Real-time Embodied Agent Adaptation

This paper reports on initial investigation of two emerging technologies, FaceFX and Smartbody, capable of creating life-like animations for embodied conversational agents (ECAs) such as the AVATAR agent. Real-time rendering and animation generation technologies can enable rapid adaptation of ECAs to changing circumstances. The benefits of each package are discussed

When Do We Need a Human? Anthropomorphic Design and Trustworthiness of Conversational Agents

Conversational agents interact with users via the most natural interface: human language. A prerequisite for their successful diffusion across use cases is user trust. Following extant research, it is reasonable to assume that increasing the human-likeness of conversational agents represents an effective trust-inducing design strategy. The present article challenges this assumption by considering an opposing theoretical perspective on the human-agent trust-relationship. Based on an extensive review of the two conflicting theoretical positions and related empirical findings, we posit that the agent substitution type (human-like vs. computer-like) represents a situational determinant on the trust-inducing effect of anthropomorphic design. We hypothesize that this is caused by user expectations and beliefs. A multi-method approach is proposed to validate our research model and to understand the cognitive processes triggered by anthropomorphic cues in varying situations. By explaining the identified theoretical contradiction and providing design suggestions, we derive meaningful insights for both researchers and practitioners

Teaching the Machine: How People Teach Algorithms to Replace People

Automation of work via artificial intelligence is becoming a significant issue for societies. This ethnographic study presents a case study that details the process that takes place in order to teach the algorithms that will eventually replace the need for human effort. The author was employed as a data quality analyst and a part of a process where algorithms were taught to generate better content and eventually replace human content creators. The study proposes a three-stage process of automation where the relationship between the humans and algorithms progresses from symbiotic to cannibalistic: the first phase is the commencement phase, where the human employees and algorithms live in symbiosis, reliant on each other. The symbiosis is followed by the expansion phase, where more work is delegated to the algorithms, and the final phase is the automaton phase, where human employees are no longer needed

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

Designing a conversational requirements elicitation system for end-users

[Context] Digital transformation impacts an ever-increasing degree of everyone’s business and private life. It is imperative to incorporate a wide audience of user requirements in the development process to design successful information systems (IS). Hence, requirements elicitation (RE) is increasingly performed by end-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 end-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] The presented dissertation project utilizes design science research to develop a requirements 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 interviewer’s capability to rephrase questions and provide assistance in the process and allows users to converse in their natural language. Furthermore, the tool will assist requirements analysts with the subsequent aggregation and analysis of collected data. [Contribution] The dissertation project makes a practical contribution in the form of a ready-to-use system for wide audience end-user RE and subsequent analysis utilizing laddering as cognitive elicitation technique. A theoretical contribution is provided by developing a design theory for the application of conversational agents for RE, including the laboratory and field evaluation of design principles

Design Science Research Modes in Human-Computer Interaction Projects

In this editorial, we introduce the special issue on design science research in human-computer interaction with four papers extended from the 2020 European Conference on Information Systems and propose a conceptual model for such research projects. Research in the interdisciplinary human-computer interaction (HCI) discipline advances knowledge of how humans interact with technologies, systems, information, and work structures. Design science research (DSR) methods support three distinct modes in HCI projects. In the interior mode, researchers build and evaluate novel technical solutions with a focus on improved system interfaces to support effective human use. Next, in the exterior mode, researchers build and evaluate novel behavioral solutions with a process focus on interactions that increase human capabilities. Lastly, in the gestalt mode, researchers build and evaluate novel composite solutions that improve synergies between technologies and human behaviors. We pose a comprehensive model for identifying the DSR modes of HCI research with related artifacts, evaluation techniques, design theories, and research impacts

Towards a Taxonomy of Platforms for Conversational Agent Design

Software that interacts with its users through natural language, so-called conversational agents (CAs), is permeating our lives with improving capabilities driven by advances in machine learning and natural language processing. For organizations, CAs have the potential to innovate and automate a variety of tasks and processes, for example in customer service or marketing and sales, yet successful design remains a major challenge. Over the last few years, a variety of platforms that offer different approaches and functionality for designing CAs have emerged. In this paper, we analyze 51 CA platforms to develop a taxonomy and empirically identify archetypes of platforms by means of a cluster analysis. Based on our analysis, we propose an extended taxonomy with eleven dimensions and three archetypes that contribute to existing work on CA design and can guide practitioners in the design of CA for their organizations

Trusting a Humanoid Robot : Exploring Personality and Trusting Effects in a Human-Robot Partnership

Research on trust between humans and machines has primarily investigated factors relating to environmental or system characteristics, largely neglecting individual differences that play an important role in human behavior and cognition. This study examines the role of the Big Five personality traits on trust in a partnership between a human user and a humanoid robot. A wizard of oz methodology was used in an experiment to simulate an artificially intelligent robot that could be leveraged as a partner to complete a life or death survival simulation. Eye-tracking was employed to measure system utilization and validated psychometric instruments were used to measure trust and personality traits. Results suggest that individuals scoring high on the openness personality trait may have greater trust in a humanoid robot partner than those with low scores in the openness personality dimension

The Influence of Conversational Agents on Socially Desirable Responding

Conversational agents (CAs) are becoming an increasingly common component in many information systems. The ubiquity of CAs in cell phones, entertainment systems, and messaging applications has led to a growing need to understand how design choices made when developing CAs influence user interactions. In this study, we explore the use case of CAs that gather potentially sensitive information from people-”for example, in a medical interview. Using a laboratory experiment, we examine the influence of CA responsiveness and embodiment on the answers people give in response to sensitive and non-sensitive questions. The results show that for sensitive questions, the responsiveness of the CA increased the social desirability of the responses given by participants