Relevant bibliographies by topics / Chatbot

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Academic literature on the topic 'Chatbot'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 March 2025

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Journal articles on the topic "Chatbot"

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Musheyev, David, Alexander Pan, Preston Gross, Daniel Kamyab, Peter Kaplinsky, Mark Spivak, Marie A. Bragg, Stacy Loeb, and Abdo E. Kabarriti. "Readability and Information Quality in Cancer Information From a Free vs Paid Chatbot." JAMA Network Open 7, no. 7 (July 26, 2024): e2422275. http://dx.doi.org/10.1001/jamanetworkopen.2024.22275.

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ImportanceThe mainstream use of chatbots requires a thorough investigation of their readability and quality of information.ObjectiveTo identify readability and quality differences in information between a free and paywalled chatbot cancer-related responses, and to explore if more precise prompting can mitigate any observed differences.Design, Setting, and ParticipantsThis cross-sectional study compared readability and information quality of a chatbot’s free vs paywalled responses with Google Trends’ top 5 search queries associated with breast, lung, prostate, colorectal, and skin cancers from January 1, 2021, to January 1, 2023. Data were extracted from the search tracker, and responses were produced by free and paywalled ChatGPT. Data were analyzed from December 20, 2023, to January 15, 2024.ExposuresFree vs paywalled chatbot outputs with and without prompt: “Explain the following at a sixth grade reading level: [nonprompted input].”Main Outcomes and MeasuresThe primary outcome measured the readability of a chatbot’s responses using Flesch Reading Ease scores (0 [graduate reading level] to 100 [easy fifth grade reading level]). Secondary outcomes included assessing consumer health information quality with the validated DISCERN instrument (overall score from 1 [low quality] to 5 [high quality]) for each response. Scores were compared between the 2 chatbot models with and without prompting.ResultsThis study evaluated 100 chatbot responses. Nonprompted free chatbot responses had lower readability (median [IQR] Flesh Reading ease scores, 52.60 [44.54-61.46]) than nonprompted paywalled chatbot responses (62.48 [54.83-68.40]) (P < .05). However, prompting the free chatbot to reword responses at a sixth grade reading level was associated with increased reading ease scores than the paywalled chatbot nonprompted responses (median [IQR], 71.55 [68.20-78.99]) (P < .001). Prompting was associated with increases in reading ease in both free (median [IQR], 71.55 [68.20-78.99]; P < .001)and paywalled versions (median [IQR], 75.64 [70.53-81.12]; P < .001). There was no significant difference in overall DISCERN scores between the chatbot models, with and without prompting.Conclusions and RelevanceIn this cross-sectional study, paying for the chatbot was found to provide easier-to-read responses, but prompting the free version of the chatbot was associated with increased response readability without changing information quality. Educating the public on how to prompt chatbots may help promote equitable access to health information.
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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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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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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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Juliansyah, Dias, Hannie Hannie, and Ade Andri Hendriadi. "Penerapan Uji-T Independen untuk Sistem Chatbot Gaotek." Jurnal Syntax Admiration 5, no. 6 (June 15, 2024): 2137–46. http://dx.doi.org/10.46799/jsa.v5i6.1224.

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Artificial intelligence has become a primary focus in the development of computer systems that mimic human brain functions. One application is the development of Chatbots to assist in various fields. In this context, the research aims to evaluate the effectiveness of two types of Chatbots, namely Chatbot Gemini and ChatGPT, in providing customer service at GAOTek Inc. Through the application of Independent T-tests, the research findings indicate a significant difference in the performance of the two Chatbots. ChatGPT exhibits a lower average score compared to Chatbot Gemini, demonstrating significant efficiency superiority. The Independent T-test statistical results show the rejection of the null hypothesis, indicating a significant difference between the two groups. The alternative hypothesis is accepted, confirming a distinction in the performance of the two Chatbots. Thus, ChatGPT is declared as the most efficient Chatbot in the context of customer service at GAOTek Inc. These results are expected to provide valuable insights for the development and implementation of Chatbots in enhancing customer service effectiveness across various industries, particularly at GAOTek Inc.
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Lapeña, José Florencio. "The Updated World Association of Medical Editors (WAME) Recommendations on Chatbots and Generative AI in Relation to Scholarly Publications and International Committee of Medical Journal Editors (ICMJE) Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals (May 2023)." Philippine Journal of Otolaryngology Head and Neck Surgery 38, no. 1 (June 4, 2023): 4. http://dx.doi.org/10.32412/pjohns.v38i1.2127.

