Chatbots: Are they Really Useful?

By Bayan Abu Shawar, Eric Atwell. Ldv Forum, 2007

601 citations recorded by the source corpus.

Chatbots are computer programs that interact with users using natural lan- guages. This technology started in the 1960’s; the aim was to see if chatbot systems could fool users that they were real humans. However, chatbot sys- tems are not only built to mimic human conversation, and entertain users. In this paper, we investigate other applications where chatbots could be useful such as education, information retrival, business, and e-commerce. A range of chatbots with useful applications, including several based on the ALICE/AIML architecture, are presented in this paper.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/8d8284bfba7ebcb4e2575d864ec7c16ea6a168f0

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Citations in the corpus

Teacher support and student motivation to learn with Artificial Intelligence (AI) based chatbot (2023, neutral): “Students with stronger expertise may perceive stronger needs support from chatbots than those with weaker expertise, resulting in greater intrinsic motivation to learn with chatbots alone (Shawar & Atwell, 2007).”
Towards Designing Cooperative and Social Conversational Agents for Customer Service (2017, methodology): “…attempts to provide overly humanized CA representations (e.g., avatars) and/or to perfectly imitate human conversation (Von Der Pütten et al. 2010; Shawar and Atwell 2007) led to unrealistic user expectations that often turned into frustration (Berg 2013; Knijnenburg and Willemsen 2016).”
How Do AI-driven Chatbots Impact User Experience? Examining Gratifications, Perceived Privacy Risk, Satisfaction, Loyalty, and Continued Use (2020, methodology): “AI-powered smart media such as chatbots were initially designed for entertainment by using simple machine languages to respond to user inputs online (Atwell & Shawar, 2007).”
"Hey Alexa, What's Up?": A Mixed-Methods Studies of In-Home Conversational Agent Usage (2018, background): “Chatbots that use natural language processing to provide appropriate human responses in the context of a dialogue have been shown to influence the behavior of the user [4].”
A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version (2015, methodology): “Dialogue systems, also known as interactive conversational agents, virtual agents or sometimes chatterbots, are useful in a wide range of applications ranging from technical support services to language learning tools and entertainment (Young et al., 2013; Shawar and Atwell, 2007b).”
Designing a Chatbot for diabetic patients (2009, background): “open-source software technology [1].”
Emotionally-Aware Chatbots: A Survey (2019, background): “[51] provides a long history of chatbots technology development.”
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation (2016, background): “Dialogue systems have been developed for applications ranging from technical support to language learning and entertainment [35, 30].”
Hierarchical Neural Network Generative Models for Movie Dialogues (2015, background): “Dialogue systems, also known as interactive conversational agents, virtual agents and sometimes chatterbots, are used in a wide set of applications ranging from technical support services to language learning tools and entertainment (Young et al., 2013; Shawar and Atwell, 2007).”
Real conversations with artificial intelligence: A comparison between human-human online conversations and human-chatbot conversations (2015, background): “…of new chatbot architectures and technologies (e.g., Ultra Hal, ALICE, Jabberwacky, Cleverbot) have arisen recently, each attempting to simulate natural human language more accurately and thoroughly (Carpenter, n.d. a; Shawar & Atwell, 2007; Wallace, Tomabechi, & Aimless, 2003; Zabaware., n.d.).”
Transforming the communication between citizens and government through AI-guided chatbots (2019, background): “…sector nowadays concerns the utilization of applications that interact with users in a conversational format and mimic human conversation, known as “conversational agents”, “chatbots” or simply “bots” (Abu Shawar & Atwell, 2007; Klopfenstein, Delpriori, Malatini, & Bogliolo, 2017; Poola, 2017).”
How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Design (2019, background): “urveys discuss chatbot’s urgency [Dale 2016; Pereira et al. 2016] and their potential application for particular domains, which include education [Deryugina 2010; Rubin et al. 2010; Satu et al. 2015; Shawar and Atwell 2007; Winkler and Söllner 2018], business [Deryugina 2010; Shawar and Atwell 2007]”
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models (2015, background): “Dialogue systems, also known as interactive conversational agents, virtual agents and sometimes chatterbots, are used in a wide set of applications ranging from technical support services to language learning tools and entertainment (Young et al. 2013; Shawar and Atwell 2007).”
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach (2018, background): “The ﬁrst response model is the chatbot Alice (Wallace, 2009; Shawar & Atwell, 2007), which generates responses by using thousands of template rules.”
A Deep Reinforcement Learning Chatbot (2017, methodology): “Alicebot : Alicebot uses a set of AIML (artiﬁcial intelligence markup language) templates to produce a response given the dialogue history and user utterance (Wallace 2009, Shawar & Atwell 2007).”