1,123 research outputs found
Interacting with a Chatbot-Based Advising System: Understanding the Effect of Chatbot Personality and User Gender on Behavior
Chatbots with personality have been shown to affect engagement and user subjective satisfaction. Yet, the design of most chatbots focuses on functionality and accuracy rather than an interpersonal communication style. Existing studies on personality-imbued chatbots have mostly assessed the effect of chatbot personality on user preference and satisfaction. However, the influence of chatbot personality on behavioral qualities, such as users’ trust, engagement, and perceived authenticity of the chatbots, is largely unexplored. To bridge this gap, this study contributes: (1) A detailed design of a personality-imbued chatbot used in academic advising. (2) Empirical findings of an experiment with students who interacted with three different versions of the chatbot. Each version, vetted by psychology experts, represents one of the three dominant traits, agreeableness, conscientiousness, and extraversion. The experiment focused on the effect of chatbot personality on trust, authenticity, engagement, and intention to use the chatbot. Furthermore, we assessed whether gender plays a role in students’ perception of the personality-imbued chatbots. Our findings show a positive impact of chatbot personality on perceived chatbot authenticity and intended engagement, while student gender does not play a significant role in the students’ perception of chatbots
Building Decision Adviser Bots
This overview article explores the prospects and promises of new technologies for developing conversational software to aid, assist and advise people in personal and organizational decision situations. The quest for conversational decision advisers began in the 1970s with the development of interactive, computing systems like the Hewlett-Packard 2000 Access Time- Share systems. With the advent of Cloud-based, Artificial Intelligence development environments, the capabilities needed to develop conversational software are increasingly available and easy to use. Hence, it is feasible to develop decision adviser (DA) bots and the bots are easier to deploy. Bots can be built for action taking and for question and answer dialogs. DA bots can be deployed for use in both structured and semi-structured decision situations. DA bots can perform increasingly complex tasks. Overall, more exploratory design science research is needed to improve our understanding of the design, development, and deployment of DA bots for use by managers, customers, and clients
Interacting with educational chatbots: A systematic review
Chatbots hold the promise of revolutionizing education by engaging learners, personalizing learning activities, supporting educators, and developing deep insight into learners’ behavior. However, there is a lack of studies that analyze the recent evidence-based chatbot-learner interaction design techniques applied in education. This study presents a systematic review of 36 papers to understand, compare, and reflect on recent attempts to utilize chatbots in education using seven dimensions: educational field, platform, design principles, the role of chatbots, interaction styles, evidence, and limitations. The results show that the chatbots were mainly designed on a web platform to teach computer science, language, general education, and a few other fields such as engineering and mathematics. Further, more than half of the chatbots were used as teaching agents, while more than a third were peer agents. Most of the chatbots used a predetermined conversational path, and more than a quarter utilized a personalized learning approach that catered to students’ learning needs, while other chatbots used experiential and collaborative learning besides other design principles. Moreover, more than a third of the chatbots were evaluated with experiments, and the results primarily point to improved learning and subjective satisfaction. Challenges and limitations include inadequate or insufficient dataset training and a lack of reliance on usability heuristics. Future studies should explore the effect of chatbot personality and localization on subjective satisfaction and learning effectiveness
Generating socially appropriate tutorial dialog
Analysis of student-tutor coaching dialogs suggest that good human tutors attend to and attempt to influence the motivational state of learners. Moreover, they are sensitive to the social face of the learner, and seek to mitigate the potential face threat of their comments. This paper describes a dialog generator for pedagogical agents that takes motivation and face threat factors into account. This enables the agent to interact with learners in a socially appropriate fashion, and foster intrinsic motivation on the part of the learner, which in turn may lead to more positive learner affective states
Facilitating Natural Conversational Agent Interactions: Lessons from a Deception Experiment
