Relevant bibliographies by topics / Chatbots

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Chatbots'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 December 2024

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Journal articles on the topic "Chatbots"

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Rangasamy, Sangeetha, Aishwarya Nagarathinam, Aarthy Chellasamy, and Elangovan N. "Health-Seeking Behaviour and the use of Artificial Intelligence-based Healthcare Chatbots among Indian Patients." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10s (October 7, 2023): 440–50. http://dx.doi.org/10.17762/ijritcc.v11i10s.7652.

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Artificial Intelligence (AI) based healthcare chatbots can scale up healthcare services in terms of diagnosis and treatment. However, the use of such chatbots may differ among the Indian population. This study investigates the influence of health-seeking behaviour and the availability of traditional, complementary and alternative medicine systems on healthcare chatbots. A quantitative study using a survey technique collects data from the Indian population. Items measuring the awareness of chatbot’s attributes and services, trust in the chatbots, health-seeking behaviour, traditional, complementary and alternative medicine, and use of chatbots are adapted from previous scales. A convenience sample is used to collect the data from the urban population. 397 samples were fetched, and statistical analysis was done. Awareness of the chatbot’s attributes and services impacted the trust in the chatbots. Health-seeking behaviour positively impacted the use of chatbots and enhanced the impact of trust of a chatbot on the use of a chatbot. Traditional, complementary and alternative medicine was not included in the chatbot, which negatively impacted the use of chatbots. At the same time, it dampened the impact of trust in chatbots on the use of chatbots. The study was limited to the urban population and a convenience sampling because of the need to use the Internet and a smart device for accessing the chatbots. The results of the study need to be used cautiously. The results can be inferred from the relationships’ existence rather than the impact’s magnitude. The study’s outcome encourages the availability of chatbots due to the health-seeking behaviour of the Indian urban population. The study also highlights the need for creating intelligent agents with knowledge of Traditional, complementary and alternative medicine. The study contributes to the knowledge of using chatbots in the Indian context. When earlier studies focus mainly on the chatbot features or user characteristics in the intention studies, this study looks at the healthcare system and the services unique to India.
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Suh, Jeehae. "A Study on the Conformity of Chatbot Builder as a Korean Speech Practice Tool." Korean Society of Culture and Convergence 45, no. 1 (January 31, 2023): 61–70. http://dx.doi.org/10.33645/cnc.2023.01.45.01.61.

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The purpose of this study is to verify whether chatbots made with chatbot builders are suitable as a Korean speaking practice tool. Chatbot builders, which can be easily produced as chatbots without separate coding knowledge and can design conversations meaningful for learning, have recently been in the spotlight as a learning tool. In this study, chatbots were created using dialog flows, and conversation patterns with chatbots shared by study participants were analyzed. As a result of the analysis, it was found that 35% of all conversations were not successfully completed. Such a conversation failure was found to be due to the inaccuracy of chatbot's recognition of Korean learner pronunciation, error in handling learner utterance intention, and inaccuracy in handling learner error sentences. In this regard, in order for chatbot builder to be used as a Korean language learning tool now, learner's proficiency or academic achievement should be considered for smooth processing of chatbot's learner utterance. In addition, this study is meaningful in that it verified the suitability of chatbot builders as a learning tool not covered in previous studies.
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Cui, Yichao, Yu-Jen Lee, Jack Jamieson, Naomi Yamashita, and Yi-Chieh Lee. "Exploring Effects of Chatbot's Interpretation and Self-disclosure on Mental Illness Stigma." Proceedings of the ACM on Human-Computer Interaction 8, CSCW1 (April 17, 2024): 1–33. http://dx.doi.org/10.1145/3637329.

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Chatbots are increasingly being used in mental healthcare - e.g., for assessing mental-health conditions and providing digital counseling - and have been found to have considerable potential for facilitating people's behavioral changes. Nevertheless, little research has examined how specific chatbot designs may help reduce public stigmatization of mental illness. To help fill that gap, this study explores how stigmatizing attitudes toward mental illness may be affected by conversations with chatbots that have 1) varying ways of expressing their interpretations of participants' statements and 2) different styles of self-disclosure. More specifically, we implemented and tested four chatbot designs that varied in terms of whether they interpreted participants' comments as stigmatizing or non-stigmatizing, and whether they provided stigmatizing, non-stigmatizing, or no self-disclosure of chatbot's own views. Over the two-week period of the experiment, all four chatbots' conversations with our participants centered on seven mental-illness vignettes, all featuring the same character. We found that the chatbot featuring non-stigmatizing interpretations and non-stigmatizing self-disclosure performed best at reducing the participants' stigmatizing attitudes, while the one that provided stigmatizing interpretations and stigmatizing self-disclosures had the least beneficial effect. We also discovered side effects of chatbot's self-disclosure: notably, that chatbots were perceived to have inflexible and strong opinions, which undermined their credibility. As such, this paper contributes to knowledge about how chatbot designs shape users' perceptions of the chatbots themselves, and how chatbots' interpretation and self-disclosure may be leveraged to help reduce mental-illness stigma.
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Chaves, Ana Paula, Jesse Egbert, Toby Hocking, Eck Doerry, and Marco Aurelio Gerosa. "Chatbots Language Design: The Influence of Language Variation on User Experience with Tourist Assistant Chatbots." ACM Transactions on Computer-Human Interaction 29, no. 2 (April 30, 2022): 1–38. http://dx.doi.org/10.1145/3487193.

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Chatbots are often designed to mimic social roles attributed to humans. However, little is known about the impact of using language that fails to conform to the associated social role. Our research draws on sociolinguistic to investigate how a chatbot’s language choices can adhere to the expected social role the agent performs within a context. We seek to understand whether chatbots design should account for linguistic register. This research analyzes how register differences play a role in shaping the user’s perception of the human-chatbot interaction. We produced parallel corpora of conversations in the tourism domain with similar content and varying register characteristics and evaluated users’ preferences of chatbot’s linguistic choices in terms of appropriateness, credibility, and user experience. Our results show that register characteristics are strong predictors of user’s preferences, which points to the needs of designing chatbots with register-appropriate language to improve acceptance and users’ perceptions of chatbot interactions.
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Bortko, Kamil, Kacper Fornalczyk, Jarosław Jankowski, Piotr Sulikowski, and Karina Dziedziak. "Impact of changes in chatbot’s facial expressions on user attention and reaction time." PLOS ONE 18, no. 7 (July 27, 2023): e0288122. http://dx.doi.org/10.1371/journal.pone.0288122.

