Can proactive behavior turn chatterbots into conversational agents?

L'Abbate, Marcello; Thiel, Ulrich; Kamps, Thomas

doi:10.1109/iat.2005.49

Cited by 13 publications

(3 citation statements)

References 4 publications

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“…Although chatbot communication has its factual and linguistic limitations-as pointed out by respondents and many authors (Duncker, 2020;Fryer et al, 2019;L'Abbate et al, 2005)-it is the conversation that establishes the basic notion of the chatbot as a specific entity and not just one of many educational objects. Although the respondents compare the chatbot to a textbook or a worksheet, they endow it with entirely unexpected qualities of being interactive and fluent in dialogue, and able to motivate, give feedback, lead to reflection, ask questions, etc.…”

Section: Discussionmentioning

confidence: 99%

Perceptions of the Design and Use Chatbots for Educational Purposes: A Dialogue Partner

Cerny

2023

JEO

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This study aims to analyze how professionals preparing to use technology in education reflect on the phenomenon of chatbots as learning objects. The study is divided into two parts. For the first part of the study, 17 studies indexed in the Web of Science database that focus on using chatbots in education were selected for a systematic review. From this study, six discourses were identified: a Chatbot is not human; a Chatbot is developed iteratively; a Chatbot is a solution to a specific educational problem; a Chatbot is a technological issue; a Chatbot is constituted by narrative and a distinct "psychology"; and a Chatbot is always available with the correct information. In the second part of the study, the responses of 29 respondents (12 teaching librarians and 17 students of the Educational Technology course) to questions about chatbots were processed by qualitative analysis. The analysis used the discourses obtained from the review study and examined their specific perception among this particular group. A key result was that even though the respondents worked with three chatbots without AI, they mentioned similar characteristics in their evaluation that they would apply to a live teacher. Thus, they understand chatbots as part of their learning environment without differentiating between living and nonliving systems.

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Section: Discussionmentioning

confidence: 99%

Perceptions of the Design and Use Chatbots for Educational Purposes: A Dialogue Partner

Cerny

2023

JEO

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“…All conventional chatbot's communication proficiency is immoderately intransigent. As per it, the user can be responded with the answer only if there is existence of some matching pattern amongst the divulgence from user and predefined communication sequence repertoire inside the plinth of knowledge [36]. Traditional chatbots deficits in the instinctive capacity like that of intellectual beings for perceiving the elucidation, associations and potential abilities afar from its own inventory extend.…”

Section: A Definition Of Chatbotmentioning

confidence: 99%

Chatbots Employing Deep Learning for Big Data

Verma*¹,

Jyoti²

2019

IJITEE

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With the evolution of artificial intelligence to deep learning, the age of perspicacious machines has pioneered that can even mimic as a human. A Conversational software agent is one of the best-suited examples of such intuitive machines which are also commonly known as chatbot actuated with natural language processing. The paper enlisted some existing popular chatbots along with their details, technical specifications, and functionalities. Research shows that most of the customers have experienced penurious service. Also, the inception of meaningful cum instructive feedback endure a demanding and exigent assignment as enactment for chatbots builtout reckon mostly upon templates and hand-written rules. Current chatbot models lack in generating required responses and thus contradict the quality conversation. So involving deep learning amongst these models can overcome this lack and can fill up the paucity with deep neural networks. Some of the deep Neural networks utilized for this till now are Stacked Auto-Encoder, sparse auto-encoders, predictive sparse and denoising auto-encoders. But these DNN are unable to handle big data involving large amounts of heterogeneous data. While Tensor Auto Encoder which overcomes this drawback is time-consuming. This paper has proposed the Chatbot to handle the big data in a manageable time.

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“…Rule-based systems can produce surprisingly convincing conversations, as the wellknown Eliza proved (Weizenbaum, 1966). Nowadays, there are a myriad of conversational agents (also known as chatterbots) available (for example, see (L'Abbate et al, 2005;Bass, 2005)), each with their own advantages and disadvantages. We based our work on the well-known A.L.I.C.E.…”

Section: Related Workmentioning

confidence: 99%

An Embodied Conversational Agent With Asperger Syndrome

2011

Proceedings of the 3rd International Conference on Computer Supported Education

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We discuss the development of an embodied conversational agent with Asperger-like communication skills. The agent was developed for use in educational software in a virtual environment specifically aimed at autism spectrum disorder software. We describe the design and implementation of the agent, and pay particular attention to the interaction between emotion, personality and social context. A 3D demonstration shows the typical output to conform to Asperger-like answers, with corresponding emotional responses.

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Can proactive behavior turn chatterbots into conversational agents?

Cited by 13 publications

References 4 publications

Perceptions of the Design and Use Chatbots for Educational Purposes: A Dialogue Partner

Perceptions of the Design and Use Chatbots for Educational Purposes: A Dialogue Partner

Chatbots Employing Deep Learning for Big Data

An Embodied Conversational Agent With Asperger Syndrome

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