This study reports the results of a laboratory experiment exploring interactions between humans and a conversational agent. Using the ChatScript language, we created a chat bot that asked participants to describe a series of images. The two objectives of this study were (1) to analyze the impact of dynamic responses on participants’ perceptions of the conversational agent, and (2) to explore behavioral changes in interactions with the chat bot (i.e. response latency and pauses) when participants engaged in deception. We discovered that a chat bot that provides adaptive responses based on the participant’s input dramatically increases the perceived humanness and engagement of the conversational agent. Deceivers interacting with a dynamic chat bot exhibited consistent response latencies and pause lengths while deceivers with a static chat bot exhibited longer response latencies and pause lengths. These results give new insights on social interactions with computer agents during truthful and deceptive interactions
Facilitating Natural Conversational Agent Interactions: Lessons from a Deception Experiment
This study reports the results of a laboratory experiment exploring interactions between humans and a conversational agent. Using the ChatScript language, we created a chat bot that asked participants to describe a series of images. The two objectives of this study were (1) to analyze the impact of dynamic responses on participants’ perceptions of the conversational agent, and (2) to explore behavioral changes in interactions with the chat bot (i.e. response latency and pauses) when participants engaged in deception. We discovered that a chat bot that provides adaptive responses based on the participant’s input dramatically increases the perceived humanness and engagement of the conversational agent. Deceivers interacting with a dynamic chat bot exhibited consistent response latencies and pause lengths while deceivers with a static chat bot exhibited longer response latencies and pause lengths. These results give new insights on social interactions with computer agents during truthful and deceptive interactions
CYBERNETIFICATION I: Cybernetics Feedback Netgraft in Architecture
During the last decades, architecture has changed its role from fetishizing and fertilizing objectification and objects alike towards glamorising the processing of relations, observations and materialization of the 'objectile'. Steering the design process in contemporary computational architecture through and with a variety of dynamic, interconnecting agents affords re-framing, reviewing, and re-designing prescribed patterns of creating architecture. It critically encourages to examine the concept of feedback beyond the beloved evolutionary algorithm, which presents a technical rather than architectural cultural calculus. ‚CYBERNETICS FEEDBACK NETGRAFT’ proposes cybernetic principles as blueprint or genotype for computational architecture. Such principles allow for a systemic continuation of re-programming the architectural culture currently at stake. The forthcoming observation hovers between theories and meta-models. It argues that the possibilities for design increase through digitization and digitalization. In this respect, the chapter refers to Ross Ashby’s Law of Requisite Variety (Ashby 1957) on one hand and to emergence through digital self-organization on the other. (DeLanda 2011; Johnson 2001). The text offers a critic of the bio-digital and too fantastic (Werner 2014, pp.229-230). The author is starting to suggest an ‘architectural laboratorium of and for computational theory’ built on a systemic approach to emergence and the unforeseen - nourished by cybernetic principles: a cybernetification that eventually can govern and feed back into practice and the art of architecture.In den letzten Jahrzehnten hat die Architektur ihre Rolle verändert; von 'fetishizing' von Objektivierung und Objekten, hin zu einer Veredelung von Beziehungen, Beobachtungen und Materialisierung des 'objectile'. Lenkung Design-Prozess in der zeitgenössischen Computational Architecture durch und mit einer Vielzahl von dynamischen, verbindenden Agenten offeriert ein Re-Framing und Überprüfung von Entwurfsstrategien von vorgeschriebenen Mustern zur Gestaltung von Architekturen. Dies fordert kritisch dazu auf, das Konzept des Feedbacks jenseits der geliebten evolutionärer Algorithmus, der eher ein technisches als ein architektonisches Kulturkalkül. ,CYBERNETICS FEEDBACK NETGRAFT' schlägt vor Kybernetische Prinzipien als Blaupause oder Genotyp für Computational Architecture. Solche Prinzipien ermöglichen eine systematische Fortsetzung der Neuprogrammierung der derzeit auf dem Spiel steht. Die bevorstehende Beobachtung schwebt zwischen Theorien und Metamodellen. In dieser Hinsicht bezieht sich der Text auf Ross Ashby's 'Law of Requisite Variety' (Ashby 1957) einerseits und durch digitale Selbstorganisation auftauchen. (DeLandas) 2011; Johnson 2001). Die Autorin beginnt, ein 'architektonisches Laboratorium' über und für die Computertheorie aufgebaut 'auf einem systemischen Ansatz zu konstruieren
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