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On January 20, 2023, the World Association of Medical Editors published a policy statement on Chatbots, ChatGPT, and Scholarly Manuscripts: WAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications.1 There were four recommendations, namely: 1. Chatbots cannot be authors; 2. Authors should be transparent when chatbots are used and provide information about how they were used; 3. Authors are responsible for the work performed by a chatbot in their paper (including the accuracy of what is presented, and the absence of plagiarism) and for appropriate attribution of all sources (including for material produced by the chatbot); and 4. Editors need appropriate tools to help them detect content generated or altered by AI and these tools must be available regardless of their ability to pay.1 This statement was spurred in part by some journals beginning to publish papers in which chatbots such as ChatGPT were listed as co-authors.2 First, only humans can be authors. Chatbots cannot be authors because they cannot meet authorship requirements “as they cannot understand the role of authors or take responsibility for the paper.”1 In particular, they cannot meet the third and fourth ICMJE criteria for authorship, namely “Final approval of the version to be published” and “Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.”1,3 Moreover, “a chatbot cannot understand a conflict of interest statement, or have the legal standing to sign (such a) statement,” nor can they “hold copyright.”1 Because authors submitting a manuscript must ensure that all those named as authors meet ICMJE authorship criteria, chatbots clearly should not be included as authors.1 Second, authors should acknowledge the sources of their materials. When chatbots are used, authors “should declare this fact and provide full technical specifications of the chatbot used (name, version, model, source) and method of application in the paper they are submitting (query structure, syntax),” “consistent with the ICMJE recommendation of acknowledging writing assistance.”1,4 Third, authors must take public responsibility for their work; “Human authors of articles written with the help of a chatbot are responsible for the contributions made by chatbots, including their accuracy,” and “must be able to assert that there is no plagiarism in their paper, including in text produced by the chatbot.”1Consequently, authors must “ensure … appropriate attribution of all quoted material, including full citations,” “seek and cite the sources that support,” as well as oppose (since chatbots can be designed to omit counterviews), the chatbot’s statements.1 Fourth, to facilitate all this, medical journal editors (who “use manuscript evaluation approaches from the 20th century”) “need appropriate (digital) tools … that will help them evaluate … 21st century … content (generated or altered by AI) efficiently and accurately.”1
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Abu-Haifa, Mohammad, Bara'a Etawi, Huthaifa Alkhatatbeh, and Ayman Ababneh. "Comparative Analysis of ChatGPT, GPT-4, and Microsoft Copilot Chatbots for GRE Test." International Journal of Learning, Teaching and Educational Research 23, no. 6 (June 30, 2024): 327–47. http://dx.doi.org/10.26803/ijlter.23.6.15.

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This paper presents an analysis of how well three artificial intelligence chatbots: Copilot, ChatGPT, and GPT-4, perform when answering questions from standardized tests, mainly the Graduate Record Examination (GRE). A total of 137 questions with different forms of quantitative reasoning and 157 questions with verbal categories were used to assess the chatbot’s capabilities. This paper presents the performance of each chatbot across various skills and styles tested in the exam. The proficiency of the chatbots in addressing image-based questions is also explored, and the uncertainty level of each chatbot is illustrated. The results show varying degrees of success among the chatbots. ChatGPT primarily makes arithmetic errors, whereas the highest percentage of errors made by Copilot and GPT-4 are conceptual. However, GPT-4 exhibited the highest success rates, particularly in tasks involving complex language understanding and image-based questions. Results highlight the ability of these chatbots in helping examinees to pass the GRE with a high score, which encourages the use of them in test preparation. The results also show the importance of preventing access to similar chatbots when tests are conducted online, such as during the COVID-19 pandemic, to ensure a fair environment for all test takers competing for higher education opportunities.
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Wang, Kaicheng. "From ELIZA to ChatGPT: A brief history of chatbots and their evolution." Applied and Computational Engineering 39, no. 1 (February 21, 2024): 57–62. http://dx.doi.org/10.54254/2755-2721/39/20230579.

