How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Design

Chaves, Ana Paula; Gerosa, Marco Aurélio

doi:10.1080/10447318.2020.1841438

Cited by 336 publications

(173 citation statements)

References 109 publications

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“…These frameworks tackle primarily natural language and speech processing, providing little support to the management of conversation coordination, user profiling, and user experience. Beyond these commercial solutions, further research has been performed regarding human-computer interaction approaches that enrich chatbots with social characteristics in order to cope with frustration and dissatisfaction [18]. The human factor in this type of interaction is not negligible, given the differences in perception [19] and emotional state [20] that can lead to entirely different paradigms for designing a conversational agent and evaluating it [21].…”

Section: Hmi and Chatbotsmentioning

confidence: 99%

EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots

et al. 2021

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Context. Asynchronous messaging is increasingly used to support human–machine interactions, generally implemented through chatbots. Such virtual entities assist the users in activities of different kinds (e.g., work, leisure, and health-related) and are becoming ingrained into humans’ habits due to factors including (i) the availability of mobile devices such as smartphones and tablets, (ii) the increasingly engaging nature of chatbot interactions, (iii) the release of dedicated APIs from messaging platforms, and (iv) increasingly complex AI-based mechanisms to power the bots’ behaviors. Nevertheless, most of the modern chatbots rely on state machines (implementing conversational rules) and one-fits-all approaches, neglecting personalization, data-stream privacy management, multi-topic management/interconnection, and multimodal interactions. Objective. This work addresses the challenges above through an agent-based framework for chatbot development named EREBOTS. Methods. The foundations of the framework are based on the implementation of (i) multi-front-end connectors and interfaces (i.e., Telegram, dedicated App, and web interface), (ii) enabling the configuration of multi-scenario behaviors (i.e., preventive physical conditioning, smoking cessation, and support for breast-cancer survivors), (iii) online learning, (iv) personalized conversations and recommendations (i.e., mood boost, anti-craving persuasion, and balance-preserving physical exercises), and (v) responsive multi-device monitoring interface (i.e., doctor and admin). Results. EREBOTS has been tested in the context of physical balance preservation in social confinement times (due to the ongoing pandemic). Thirteen individuals characterized by diverse age, gender, and country distribution have actively participated in the experimentation, reporting advancements in the physical balance and overall satisfaction of the interaction and exercises’ variety they have been proposed.

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Section: Hmi and Chatbotsmentioning

confidence: 99%

EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots

et al. 2021

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“…Similarly, [41] observed that security messages that appeal to an individual's motivational needs are more likely to elicit secure responses than messages that elicit fear. Yet, to date, there is only limited research that has specifically looked into the effect of SDT on the chatbot design [9,45]. For instance, Chaves and colleagues [9]utilized a meta-analytical method to examine how social characteristics of a conversational chatbot can benefit user interactions with the chatbot.…”

Section: Self-determination Theorymentioning

confidence: 99%

“…Yet, to date, there is only limited research that has specifically looked into the effect of SDT on the chatbot design [9,45]. For instance, Chaves and colleagues [9]utilized a meta-analytical method to examine how social characteristics of a conversational chatbot can benefit user interactions with the chatbot. Nguyen et al [45] used SDT to study the differences in satisfaction with a website and with a chatbot.…”

Section: Self-determination Theorymentioning

confidence: 99%

Design Foundations for AI Assisted Decision Making: A Self Determination Theory Approach

Vreede¹,

Raghavan²,

Vreede³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Progress of technology and processing power has enabled the advent of sophisticated technology including Artificial Intelligence (AI) agents. AI agents have penetrated society in many forms including conversation agents or chatbots. As these chatbots have a social component to them, is it critical to evaluate the social aspects of their design and its impact on user outcomes. This study employs Social Determination Theory to examine the effect of the three motivational needs on user interaction outcome variables of a decision-making chatbot. Specifically, this study looks at the influence of relatedness, competency, and autonomy on user satisfaction, engagement, decision efficiency, and decision accuracy. A carefully designed experiment revealed that all three needs are important for user satisfaction and engagement while competency and autonomy is associated with decision accuracy. These findings highlight the importance of considering psychological constructs during AI design. Our findings also offer useful implications for AI designers and organizations that plan on using AI assisted chatbots to improve decision-making efforts.

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“…On the other hand, chatbots are changing the patterns of how humans interact with computers today. [4] Chatbots are conversational software systems based on artificial intelligence (AI) techniques with which users interact through natural language dialogue, by text or voice. [5] Today, the growth of chatbots in customer service, education, health, travel, entertainment, personal assistance and other areas, has changed how companies engage with their customers, students with learning platforms, tourists with travel systems and many other applications.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the benefits, there is a lot of issues. Recent research shows that chatbots still fail to meet users' expectations because of the lack of social capabilities [4]. Also, people are afraid of chatbot could use some of their private data for unknown purposes.…”

Section: Introductionmentioning

confidence: 99%

Conversational Survey Chatbot: User Experience and Perception

Njeguš¹

2021

Proceedings of the International Scientific Conference - Sinteza 2021

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How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Design

Cited by 336 publications

References 109 publications

EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots

EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots

Design Foundations for AI Assisted Decision Making: A Self Determination Theory Approach

Conversational Survey Chatbot: User Experience and Perception

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