Systematic Review: Trust-Building Factors and Implications for Conversational Agent Design

Objective: The need for digital tools in mental health is clear, with insufficient access to mental health services. Conversational agents, also known as chatbots or voice assistants, are digital tools capable of holding natural language conversations. Since our last review in 2018, many new conversational agents and research have emerged, and we aimed to reassess the conversational agent landscape in this updated systematic review. Methods: A systematic literature search was conducted in January 2020 using the PubMed, Embase, PsychINFO, and Cochrane databases. Studies included were those that involved a conversational agent assessing serious mental illness: major depressive disorder, schizophrenia spectrum disorders, bipolar disorder, or anxiety disorder. Results: Of the 247 references identified from selected databases, 7 studies met inclusion criteria. Overall, there were generally positive experiences with conversational agents in regard to diagnostic quality, therapeutic efficacy, or acceptability. There continues to be, however, a lack of standard measures that allow ease of comparison of studies in this space. There were several populations that lacked representation such as the pediatric population and those with schizophrenia or bipolar disorder. While comparing 2018 to 2020 research offers useful insight into changes and growth, the high degree of heterogeneity between all studies in this space makes direct comparison challenging. Conclusions: This review revealed few but generally positive outcomes regarding conversational agents’ diagnostic quality, therapeutic efficacy, and acceptability, which may augment mental health care. Despite this increase in research activity, there continues to be a lack of standard measures for evaluating conversational agents as well as several neglected populations. We recommend that the standardization of conversational agent studies should include patient adherence and engagement, therapeutic efficacy, and clinician perspectives.

Download Full-text

Are You Flirting, Objectifying or What? a Conversation Analysis of “you’re very sexy” Conversational Turn

Soshum Jurnal Sosial dan Humaniora ◽

10.31940/soshum.v10i3.1972 ◽

2020 ◽

Vol 10 (3) ◽

pp. 294-308

Author(s):

Kadek Ratih Dwi Oktarini ◽

Keyword(s):

Conversation Analysis ◽

Language Processing ◽

Fundamental Question ◽

Human Interaction ◽

Conversational Agent ◽

Dialogue System ◽

Agent Design ◽

Basic Concepts ◽

Mature Field ◽

Critical Components

Intent identification is one of the most critical components in conversational agent design. Conversational agent “is any dialogue system that not only conducts natural language processing but also responds automatically using human language.” (Conversational Agent, 2019). The crux of designing human-like conversational agent is to mimic how human understands another human and then responds “naturally”. The current study attempts to answer the fundamental question: how to model human processes of understanding another human? In order to answer that question, it starts from exploring some basic concepts relevant to intent identification from Conversation Analysis (CA). CA is a mature field that studies authentic human interaction. The basic concepts from CA are then synthesised into a model that potentially fit to existing framework and paradigm in conversational agent design, i.e. Natural Conversation Framework (NCF) and Intent-Entity-Context-Response (IECR) paradigm. Instead of using a made-up sentence, the model is then tested to an authentic conversational turn seksi sekali dirimu ‘you’re very sexy’. The test shows that the model is able to detect several possible intents contain in this authentic conversational turn. The model is also able to handle Conversational Indonesian and multi-modality. Considering the versatility of Conversation Analysis, in all likelihood the model will be able to handle any language and all kinds of modalities. Future study can be done to analyse more Conversational Indonesian data (to develop library of intent for Conversational Indonesian Language), as well as conversational data from different languages and conversational data containing diverse modalities.

Download Full-text

To trust or not to trust smart consumer products: a literature review of trust-building factors

Management Review Quarterly ◽

10.1007/s11301-019-00171-8 ◽

2019 ◽

Vol 70 (3) ◽

pp. 391-420 ◽

Cited By ~ 1

Author(s):

Oliver Michler ◽

Reinhold Decker ◽

Christian Stummer

Keyword(s):

Literature Review ◽

Consumer Products ◽

Trust Building ◽

Building Factors

Download Full-text

User experience with conversational agent: a systematic review of assessment methods

Behaviour and Information Technology ◽

10.1080/0144929x.2021.2001047 ◽

2021 ◽

pp. 1-11

Author(s):

Carla Tubin ◽

João Pedro Mazuco Rodriguez ◽

Ana Carolina Bertoletti de Marchi

Keyword(s):

Systematic Review ◽

User Experience ◽

Assessment Methods ◽

Conversational Agent

Download Full-text

Erratum to: Natural language scripting within conversational agent design

Applied Intelligence ◽

10.1007/s10489-013-0489-6 ◽

2013 ◽

Vol 40 (1) ◽

pp. 198-198

Author(s):

Karen O’Shea ◽

Keeley Crockett ◽

Zuhair Bandar ◽

James O’Shea

Keyword(s):

Natural Language ◽

Conversational Agent ◽

Agent Design

Download Full-text

Conversational agents in healthcare: a systematic review

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocy072 ◽

2018 ◽

Vol 25 (9) ◽

pp. 1248-1258 ◽

Cited By ~ 146

Author(s):

Liliana Laranjo ◽

Adam G Dunn ◽

Huong Ly Tong ◽

Ahmet Baki Kocaballi ◽

Jessica Chen ◽

...

Keyword(s):

Systematic Review ◽

Natural Language ◽

Depression Symptoms ◽

Conversational Agent ◽

Conversational Agents ◽

Cross Sectional ◽

Evaluation Measures ◽

Health Related ◽

Quasi Experimental ◽

Language Input

Abstract Objective Our objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes. Methods We searched PubMed, Embase, CINAHL, PsycInfo, and ACM Digital using a predefined search strategy. Studies were included if they focused on consumers or healthcare professionals; involved a conversational agent using any unconstrained natural language input; and reported evaluation measures resulting from user interaction with the system. Studies were screened by independent reviewers and Cohen’s kappa measured inter-coder agreement. Results The database search retrieved 1513 citations; 17 articles (14 different conversational agents) met the inclusion criteria. Dialogue management strategies were mostly finite-state and frame-based (6 and 7 conversational agents, respectively); agent-based strategies were present in one type of system. Two studies were randomized controlled trials (RCTs), 1 was cross-sectional, and the remaining were quasi-experimental. Half of the conversational agents supported consumers with health tasks such as self-care. The only RCT evaluating the efficacy of a conversational agent found a significant effect in reducing depression symptoms (effect size d = 0.44, p = .04). Patient safety was rarely evaluated in the included studies. Conclusions The use of conversational agents with unconstrained natural language input capabilities for health-related purposes is an emerging field of research, where the few published studies were mainly quasi-experimental, and rarely evaluated efficacy or safety. Future studies would benefit from more robust experimental designs and standardized reporting. Protocol Registration The protocol for this systematic review is registered at PROSPERO with the number CRD42017065917.

Download Full-text