explored if and how robotic coaches can promote and maintain mental well-being in different settings. However, findings from these studies revealed that these robotic coaches are not ready to be used and deployed in real-world settings due to several limitations that span from technological challenges to coaching success. To overcome these challenges, this paper presents VITA, a novel multi-modal LLM-based system that allows robotic coaches to autonomously adapt to the coachee's multi-modal behaviours (facial valence and speech duration) and deliver coaching exercises in order to promote mental well-being in adults. We identified five objectives that correspond to the challenges in the recent literature, and we show how the VITA system addresses these via experimental validations that include one in-lab pilot study (𝑁 = 4) that enabled us to test different robotic coach configurations (pre-scripted, generic, and adaptive models) and inform its design for using it in the real world, and one real-world study (𝑁 = 17) conducted in a workplace over 4 weeks. Our results show that: (i) coachees perceived the VITA adaptive and generic configurations more positively than the pre-scripted one, and they felt understood and heard by the adaptive robotic coach, (ii) the VITA adaptive robotic coach kept learning successfully by personalising to each coachee over time and did not detect any interaction ruptures during the coaching, (iii) coachees had significant mental well-being improvements via the VITA-based robotic coach practice. The code for the VITA system is openly available via https://github.com/Cambridge-AFAR/VITA-system. CCS Concepts: • Human-centered computing → Empirical studies in HCI ; HCI theory, concepts and models.

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A Robotic Companion for Psychological Well-being

Cited by 16 publications

References 37 publications

Promising directions for human-robot interactions defined by older adults

Promising directions for human-robot interactions defined by older adults

Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study (Preprint)

Robotic Mental Well-being Coaches for the Workplace

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