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Communication within online platforms supported by chatbots requires algorithms, language processing methods, and an effective visual representation. These are crucial elements for increasing user engagement and making communication more akin to natural conversation. Chatbots compete with other graphic elements within websites or applications, and thus attracting a user’s attention is a challenge even before the actual conversation begins. A chatbot may remain unnoticed even with sophisticated techniques at play. Drawing attention to the chatbot area localized within the periphery area can be carried out with the use of various visual characteristics. The presented study analyzed the impact of changes in a chatbot’s emotional expressions on user reaction. The aim of this study was to observe, based on user reaction times, whether changes in a chatbot’s emotional expressions make it more noticeable. The results showed that users are more sensitive to positive emotions within chatbots, as positive facial expressions were noticed more quickly than negative ones.
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Biro, Joshua, Courtney Linder, and David Neyens. "The Effects of a Health Care Chatbot’s Complexity and Persona on User Trust, Perceived Usability, and Effectiveness: Mixed Methods Study." JMIR Human Factors 10 (February 1, 2023): e41017. http://dx.doi.org/10.2196/41017.

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Background The rising adoption of telehealth provides new opportunities for more effective and equitable health care information mediums. The ability of chatbots to provide a conversational, personal, and comprehendible avenue for learning about health care information make them a promising tool for addressing health care inequity as health care trends continue toward web-based and remote processes. Although chatbots have been studied in the health care domain for their efficacy for smoking cessation, diet recommendation, and other assistive applications, few studies have examined how specific design characteristics influence the effectiveness of chatbots in providing health information. Objective Our objective was to investigate the influence of different design considerations on the effectiveness of an educational health care chatbot. Methods A 2×3 between-subjects study was performed with 2 independent variables: a chatbot’s complexity of responses (eg, technical or nontechnical language) and the presented qualifications of the chatbot’s persona (eg, doctor, nurse, or nursing student). Regression models were used to evaluate the impact of these variables on 3 outcome measures: effectiveness, usability, and trust. A qualitative transcript review was also done to review how participants engaged with the chatbot. Results Analysis of 71 participants found that participants who received technical language responses were significantly more likely to be in the high effectiveness group, which had higher improvements in test scores (odds ratio [OR] 2.73, 95% CI 1.05-7.41; P=.04). Participants with higher health literacy (OR 2.04, 95% CI 1.11-4.00, P=.03) were significantly more likely to trust the chatbot. The participants engaged with the chatbot in a variety of ways, with some taking a conversational approach and others treating the chatbot more like a search engine. Conclusions Given their increasing popularity, it is vital that we consider how chatbots are designed and implemented. This study showed that factors such as chatbots’ persona and language complexity are two design considerations that influence the ability of chatbots to successfully provide health care information.
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Septiyanti, Nisa Dwi, Muhammad Irfan Luthfi, and Darmawansah Darmawansah. "Effect of Chatbot-Assisted Learning on Students’ Learning Motivation and Its Pedagogical Approaches." Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika 10, no. 1 (April 30, 2024): 69–77. http://dx.doi.org/10.23917/khif.v10i1.4246.

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Abstract- The use of chatbots in the learning process has been increasingly investigated and applied. While many studies have discussed the chatbot's ability to motivate students' interest in learning, few have examined whether students' perception of learning affects the effectiveness of chatbots and the pedagogical approach taken by chatbots as conversational agents during the learning process. There is a need for new analysis to capture the effects of Chatbot-Assisted Learning (Chatbot-AL) and student-chatbot conversations. In an eight-week semester, 48 first-year undergraduate students participated in a chatbot-assisted learning environment integrated into an engineering course. Data were collected through questionnaires on students' learning motivation and discourse in chatbot conversations. Statistical non-parametric analysis and Epistemic Network Analysis (ENA) were used to explore the research questions. The results showed that students with high learning perception had better learning motivation using chatbot-AL than students with low learning perception. Additionally, most of the questions asked by students were aimed at receiving emotional support through casual conversation with the chatbot. Finally, the implications, limitations, and conclusions were discussed.
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Dennis, Alan R., Antino Kim, Mohammad Rahimi, and Sezgin Ayabakan. "User reactions to COVID-19 screening chatbots from reputable providers." Journal of the American Medical Informatics Association 27, no. 11 (July 6, 2020): 1727–31. http://dx.doi.org/10.1093/jamia/ocaa167.

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Abstract Objectives The objective was to understand how people respond to coronavirus disease 2019 (COVID-19) screening chatbots. Materials and Methods We conducted an online experiment with 371 participants who viewed a COVID-19 screening session between a hotline agent (chatbot or human) and a user with mild or severe symptoms. Results The primary factor driving user response to screening hotlines (human or chatbot) is perceptions of the agent’s ability. When ability is the same, users view chatbots no differently or more positively than human agents. The primary factor driving perceptions of ability is the user’s trust in the hotline provider, with a slight negative bias against chatbots’ ability. Asian individuals perceived higher ability and benevolence than did White individuals. Conclusions Ensuring that COVID-19 screening chatbots provide high-quality service is critical but not sufficient for widespread adoption. The key is to emphasize the chatbot’s ability and assure users that it delivers the same quality as human agents.
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Sabna, Eka. "CHATBOT SEBAGAI GURU VIRTUAL UNTUK MATA KULIAH DATA MINING." Jurnal Ilmu Komputer 11, no. 2 (November 4, 2022): 110–15. http://dx.doi.org/10.33060/jik/2022/vol11.iss2.276.

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Abstract A chatbot is an application (service) that interacts with users through text conversations. Chatbots work to replace the role of humans in serving conversations through messaging applications. The chatbot that is built will be virtual assisting that will help students learn at home. Chatbots can only answer questions based on patterns that have been stored in the chatbot's knowledge base. Chatbots are automated conversational agents that interact with users using natural human language that can help anytime and anywhere. This chatbot is applied as a Virtual Teacher who can provide information and learning materials to students in Data Mining courses. Keywords: C4.5, NBC, GPA, performance, student Abstrak Chatbot merupakan aplikasi (layanan) yang berinteraksi dengan pengguna melalui percakapan teks. Chatbot bekerja untuk menggantikan peranan manusia dalam melayani pembicaraan melalui aplikasi pesan. chatbot yang dibangun akan menjadi virtual assisting yang akan membantu Mahasiswa dalam belajar dirumah. Chatbot hanya dapat menjawab pertanyaan berdasarkan pola yang telah disimpan di dalam knowledge base chatbot. Chatbot adalah agen percakapan otomatis yang berinteraksi dengan pengguna menggunakan bahasa alami manusia yang dapat membantu kapan saja dan dimana saja. Chatbot ini di aplikasikan sebagai Guru Virtual yang dapat memberikan informasi dan materi pembelajaran terhadap mahasiswa dalam matakuliah Data Mining. Kata Kunci : Chatbot, Data Mining, Virtual Teacher, Student, Learning
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Le, Nhat, A. B. Siddique, Fuad Jamour, Samet Oymak, and Vagelis Hristidis. "Generating Predictable and Adaptive Dialog Policies in Single- and Multi-domain Goal-oriented Dialog Systems." International Journal of Semantic Computing 15, no. 04 (December 2021): 419–39. http://dx.doi.org/10.1142/s1793351x21400109.