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Over the years, chatbots have grown to be used in a variety of industries. From their humble beginnings to their current prominence, chatbots have come a long way. From the earliest chatbot ELIZA in the 1960s to todays popular Chatgpt, chatbot language models, codes, and databases have improved greatly with the advancement of artificial intelligence technology.This paper introduces the development of chatbots through literature review and theoretical analysis. It also analyzes and summarizes the advantages and challenges of chatbots according to the current status of chatbot applications and social needs. Personalized interaction will be an important development direction for chatbots, because providing personalized responses through user data analysis can provide users with a personalized experience, thus increasing user engagement and satisfaction.
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Srivastava, Praveen Ranjan, Harshit Kumar Singh, Surabhi Sakshi, Justin Zuopeng Zhang, and Qiuzheng Li. "Identifying Alternative Options for Chatbots With Multi-Criteria Decision-Making." Journal of Database Management 35, no. 1 (July 17, 2024): 1–25. http://dx.doi.org/10.4018/jdm.345917.

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Artificial intelligence-powered chatbot usage continues to grow worldwide, and there is ongoing research to identify features that maximize the utility of chatbots. This study uses the multi-criteria decision-making (MCDM) method to find the best available alternative chatbot for task completion. We identify chatbot evaluation criteria from literature followed by inputs from experts using the Delphi method. We apply CRITIC to evaluate the relative importance of the specified criteria. Finally, we list popular alternatives of chatbots and features offered and apply WASPAS and EDAS techniques to rank the available alternatives. The alternatives explored in this study include YOU, ChatGPT, PerplexityAI, ChatSonic, and CharacterAI. Both methods yield identical results in ranking, with ChatGPT emerging as the most preferred alternative based on the criteria identified.
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Holland, Alexis M., William R. Lorenz, Jack C. Cavanagh, Neil J. Smart, Sullivan A. Ayuso, Gregory T. Scarola, Kent W. Kercher, et al. "Comparison of Medical Research Abstracts Written by Surgical Trainees and Senior Surgeons or Generated by Large Language Models." JAMA Network Open 7, no. 8 (August 2, 2024): e2425373. http://dx.doi.org/10.1001/jamanetworkopen.2024.25373.

