Real conversations with artificial intelligence: A comparison between human-human online conversations and human-chatbot conversations

By Jennifer Hill, W. Randolph Ford, I. Farreras. 2015

771 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Human-Computer Interaction

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Citations in the corpus

Towards a Human-like Open-Domain Chatbot (2020, background): “Hill et al. (2015) describe differences in human behavior when talking to a chatbot.”
Recipes for Safety in Open-domain Chatbots (2020, background): “Hill et al. (2015) observed an almost 30-fold increase in profanity when humans talked to a chatbot (Clever-bot) compared to another human, while Lortie and Guitton (2011) showed that humans display more aggressiveness when believing that their (human) conversation partner is a bot.”
Bot-Adversarial Dialogue for Safe Conversational Agents (2021, background): “Numerous works have shown that humans speak differently with bots than with humans, with increases in profanity and aggressiveness associated with addressing a bot (Hill et al., 2015; Lortie and Guitton, 2011), which motivates the incorporation of human-bot dialogues into our safety framework.”
AI-based chatbots in customer service and their effects on user compliance (2020, background): “For instance, users communicate with chatbots for a longer duration and with less rich vocabulary as well as greater profanity (Hill et al. 2015).”
Conversational AI: Social and Ethical Considerations (2019, background): “Research has shown users use greater profanity with a chatbot than with a human and are similarly more likely to harass a chatbot than a human agent [16], even more so if the agent has a female persona [42].”
The impact of Generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney (2023, positive): “The reviewed studies also showed that AI-enabled feed-back learning systems improved students problem-solving skills (Mokmin, 2020), communication skills (Hill et al., 2015), self-directed learning capacity (RodrÃguez et al., 2021), and creativity (Huang, 2018).”
Teacher support and student motivation to learn with Artificial Intelligence (AI) based chatbot (2023, neutral): “Notwithstanding the lack of major breakthroughs in chatbot language communication skills, students still ﬁ nd them comfortable, interesting and fun to play with (Fryer et al., 2019; Hill et al., 2015; Yin et al., 2021).”
Towards Designing Cooperative and Social Conversational Agents for Customer Service (2017, background): “Text-based CAs are often referred to as chatbots (e.g., Hill et al. 2015; Shawar and Atwell 2007) or natural dialogue systems (e.g., Shah et al. 2016; Zadrozny et al. 2000), which can be interacted with using text messages.”
How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Design (2019, background): “The surveyed literature highlights the following benefits of providing damage control in chatbots: [B1] to appropriately respond to harassment: chatbots are more likely to be exposed to profanity than humans would be [Hill et al. 2015].”
Stimulating and sustaining interest in a language course: An experimental comparison of Chatbot and Human task partners (2017, methodology): “The current ﬁ ndings do not support the longstanding (Fryer & Carpenter, 2006; Weizenbaum, 1966) and recent (Hill et al., 2015) assumptions regarding the motivational bene ﬁ ts of Chatbot interaction.”