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Most existing commercial goal-oriented chatbots are diagram-based; i.e. they follow a rigid dialog flow to fill the slot values needed to achieve a user’s goal. Diagram-based chatbots are predictable, thus their adoption in commercial settings; however, their lack of flexibility may cause many users to leave the conversation before achieving their goal. On the other hand, state-of-the-art research chatbots use Reinforcement Learning (RL) to generate flexible dialog policies. However, such chatbots can be unpredictable, may violate the intended business constraints, and require large training datasets to produce a mature policy. We propose a framework that achieves a middle ground between the diagram-based and RL-based chatbots: we constrain the space of possible chatbot responses using a novel structure, the chatbot dependency graph, and use RL to dynamically select the best valid responses. Dependency graphs are directed graphs that conveniently express a chatbot’s logic by defining the dependencies among slots: all valid dialog flows are encapsulated in one dependency graph. Our experiments in both single-domain and multi-domain settings show that our framework quickly adapts to user characteristics and achieves up to 23.77% improved success rate compared to a state-of-the-art RL model.
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Dissertations / Theses on the topic "Chatbots"

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Aljadri, Sinan. "Chatbot : A qualitative study of users' experience of Chatbots." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105434.

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The aim of the present study has been to examine users' experience of Chatbot from a business perspective and a consumer perspective. The study has also focused on highlighting what limitations a Chatbot can have and possible improvements for future development. The study is based on a qualitative research method with semi-structured interviews that have been analyzed on the basis of a thematic analysis. The results of the interview material have been analyzed based on previous research and various theoretical perspectives such as Artificial Intelligence (AI), Natural Language Processing (NLP). The results of the study have shown that the experience of Chatbot can differ between businesses that offer Chatbot, which are more positive and consumers who use it as customer service. Limitations and suggestions for improvements around Chatbotar are also a consistent result of the study.
Den föreliggande studie har haft som syfte att undersöka användarnas upplevelse av Chatbot utifrån verksamhetsperspektiv och konsumentperspektiv. Studien har också fokuserat på att lyfta fram vilka begränsningar en Chatbot kan ha och eventuella förbättringar för framtida utvecklingen. Studien är baserad på en kvalitativ forskningsmetod med semistrukturerade intervjuer som har analyserats utifrån en tematisk analys. Resultatet av intervjumaterialet har analyserat utifrån tidigare forskning och olika teoretiska perspektiv som Artificial Intelligence (AI), Natural Language Processing (NLP). Resultatet av studien har visat att upplevelsen av Chatbot kan skilja sig mellan verksamheter som erbjuder Chatbot, som är mer positiva och konsumenter som använder det som kundtjänst. Begränsningar och förslag på förbättringar kring Chatbotar är också ett genomgående resultat i studien.
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Bartenberger, Martin, Sven Galla, and Alexander Kosak. "Legal chatbots." Universität Leipzig, 2018. https://ul.qucosa.de/id/qucosa%3A21215.

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This article introduces the idea of legal chatbots and how legal chatbots might affect the legal market in the near future. We define chatbots as computer programs that automatically chat with users and assess their potential for legal consultation. We identify four potential strengths of legal chatbots: providing access to justice, serving as contact points for customers, reducing the knowledge gap between lawyer and client and automatically generating documents and taking further actions. In the concluding section we briefly discuss ethical aspects of legal chatbots and possible future developments.
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Joigneau, Axel. "Utterances classifier for chatbots’ intents." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233362.

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Chatbots are the next big improvement in the era of conversational services. A chatbot is a virtual person who can carry out a conversation with a human about a certain subject, using interactive textual skills. Currently, there are many cloud-based chatbots services that are being developed and improved such as IBM Watson, well known for winning the quiz show “Jeopardy!” in 2011. Chatbots are based on a large amount of structured data. They contains many examples of questions that are associated to a specific intent which represents what the user wants to say. Those associations are currently being done by hand, and this project focuses on improving this data structuring using both supervised and unsupervised algorithms. A supervised reclassification using an improved Barycenter method reached 85% in precision and 75% in recall for a data set containing 2005 questions. Questions that did not match any intent were then clustered in an unsupervised way using a K-means algorithm that reached a purity of 0.5 for the optimal K chosen.
Chatbots är nästa stora förbättring i konversationstiden. En chatbot är en virtuell person som kan genomföra en konversation med en människa om ett visst ämne, med hjälp av interaktiva textkunskaper. För närvarande finns det många molnbaserade chatbots-tjänster som utvecklas och förbättras som IBM Watson, känt för att vinna quizshowen "Jeopardy!" 2011. Chatbots baseras på en stor mängd strukturerade data. De innehåller många exempel på frågor som är kopplade till en specifik avsikt som representerar vad användaren vill säga. Dessa föreningar görs för närvarande för hand, och detta projekt fokuserar på att förbättra denna datastrukturering med hjälp av både övervakade och oövervakade algoritmer. En övervakad omklassificering med hjälp av en förbättrad Barycenter-metod uppnådde 85 % i precision och 75 % i recall för en dataset innehållande 2005 frågorna. Frågorna som inte matchade någon avsikt blev sedan grupperade på ett oövervakad sätt med en K-medelalgoritm som nådde en renhet på 0,5 för den optimala K som valts.
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Kottorp, Max, and Filip Jäderberg. "Chatbot As a Potential Tool for Businesses : A study on chatbots made in collaboration with Bisnode." Thesis, KTH, Industriell ekonomi och organisation (Inst.), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210768.

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The investigation aims to provide an answer to if a chatbot is a potential complement to an internal service desk of a company. The work has centered around developing a chatbot able to handle simple Q&A-interaction of the internal service desk of Bisnode, the company in question. The chatbot acted as an proof of concept, which then was tested by 15 individuals. The testing was done with pre- defined user scenarios, where the test person ultimately had to fill in a questionnaire with statements related to the overall experience. By summarizing the user evaluations from the questionnaires, combined with an SWOT analysis, the work concluded that a chatbot is indeed a potential complement to an internal service desk of a company, if it handles Q&A-interaction.
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Lotze, Netaya [Verfasser]. "Chatbots : Eine linguistische Analyse / Netaya Lotze." Frankfurt a.M. : Peter Lang GmbH, Internationaler Verlag der Wissenschaften, 2016. http://d-nb.info/1122003781/34.

