Pan: conversational agent for criminal investigations

We present an early prototype conversational agent (CA), called Pan, for retrieving information to support criminal investigations. Our approach tackles the issue of algorithmic transparency, which is critical in unpredictable, high risk, and high consequence domains. We present a novel method to flexibly model CA intentions and provide transparency of attributes that is underpinned with human recognition. We propose that Pan can be used for experimentation to probe analyst requirements and to evaluate the effectiveness of our explanation structure

Towards Gamified Conversational Agents for Self-Regulated Learning in Digital Education

Formal education like higher education oftentimes emphasized on strict non-digital setting. This approach can lead to issues during stressful times (e.g., Covid crisis) or when learners’ needs in general are not considered. Moreover, these times highlighted how important self-regulated learning is and how much this capability is lacking in our educational system. To address these issues, we follow an Action Design Research approach and develop a gamified conversational agent (CA) that considers the learners’ needs. We present our CA and conduct a first small-scale evaluation following a mixed-method approach. First results show that students universally liked a CA for self-regulated digital learning and many enjoyed the gamified experience which helped students to be motivated to learn. As next steps we will develop the next iteration of our CA and conduct a long-term field test at a university

Introducing conversational explanations as a novel response strategy to data breach incidents in digital commerce

In order to individualize and personalize digital services, an increasing number of e-commerce providers are exploiting abundant amounts of customer information. Alongside these positive effects, an inherent risk of compromise of customer information arises, resulting in data breaches. Compelled by regulations, companies are obliged to notify their customers. Previous literature indicates that different data breach response strategies can mitigate the negative effects of these security incidents. Drawing on data breach and conversational agent (CA) research, we theorize that the manner in which a data breach is communicated is equally relevant. We test our developed hypotheses in an online experiment (n=89). Our results show that explaining a data breach increases customer satisfaction. Simultaneously, we reveal that CAs lend themselves as a tool to positively influence this degree of explanation. Our work provides novel insights into the centrality of explanation in a data breach response and their positive correlation with CAs

Augmented Facilitation: Designing a multi-modal Conversational Agent for Group Ideation

Human facilitators face the challenge to structure and collect relevant insights from collaborative creative work sessions, which can suffer if they face a high workload. Hence, for effective value co-creation in organizational ideation we suggest an facilitation augmentation with a conversational agent (CA). CAs have the ability to support respective collaborative work by documenting and analyzing unstructured data. Following the design science research paradigm, and based on the literature about facilitation and human-AI collaboration, we derive design principles to develop a CA prototype that collects ideas from a group ideation session and displays them back in a structured (multi-modal) manner. We evaluate the CA by conducting four focus groups. Key findings show that the CA successfully distills and enriches information. Our study contributes to understanding the role of CA in augmenting facilitation and it provides guidance for practice on how to integrate these technologies in group meetings

Emoty: an Emotionally Sensitive Conversational Agent for People with Neurodevelopmental Disorders

Our research aims at exploiting the advances in conversational technology to support people with Neurodevelopmental Disorder (NDD). NDD is a group of conditions that are characterized by severe deficits in the cognitive, emotional and motor areas and produce severe impairments in communication and social functioning. This paper presents the design, technology and exploratory evaluation of Emoty, a spoken Conversational Agent (CA) created specifically for individuals with NDD. The goal of Emoty is to help these persons enhancing communication abilities related to emotional recognition and expression, which are fundamental in any form of human relationship. The system exploits emotion detection capabilities based on the semantics of the speech by calling the IBM Watson Tone Analyzer API and from the harmonic features of the audio thanks to an “all-of-us” Deep Learning model. The design and evaluation of Emoty are based on the close collaboration among computer engineers and specialists in NDD (psychologists, neurological doctors, educators)

Who’s Bad? – The Influence of Perceived Humanness on Users’ Intention to Complain about Conversational Agent Errors to Others

The perception of humanness in a conversational agent (CA) has been shown to strongly impact users’ processing and reaction to it. However, it is largely unclear how this perception of humanness influences users’ processing of errors and subsequent intention for negative word-of-mouth (WoM). In this context, we propose two pathways between perceived humanness and negative WoM: a cognitive pathway and an affective pathway. In a 2x2 online experiment with chatbots, we manipulated both the occurrence of errors and the degree of humanlike design. Our findings indicate that perceived humanness effects users\u27 intentions towards negative WoM through the cognitive pathway: users\u27 confirmation of expectations is increased by perceived humanness, reducing negative WoM intentions. However, it has no effect on users’ anger and frustration and does not interact with the effects of errors. For practice, our results indicate that adding humanlike design elements can be a means to reduce negative WoM

Adapting Feedback to Personality to Increase Motivation

Peer reviewedPostprin

LadderBot: A requirements self-elicitation system

Digital transformation impacts an ever-increasing amount of everyone’s business and private life. It is imperative to incorporate user requirements in the development process to design successful information systems (IS). Hence, requirements elicitation (RE) is increasingly performed by users that are novices at contributing requirements to IS development projects. [Objective] We need to develop RE systems that are capable of assisting a wide audience of users in communicating their needs and requirements. Prominent methods, such as elicitation interviews, are challenging to apply in such a context, as time and location constraints limit potential audiences. [Research Method] We present the prototypical self-elicitation system “LadderBot”. A conversational agent (CA) enables end-users to articulate needs and requirements on the grounds of the laddering method. The CA mimics a human (expert) interviewer’s capability to rephrase questions and provide assistance in the process. An experimental study is proposed to evaluate LadderBot against an established questionnaire-based laddering approach. [Contribution] This work-in-progress introduces the chatbot LadderBot as a tool to guide novice users during requirements self-elicitation using the laddering technique. Furthermore, we present the design of an experimental study and outline the next steps and a vision for the future

Trends, challenges and processes in conversational agent design: exploring practitioners’ views through semi-structured interviews

The aim of this study is to explore the challenges and experiences of conversational agent (CA) practitioners in order to highlight their practical needs and bring them into consideration within the scholarly sphere. A range of data scientists, conversational designers, executive managers and researchers shared their opinions and experiences through semi-structured interviews. They were asked about emerging trends, the challenges they face, and the design processes they follow when creating CAs. In terms of trends, findings included mixed feelings regarding no-code solutions and a desire for a separation of roles. The challenges mentioned included a lack of socio-technical tools and conversational archetypes. Finally, practitioners followed different design processes and did not use the design processes described in the academic literature. These findings were analyzed to establish links between practitioners’ insights and discussions in related literature. The goal of this analysis is to highlight research-practice gaps by synthesising five practitioner needs that are not currently being met. By highlighting these research-practice gaps and foregrounding the challenges and experiences of CA practitioners, we can begin to understand the extent to which emerging literature is influencing industrial settings and where more research is needed to better support CA practitioners in their work