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Cruz, Erik, and Anton Svanborg. "RCS Chatbots vs. Single- purpose Apps." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302024.

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The Rich Communication Service (RCS) aims to be the default messaging protocol in mobile devices. The native integration of RCS opens up the possibility of RCS chatbots replacing single-purpose apps. This report analyzes this possibility through in-depth experimentation of the chatbot functionality, followed by user testing of the chatbot features. The report found that the RCS chatbot could replace mobile applications since many solve a specific task and follow a closed-loop system. The report also identified RCS as a solution to the accumulated unused apps users have on their mobile devices. This report also attempts to fill the research gap on the current situation of RCS and the reasons behind the different rollout rates of RCS globally. The examination of interviews with RCS stakeholders was the basis of a stakeholder analysis. This analysis found that the Mobile Network Operators have different standings regarding RCS. Some see potential, and some see issues with RCS hindering the global rollout. Furthermore, the reluctance from Apple and the high involvement of Google are reviewed and contrasted with the answers from the interviews.
Rich Communication Service (RCS) siktar på att bli det nya standardprotokollet för att skicka meddelanden mellan mobiler. RCS potentiella integration i mobilens egna meddelande applikation öppnar upp för möjligheten att RCS- chatbotar även kan ersätta enklare appar med begränsade användningsområden. Denna rapport undersöker den här möjligheten genom experimenterande med chatbotens funktionalitet följt av användartester på chatbotens olika funktioner. Rapporten fann att chatboten i RCS har goda möjligheter att ersätta enklare mobila applikationer då många löser specifika uppgifter som följer ett system med en sluten slinga. Rapporten identifierade även RCS som en lösning till den mängd oanvända appar som användare har i sina mobiler. Denna rapport försöker även fylla det forskningsgap som finns angående RCS nuvarande situation och anledningarna till de olika adoptionshastigheterna i världen. Granskning av de intervjuer som hölls med olika intressenter på marknaden blev grunden för den intressentanalys som presenteras. Denna analys visade att teleoperatörer har olika ställningar gentemot RCS. Vissa ser potential medan andra främst ser problem vilket hindrar spridningen av RCS. Dessutom analyserades Apples ovilja att anamma RCS samt Googles
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Pittaro, Giulio <1996&gt. "Artificial Intelligence Marketing: il caso dei Chatbots." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/20340.

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L’oggetto della mia tesi scaturisce da un forte interesse riguardo l’AI (Artificial intelligence) implementata nell’ambito del Marketing. Ciò che mi ha spinto a voler approfondire questo tema parte dal libro di Alessio Semoli “AI Marketing, Capire l’intelligenza artificiale per coglierne le opportunità” attraverso il quale ho potuto comprendere quanto il modo di fare business non sia più lo stesso a cause delle innovative tecnologie che rinnovano gli ambienti e la mentalità. Le nuove dinamiche economiche e sociali stanno rivoluzionando gli stili di vita e i comportamenti di acquisto, per questo motivo è di vitale importanza che le aziende monitorino costantemente le nuove tendenze di consumo e adattino la propria offerta ad una domanda sempre più variabile. Ed è proprio nella fase di “ascolto del mercato” che si inserisce l’Artificial Intelligence diventando per i marketer un elemento determinante per analizzare gli insights di domanda. In modo particolare la mia analisi prevede di approfondire l’implementazione di tale tecnologia nel marketing, analizzando come l’intelligenza Artificiale ha trasformato il lavoro dei marketer nonché comprendere quali sono le principali sfide e criticità. Dopo aver discusso, nel primo capitolo, il ruolo dei Big Data nel plasmare il modo di fare business, e della loro importanza nel guidare l’impresa verso una cultura “data-driven”, nel secondo capitolo si entra nello specifico, approfondendo l’intelligenza Artificiale in tutte le sue sfaccettature descrivendone vantaggi e zone d’ombra. Nel terzo capitolo si esamina l’avvento del Marketing Tecnologizzato che in larga misura risulta essere terreno fertile per l’implementazione dell’AI e della Marketing Automation, con una crescente necessita da parte delle imprese di congiungere queste due tecnologie per far fronte ad un ambiente sempre più dinamico e competitivo. Viene successivamente analizzato il tema dei chatbots o assistenti virtuali, la tecnologia più avanzata dell’Intelligenza Artificiale, che negli ultimi anni, ha fatto riscontrare un tasso di adozione elevato da parte delle imprese, divenendo parte fondamentale delle strategie di marketing digitale. Anche a causa della pandemia l’implementazione degli assistenti virtuali si è fatta più robusta, favorendo la necessità di comprendere la percezione dei consumatori di fronte gli assistenti virtuali che di fatto irrompono sempre più nella comunicazione brand-cliente. Pertanto, l’elaborato si conclude con una ricerca qualitativa volta a esaminare il comportamento degli utenti quando si trovano a comunicare con tali sistemi, analizzando le problematiche e/o gli stimoli nell' interagire con i chatbots.
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Silva, João Quirino Machado e. "Desenvolvimento de Chatbots para responder a perguntas frequentes." Master's thesis, Universidade de Évora, 2021. http://hdl.handle.net/10174/29039.

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Hoje em dia, seja qual for o sítio que visitamos na Internet, dificilmente não nos deparamos com um Chatbot: desde as pequenas caixas de diálogo que nos assistem em compras online, até aos agentes virtuais que executam os pedidos que fazemos através de comandos de voz. Nesta dissertação, o objetivo principal é a implementação de um Chatbot para responder a perguntas frequentes. Para tal, foi realizado um estudo sobre o Processamento de Linguagem Natural, formas de preparar o texto para que fique pronto a ser utilizado pelo computador, distâncias que podem ser aplicadas a texto, similaridade semântica e, por fim, qual o impacto da remoção das palavras vazias. Após a realização deste estudo e obtenção da informação necessária, foi implementado um Chatbot para responder a perguntas frequentes, independentemente do tema dessas perguntas. Esse Chatbot foi, posteriormente, testado em perguntas frequentes relacionadas com o Instituto da Vinha e do Vinho; Abstract: Chatbots development to answer frequently asked questions Nowadays, whatever the site we visit on the Internet, we hardly encounter a Chatbot: from the small dialog boxes that assist us in online shopping to the virtual agents that execute the orders we make through voice commands. In this dissertation, the main goal is to implement a Chatbot to answer frequently asked questions. With this in mind, a study was carried out on natural language processing, ways to prepare the text so that it is ready for use by the computer, distances that can be applied to text, semantic similarity and, finally, what is the impact of removing stopwords. After conducting this and obtaining the necessary information, a Chatbot was implemented to answer frequently asked questions, regardless of the domain of those questions. This Chatbot was subsequently tested on frequently asked questions related to the Institute of Vine and Wine.
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Siemon, Dominik, and Susanne Robra-Bissantz. "Bedürfniserhebung durch Chatbots an ausgewählten Touchpoints innerhalb der Kundenkommunikation." TUDpress, 2019. https://tud.qucosa.de/id/qucosa%3A36565.

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Aufgrund eines bedeutenden technologischen Fortschritts im Bereich der künstlichen Intelligenz (KI) sind eine Vielzahl neuer Systeme und Anwendungen entstanden, die eine künstliche Interaktion und Kommunikation mit Nutzern ermöglichen (Gnewuch, Morana, & Maedche, 2017; Maedche, Morana, Schacht, Werth, & Krumeich, 2016). Neben spezifischen Anwendungen in Form von virtuellen Assistenten (VA), wie Apples Siri oder Amazonas Alexa, entwickeln Unternehmen zunehmend Chatbots und Enterprise Bots für die Interaktion mit Kunden. Im Jahr 2016 hat Facebook ihren Messenger für Chatbots geöffnet wodurch mittlerweile über 34.000 Chatbots auf Facebook vorhanden sind. Auch Microsoft hat mit Cortana eine Möglichkeit geschaffen, mit einem Chatbot zu interagieren. Der Messangerdienst Telegram ermöglichte diese künstliche Interkation bereits im Jahr 2015 und Google stellt mit seinem Dienst Dialogflow ein einfaches Angebot zur Erstellung eigener Chatbots für Unternehmen bereit (Stäcker & Stanoevska-Slabeva, 2018). Das Jahr 2017 wird auch als der zweite Frühling der Chatbots bezeichnet (Haberich, 2018). Klar (2018) sagt, dass Chatbots im Kundenservice in allen Branchen eine immer wichtigere Rolle spielen und sogar von Kunden gewünscht werden. Auch wenn Chatbots bereits vielseitig im Kontakt mit Kunden eingesetzt werden, fehlen konkrete wissenschaftliche Studien zu der Akzeptanz von Kunden im Bereich der Bedürfniserhebung. Die Erfüllung von Kundenbedürfnissen ist ein zentrales Element eines Unternehmens, wodurch die Erhebung dieser Kundenbedürfnisse einen hohen Stellenwert einnimmt (Jacob, 2018). Die Erhebung von Kundenbedürfnissen ist mit viel Aufwand verbunden, welches unter anderem ressourcenintensive Studien der Marktforschung oder Analysen bestehender Daten mit sich zieht. Die direkte Bedürfniserhebung durch Kundenberater ist darüber hinaus mit einem hohen Zeit- und Personalaufwand verbunden, da möglichst jeder Kunde unmittelbar betreut werden sollte (Jacob, 2018). Eine Beratung durch einen intelligenten künstlichen Kundenbetreuer, könnte somit eine enorme Kosten- und Zeitaufwandsreduktion hervorrufen und dennoch eine Kundenzufriedenheit und somit Loyalität erzeugen. Ziel dieses Artikels ist es, herauszufinden inwieweit Chatbots in der B2C-Kommunkiation die Bedürfnisse des Kunden an bestimmten Touchpoints erheben können. Dafür wurde eine empirische Studie mit einem explorativen Fragebogen mit 131 Probanden durchgeführt. [... aus der Einleitung]
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Lidén, Alexander, and Karl Nilros. "Percieved benefits and limitations of chatbots in higher education." Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-96327.

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Prior to 2012 artificial intelligence, the study of intelligent agents, followed Moore’s law which states that compute is doubling every two years. Post 2012 it has been doubling every 3.4 months. However, intelligent agents are focusing on human language, and conversation is rarely developed for education. This study investigates a student’s perceived benefits and limitations of chatbots in higher education, by exploring the relative advantage, complexity, and compatibility of a different chatbot functionality. By interviewing students the authors could establish four different themes that perceived to be important when using a chatbot, Decreasing obstacles, Enhanced learning process, Hesitance towards complexity, and Teacher involvement. Overall, this study suggests that it is preferable to start with little functionality and then successively improve. Because smaller implementations with basic functionality are more accepted and useful to students compared to complex AI functionality, and for future implementation, this is something that should be accounted for.
Före 2012 följde Artificiell intelligens, läran om intelligenta agenter Moores lag vilket innebär att data beräkningars kraft fördubblas vartannat år. Efter 2012 har det fördubblats var 3,4 månad. Dock utvecklas ofta de intelligenta agenterna med fokus på det mänskliga språket samt hälsa och sällan för utbildnings syfte. Den här studien undersöker studenters uppfattning om fördelar och nackdelar av chatbotar i högre utbildningssyfte genom att utforska relativa fördelar, svårigheter och kompatibiliteten av olika chatbot funktionaliteter. Genom att intervjua studenter kunde författarna etablera fyra olika teman som uppfattades vara viktiga när en chatbot används, att minska hinder, förbättra lärningsprocessen, tvivel gentemot svårigheter och lärarens medverkan. Sammanfattningsvis pekar denna studie på att det är att föredra att börja utveckla chatbotar med med lite funktionalitet och att sedan successivt öka. Detta för att mindre implementationer med grundlig funktionalitet är mer accepterad och användbar för studenter jämfört med komplex AI funktionalitet, och detta är något att ta hänsyn till i framtida implementationer.
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Books on the topic "Chatbots"

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Kohne, Andreas, Philipp Kleinmanns, Christian Rolf, and Moritz Beck. Chatbots. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-28849-5.

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Crowder, James. AI Chatbots. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-45509-4.

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Bruns, Beate, and Cäcilie Kowald. Praxisleitfaden Chatbots. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-39645-9.

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Dufaux, Marius O. Coaching-Chatbots. Wiesbaden: Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-45722-8.

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Kaiser, Markus, Aline-Florence Buttkereit, and Johanna Hagenauer. Journalistische Praxis: Chatbots. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-25494-0.

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Khan, Rashid, and Anik Das. Build Better Chatbots. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3111-1.

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Galitsky, Boris. Developing Enterprise Chatbots. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04299-8.

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Lömker, Malte, Ulrike Weber, and Johannes Moskaliuk. Chatbots im Coaching. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32830-6.

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Raj, Sumit. Building Chatbots with Python. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4096-0.

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Braun, Alexander. Chatbots in der Kundenkommunikation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-19021-6.

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Book chapters on the topic "Chatbots"

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Kohne, Andreas, Philipp Kleinmanns, Christian Rolf, and Moritz Beck. "Einleitung." In Chatbots, 1–6. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-28849-5_1.

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Kohne, Andreas, Philipp Kleinmanns, Christian Rolf, and Moritz Beck. "Grundlagen." In Chatbots, 7–21. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-28849-5_2.

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Kohne, Andreas, Philipp Kleinmanns, Christian Rolf, and Moritz Beck. "Anwendungsgebiete." In Chatbots, 23–39. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-28849-5_3.

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Kohne, Andreas, Philipp Kleinmanns, Christian Rolf, and Moritz Beck. "Technik." In Chatbots, 41–81. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-28849-5_4.

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Kohne, Andreas, Philipp Kleinmanns, Christian Rolf, and Moritz Beck. "Conversation Design." In Chatbots, 83–97. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-28849-5_5.

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Kohne, Andreas, Philipp Kleinmanns, Christian Rolf, and Moritz Beck. "Beispielhafter Projektablauf." In Chatbots, 99–121. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-28849-5_6.

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Kohne, Andreas, Philipp Kleinmanns, Christian Rolf, and Moritz Beck. "Weitere wichtige Aspekte." In Chatbots, 123–42. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-28849-5_7.

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Kohne, Andreas, Philipp Kleinmanns, Christian Rolf, and Moritz Beck. "Zusammenfassung." In Chatbots, 143–44. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-28849-5_8.

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Kohne, Andreas, Philipp Kleinmanns, Christian Rolf, and Moritz Beck. "Ausblick." In Chatbots, 145–49. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-28849-5_9.

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Barone, Ada Maria, and Emanuela Stagno. "Chatbots." In Artificial Intelligence along the Customer Journey, 37–54. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48792-7_3.

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Conference papers on the topic "Chatbots"

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Liao, Ting, and Bei Yan. "Let’s Chat If You Are Unhappy – The Effect of Emotions on Interaction Experience and Trust Toward Empathetic Chatbots." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-115318.

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Abstract Chatbots are now prevalent in obtaining information and executing tasks on behalf of human users. While the design community has paid more attention to streamlining user-chatbot interactions, the existing literature has not thoroughly examined social elements such as emotions and understood how emotions influence user interaction and trust in chatbots. This study proposes to test how participants perceive an empathetic chatbot versus a non-empathetic one under various emotional states (i.e., positive, neutral, negative) when the chatbot facilitates conversations for student advising via an online platform. The study shows the importance of presenting empathetic cues in the design of chatbots or other intelligent agents. The empathetic behavior of the chatbot improves participants’ trust and perception of the chatbot’s performance. The improvement is more salient to people with negative emotions than people who feel neutral or positive. This interaction effect is explained by people’s perceived effort of the chatbot during the interaction with people, even though the chatbot is a technological agent that acts based on algorithms. The results suggest that people attribute human qualities to chatbots in social interaction and highlight the emotional needs of people who experience negative emotions. Therefore, design efforts need to be designated according to people’s dynamic emotional states.
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"How to Program a Chatbot – An Introductory Project and Student Perceptions." In InSITE 2019: Informing Science + IT Education Conferences: Jerusalem. Informing Science Institute, 2019. http://dx.doi.org/10.28945/4283.

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[This Proceedings paper was revised and published in the 2019 issue of the journal Issues in Informing Science and Information Technology, Volume 16] Aim/Purpose: In this article, we introduced a project on teaching chatbot programming in an information systems class. Background: One of the most fascinating developments in computer user interfaces in recent years is the rise of “chatbots”. Yet extent information system (IS) curriculum lacks teaching resources on chatbots programming. To better prepare students for this new technological development and to enhance the IS curriculum, we introduce a project that teaches students how to program simple chatbots, including a transactional chatbot and a conversational chatbot. Methodology: We introduce a project that teaches students how to program two types of simple chatbots, a transactional chatbot and a conversational chatbot. Following the project instructions, students can get their first intelligent chatbots up and running in a few hours using Slack. This article describes the project in detail as well as students’ perceptions. We also conducted a survey to examine students’ perceptions on their learning experience. The survey results are reported as well. Findings: Our survey on students’ perception of the project finds that learning chatbots is deemed very useful because chatbot programming projects have enabled the students to understand the subject better. We also found that social influence has positively motivated the students to learn chatbot programming. Though most of the students have no prior experiences programming chatbots, their self-efficacy towards chatbot programming remained quite high after working through the programming project. Despite the difficult tasks, over 71% of respondents agree to various degrees that chatbot programming is fun. Though most students agree that chatbot programming is not easy to learn, more than 70% of respondents indicated that they will use or learn chatbots in the near future. The overwhelmingly positive responses are impressive given that this is the first time for the students to program and learn chatbots. Future Research: For future work, we plan to expand the teaching resources to cover more advanced chatbot programming projects, such as on how to make chatbot more human-like.
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Cachola, Krystal, and Kim-Phuong L. Vu. "Investigating the Use of Chatbots as an Educational Tool." In 2024 AHFE International Conference on Human Factors in Design, Engineering, and Computing (AHFE 2024 Hawaii Edition). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005752.

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With the rise of online asynchronous learning and low levels of instructor presence, students have become self-regulated learners who must monitor their performance and adapt their learning strategies as necessary. Previous studies have shown that chatbots are a promising alternative to traditional study tools such as flashcards. This study examined the effects of a chatbot’s embodiment (Humanoid, Animated) and conversational style (Formal, Informal) on learning performance and behavioral engagement. Participants were asked to watch a lecture video and interact with a chatbot to review the material. After studying with the chatbot, participants completed a quiz that was evenly split according to difficulty (easy versus hard questions), as well as study type (questions that were and were not studied with the chatbot). Participants’ ratings of usability, usefulness, ease of use, and affective engagement were obtained. Results showed that participants performed better on easy questions than hard questions. Additionally, participants performed better on studied questions than non-studied questions. However, for the informal conversational style, participants scored higher on hard questions than easy questions amongst studied questions. Embodiment and conversational style had no impact on behavioral engagement. Overall, participants rated the chatbot above average in terms of its usability, usefulness, ease of use, and affective engagement. We conclude that chatbots are an effective study tool, but they may be better suited for learning easy, surface-level knowledge. Additionally, an informal conversational style may be preferred since it matches the linguistic features used by human tutors. Limitations and future directions for research are discussed.
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Mai, Vanessa, Johanna Nickel, Anna Gähl, Rebecca Rutschmann, and Anja Richert. "AI-based Chatbot Coaching for Interdisciplinary Project Teams: The Acceptance of AI-based in Comparison to Rule-based Chatbot Coaching." In 12th International Conference on Human Interaction and Emerging Technologies (IHIET 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005483.

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Project-based work is integral in corporate and academic settings, where coaching plays a crucial role in enhancing team performance and project success. To streamline this process and improve scalability, we developed a coaching chatbot at TH Köln/University of Applied Sciences to assist interdisciplinary teams. Utilizing a systemic coaching approach, the chatbot prompts self-reflection through solution-focused questions. We collaboratively created it with student facilitators and lecturers and tested it during a University-wide Interdisciplinary Project Week in November 2023. The pilot study involved two versions of the chatbot: a rule-based system and a hybrid model incorporating generative AI capabilities. As part of the field test, we analysed its acceptance: How effective is the chatbot in supporting projects groups and facilitating reflection processes? Are there differences in acceptance between the two chatbots? Half of the project groups in the one-week course used the rule-based chatbot, while the other half of the project groups were provided with the AI-based chatbot. 134 students participated and used the chatbots at the end of each day of the project week. The results of this study indicate that our test subjects accepted both types of chatbots with moderate to good scores in acceptance. However, the AI-based chatbot fared significantly worse in terms of performance expectancy and effort expectancy. This is possibly due to the fact that hybrid coaching chatbots are neither widely developed nor researched. We conclude that regardless of the technical basis of such a chatbot, conversation design and prompting is an essential part of chatbot development and contributes significantly to acceptance. This study demonstrates the potential of chatbots in supporting group coaching, not only in educational settings but also in corporate environments where they can aid agile project teams. This research marks one of the initial explorations into the acceptance of group coaching through chatbots.
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Raimer, Stephan, and Marleen Vanhauer. "Heuristic Evaluation of Public Service Chatbots." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001712.

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In recent years, chatbots have been adopted in business contexts and also for public services at a growing rate. Chatbots provide dialogue interfaces combining visual elements with natural conversation. Good Conversational Design in this context covers the topics of Natural-Language Processing (NLP) and Dialogue Management (DM). Few attention has been paid to the usability evaluation of conversational interfaces (Höhn & Bongard-Blanchy, 2021). The present paper builds upon the work by Höhn & Bongard-Blanchy by applying their framework of conversational heuristics to evaluate a set of public service chatbots operated in the federal state of Schleswig-Holstein. Thus, for each public service chatbot, a usability score is established and typical characteristics of public service chatbots in general are summarized. We discuss results by comparing the overall scores, weaknesses and strengths of each chatbot. In addition, we reflect on our experience in the application of the framework as well as highlight possible optimization potentials. Concludingly, this paper collects UX recommendations for public service chatbots.
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You, Hsiao-Chen, and Han-Yu Weng. "Effects of nonverbal communication on Chatbot's perceived personality and user satisfaction." In 9th International Conference on Kansei Engineering and Emotion Research (KEER2022). Kansei Engineering and Emotion Research (KEER), 2022. http://dx.doi.org/10.5821/conference-9788419184849.26.

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As artificial intelligence develops rapidly, companies have created exclusive chatbots to facilitate conversational commerce and establish an emotional connection between their brands and their customers. Therefore, shaping the chatbot personality to match the brand image is often the focus of chatbot design. Two studies were conducted to investigate how nonverbal communication elements (avatar, sticker, emoji) affect users' judgment of chatbot personality and explore the effect of chatbot personality on user satisfaction. In Study 1, Kansei engineering was adopted to conduct an online survey using six combinations of nonverbal elements as experimental conditions and the five dimensions of the Brand Personality Scale as Kansei vocabularies. The results revealed that the three nonverbal elements did affect users' perceptions of chatbot personalities; however, the impacts of each element on different personality dimensions varied. In Study 2, based on Study 1, two crowdfunding chatbots with distinct personality traits, sincere and insincere, were developed as the experimental conditions to interact with participants within FB messenger. One hundred fifty valid questionnaires and the click rate of participants during the experiment were collected to measure participants' satisfaction. The results showed that participants were more satisfied with the sincere chatbot than the insincere chatbot. In addition, the personality of the chatbots also affected the participants' judgment of the quality of the messages as well as their willingness to use the chatbots.
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Iyer, Purohit, Arnab Sarkar, Karthik Prakash, and P. Mohamed Fathimal. "Medical Diagnosis through Chatbots." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-r4i40i.

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Medical attention is critical to living a healthy life. If you have a health concern, however, it is quite difficult to seek medical help. The idea is to create a medical chatbot that can assess symptoms and provide a list of illnesses the user might have using AI and other biometric parameters. In medical diagnosis, artificial intelligence aids in medical decision making, management, automation, administration, and workflows. It can be used to diagnose cancer, triage critical findings in medical imaging, flag acute abnormalities, assist radiologists in prioritizing life-threatening cases, diagnose cardiac arrhythmias, predict stroke outcomes, and aid in chronic disease management. Medical chatbots were created with the goal of lowering medical costs and increasing access to medical information. Some chatbots act as medical guides, assisting patients in becoming more conscious of their ailment and improving their overall health. Users will undoubtedly profit from chatbots if they can identify a variety of illnesses and provide the necessary information that may help the user to understand the predicament, he/she might be facing. The main idea is to create a preliminary diagnosis chatbot that allows patients to participate in medical research and provide a customized analysis report based on their symptoms
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Alvarenga, Judson Henrique, Igor Dalepiane, Lucas Fell, and Maicon Bernardino. "Uma Revisão Sistemática da Literatura sobre Chatbots na Educação: Desenvolvimento e Aplicações." In Workshop sobre Bots na Engenharia de Software, 30–39. Sociedade Brasileira de Computação, 2024. http://dx.doi.org/10.5753/wbots.2024.3975.

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O crescente uso de chatbots na educação destaca sua importância na melhoria da experiência de aprendizagem. Este estudo realiza uma revisão sistemática da literatura sobre o desenvolvimento de chatbots, focando em suas aplicações. Os resultados mostram que os chatbots podem automatizar tarefas, responder a dúvidas e personalizar o aprendizado, beneficiando tanto alunos quanto professores. No entanto, desafios como a necessidade de alinhar avaliações e explorar a adaptabilidade dos chatbots foram identificados. Uma proposta de um chatbot personalizado para os cursos de graduação visa atender às necessidades dos estudantes, promovendo um ambiente educacional mais eficiente e acessível.
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Fairoose Abedin, Afia, Amirul Islam Al Mamun, Rownak Jahan Nowrin, Amitabha Chakrabarty, Moin Mostakim, and Sudip Kumar Naskar. "A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot." In 10th International Conference on Natural Language Processing (NLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112309.

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In recent times, a large number of people have been involved in establishing their own businesses. Unlike humans, chatbots can serve multiple customers at a time, are available 24/7 and reply in less than a fraction of a second. Though chatbots perform well in task-oriented activities, in most cases they fail to understand personalized opinions, statements or even queries which later impact the organization for poor service management. Lack of understanding capabilities in bots disinterest humans to continue conversations with them. Usually, chatbots give absurd responses when they are unable to interpret a user’s text accurately. Extracting the client reviews from conversations by using chatbots, organizations can reduce the major gap of understanding between the users and the chatbot and improve their quality of products and services.Thus, in our research we incorporated all the key elements that are necessary for a chatbot to analyse andunderstand an input text precisely and accurately. We performed sentiment analysis, emotion detection, intent classification and named-entity recognition using deep learning to develop chatbots with humanistic understanding and intelligence. The efficiency of our approach can be demonstrated accordingly by the detailed analysis.
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Zhang, Xiang, Yan Liu, Gong Chen, and Sheng-hua Zhong. "LightBlue: Nurture Your Personal Chatbot." In 9th International Conference on Artificial Intelligence and Applications (AIAP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120401.

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Chatbot has long been an important research topic in artificial intelligence and attracts lots of attention recently. Despite significant advancements in language ability, the interactions between users and chatbots are rather generic, short-term, and transnational. It has always been challenging to develop truly personal chatbots and even more difficult to establish longterm, affective connections. This paper first brings up “nurture” as a new interaction mode with chatbots. We introduce the nurture framework and accordingly design the learning algorithm and nurture functions. Then we present LightBlue – a platform that allows non-professionals to nurture personal chatbots from scratch. Experiments on both closed- and open-domain tasks validate the proposed framework and demonstrate a promising method for facilitating long-term interaction between users and chatbots.
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Reports on the topic "Chatbots"

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Hooker, Reece. Next-generation chatbots explained. Monash University, March 2023. http://dx.doi.org/10.54377/2249-3568.

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Garcia Brustenga, Guillem, Marc Fuertes Alpiste, and Núria Molas Castells. Briefing Paper: Chatbots in Education. Universitat Oberta de Catalunya (UOC), September 2018. http://dx.doi.org/10.7238/elc.chatbots.2018.

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Singh, Naresh. From chits to chatbots: cheating in India's education system. Edited by Chandan Nandy and Suzannah Lyons. Monash University, October 2024. http://dx.doi.org/10.54377/d437-0e70.

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Shabelnyk, Tetiana V., Serhii V. Krivenko, Nataliia Yu Rotanova, Oksana F. Diachenko, Iryna B. Tymofieieva, and Arnold E. Kiv. Integration of chatbots into the system of professional training of Masters. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4439.

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The article presents and describes innovative technologies of training in the professional training of Masters. For high-quality training of students of technical specialties, it becomes necessary to rethink the purpose, results of studying and means of teaching professional disciplines in modern educational conditions. The experience of implementing the chatbot tool in teaching the discipline “Mathematical modeling of socio-economic systems” in the educational and professional program 124 System Analysis is described. The characteristics of the generalized structure of the chatbot information system for investment analysis are presented and given: input information, information processing system, output information, which creates a closed cycle (system) of direct and feedback interaction. The information processing system is represented by accounting and analytical data management blocks. The investment analysis chatbot will help masters of the specialty system analysis to manage the investment process efficiently based on making the right decisions, understanding investment analysis in the extensive structure of financial management and optimizing risks in these systems using a working mobile application. Also, the chatbot will allow you to systematically assess the disadvantages and advantages of investment projects or the direction of activity of a system analyst, while increasing interest in performing practical tasks. A set of software for developing a chatbot integrated into training is installed: Kotlin programming, a library for network interaction Retrofit, receiving and transmitting data, linking processes using the HTTP API. Based on the results of the study, it is noted that the impact of integrating a chatbot into the training of Masters ensures the development of their professional activities, which gives them the opportunity to be competent specialists and contributes to the organization of high-quality training.
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Ajzenman, Nicolás, Gregory Elacqua, Analia Jaimovich, and Graciela Pérez-Nuñez. Humans versus Chatbots: Scaling-up behavioral interventions to reduce teacher shortages. Inter-American Development Bank, August 2023. http://dx.doi.org/10.18235/0005059.

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Empirical results in economics often stem from success in controlled experimental settings, but often fail when scaled up. This study presents a behavioral intervention and a scalable equivalent aimed at reducing teacher shortages by motivating high school students to pursue an education degree. The intervention was delivered through WhatsApp chats by trained human promoters (humans arm) and rule-based Chatbots programmed to closely replicate the humans program (bots arm). Results show that the humans arm successfully increased high-school students demand for and enrollment in education majors, particularly among high-performing students. The bots arm showed positive but smaller and statistically insignificant effects. These findings indicate that a relatively low-cost intervention can effectively reduce teacher shortages, but scaling up such interventions may have limitations. Therefore,testing scalable solutions during the design stage of experiments is crucial.
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Melnyk, Yuriy, and Iryna Pypenko. The legitimacy of artificial intelligence and the role of ChatBots in scientific publications. KRPOCH, 2023. http://dx.doi.org/10.26697/krpoch.melnyk.pypenko.report.2023.

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Rodrigues do Nascimento Junior, Edmilson. Annotated Bibliography - A conversation on artificial intelligence, chatbots, and plagiarism in higher education. (King, 2023). ResearchHub Technologies, Inc., December 2023. http://dx.doi.org/10.55277/researchhub.l67b3be8.

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Cao, Larry. IV. Chatbot, Knowledge Graphs, and AI Infrastructure. CFA Institute Research Foundation, April 2023. http://dx.doi.org/10.56227/23.1.10.

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Expert contributors discuss AI and big data applications that are being developed for financial services, such as AI-powered intelligent customer service systems; “factories” for data processing, AI, simulation, and visualization; and symbolic AI.
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Walsh, Toby. Chatbot revolution risks becoming a race of the reckless. Edited by Reece Hooker. Monash University, March 2023. http://dx.doi.org/10.54377/3ed9-8eea.

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Priadko, Andrii O., Kateryna P. Osadcha, Vladyslav S. Kruhlyk, and Volodymyr A. Rakovych. Development of a chatbot for informing students of the schedule. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3744.

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The article describes the process of developing a chatbot to provide students with information about schedules using the Telegram mobile messenger. During the research, the following tasks have been performed: the analysis of notification systems for their use in the educational process, identification of problems of notifying students about the schedule (dynamic environment, traditional presentation of information, lack of round-the-clock access), substantiation of the choice of mobile technologies and Telegram messenger, determination of the requirements to the software, generalization of the chatbot functioning features, description of the structure, functionality of the program to get information about the schedule using a chatbot. The following tasks have been programmatically implemented: obtaining data from several pages of a spreadsheet (faculty / institute, red / green week, group number, day of the week, period number, discipline name, information about the teacher); presentation of data in a convenient form for the messenger (XML); implementation of the mechanism of convenient presentation of data in the messenger (chatbot). Using Python and the Telegram API, the software has been designed to increase students; immediacy in getting the information about the schedules, minimizing the time spent, and optimizing of planning of student activities and higher education institution functioning.
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