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ImportanceArtificial intelligence (AI) has permeated academia, especially OpenAI Chat Generative Pretrained Transformer (ChatGPT), a large language model. However, little has been reported on its use in medical research.ObjectiveTo assess a chatbot’s capability to generate and grade medical research abstracts.Design, Setting, and ParticipantsIn this cross-sectional study, ChatGPT versions 3.5 and 4.0 (referred to as chatbot 1 and chatbot 2) were coached to generate 10 abstracts by providing background literature, prompts, analyzed data for each topic, and 10 previously presented, unassociated abstracts to serve as models. The study was conducted between August 2023 and February 2024 (including data analysis).ExposureAbstract versions utilizing the same topic and data were written by a surgical trainee or a senior physician or generated by chatbot 1 and chatbot 2 for comparison. The 10 training abstracts were written by 8 surgical residents or fellows, edited by the same senior surgeon, at a high-volume hospital in the Southeastern US with an emphasis on outcomes-based research. Abstract comparison was then based on 10 abstracts written by 5 surgical trainees within the first 6 months of their research year, edited by the same senior author.Main Outcomes and MeasuresThe primary outcome measurements were the abstract grades using 10- and 20-point scales and ranks (first to fourth). Abstract versions by chatbot 1, chatbot 2, junior residents, and the senior author were compared and judged by blinded surgeon-reviewers as well as both chatbot models. Five academic attending surgeons from Denmark, the UK, and the US, with extensive experience in surgical organizations, research, and abstract evaluation served as reviewers.ResultsSurgeon-reviewers were unable to differentiate between abstract versions. Each reviewer ranked an AI-generated version first at least once. Abstracts demonstrated no difference in their median (IQR) 10-point scores (resident, 7.0 [6.0-8.0]; senior author, 7.0 [6.0-8.0]; chatbot 1, 7.0 [6.0-8.0]; chatbot 2, 7.0 [6.0-8.0]; P = .61), 20-point scores (resident, 14.0 [12.0-7.0]; senior author, 15.0 [13.0-17.0]; chatbot 1, 14.0 [12.0-16.0]; chatbot 2, 14.0 [13.0-16.0]; P = .50), or rank (resident, 3.0 [1.0-4.0]; senior author, 2.0 [1.0-4.0]; chatbot 1, 3.0 [2.0-4.0]; chatbot 2, 2.0 [1.0-3.0]; P = .14). The abstract grades given by chatbot 1 were comparable to the surgeon-reviewers’ grades. However, chatbot 2 graded more favorably than the surgeon-reviewers and chatbot 1. Median (IQR) chatbot 2-reviewer grades were higher than surgeon-reviewer grades of all 4 abstract versions (resident, 14.0 [12.0-17.0] vs 16.9 [16.0-17.5]; P = .02; senior author, 15.0 [13.0-17.0] vs 17.0 [16.5-18.0]; P = .03; chatbot 1, 14.0 [12.0-16.0] vs 17.8 [17.5-18.5]; P = .002; chatbot 2, 14.0 [13.0-16.0] vs 16.8 [14.5-18.0]; P = .04). When comparing the grades of the 2 chatbots, chatbot 2 gave higher median (IQR) grades for abstracts than chatbot 1 (resident, 14.0 [13.0-15.0] vs 16.9 [16.0-17.5]; P = .003; senior author, 13.5 [13.0-15.5] vs 17.0 [16.5-18.0]; P = .004; chatbot 1, 14.5 [13.0-15.0] vs 17.8 [17.5-18.5]; P = .003; chatbot 2, 14.0 [13.0-15.0] vs 16.8 [14.5-18.0]; P = .01).Conclusions and RelevanceIn this cross-sectional study, trained chatbots generated convincing medical abstracts, undifferentiable from resident or senior author drafts. Chatbot 1 graded abstracts similarly to surgeon-reviewers, while chatbot 2 was less stringent. These findings may assist surgeon-scientists in successfully implementing AI in medical research.
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Dissertations / Theses on the topic "Chatbot"

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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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Jusko, Ján. "Chatbot pro Smart Cities." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403204.

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The aim of this work is to simplify access to information for citizens of the city of Brno and at the same time to innovate the way of communication between the citizen and his city. The problem is solved by creating a conversational agent - chatbot Kroko. Using artificial intelligence and a Czech language analyzer, the agent is able to understand and respond to a certain set of textual, natural language queries. The agent is available on the Messenger platform and has a knowledge base that includes data provided by the city council. After conducting an extensive user testing on a total of 76 citizens of the city, it turned out that up to 97\% of respondents like the idea of a city-oriented chatbot and can imagine using it regularly. The main finding of this work is that the general public can easily adopt and effectively use a chatbot. The results of this work motivate further development of practical applications of conversational agents.
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Jonsson, Sofia, and Jenny Bredmar. "Chatbot - Magic in a box? : A study of a chatbot in a Swedish bank." Thesis, Uppsala universitet, Institutionen för informatik och media, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-355381.

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Artificial Intelligence (AI) is a topic, which is widely discussed around the globe. One branch of AI is Chatbot (CB) technology that uses Natural Language Processing to understand, reply and communicate with humans. Increasingly, CB has gained more popularity in many companies because of its contribution to productivity and efficiency. However, less is said about organizations expectations, use, and challenges of the CB. This exploratory research tries to get a more organizational perspective of this new phenomenon. To do so, we conducted interviews with project members in a large banking organization that utilizes CB. This study contributed three major conclusions: first, the importance of making sure employees understand the importance and scale of training in regard to the use of a chatbot. Second, the results also suggest that the CB performs simple tasks but still has the ability to save time for employees who use it. Third, this study acknowledged the potential in CB and the importance of proactively embracing it today to not fall behind the curve. The result contributes to the research area of CB with insights from an organizational perspective.
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Ahlström, Sara, and Patricia Jansson. "Chatbot den digitalakundtjänstmedarbetaren : En kvalitativ studie kring hur lojalitetsfaktorernapåverkas av konsumentmötet med en chatbot." Thesis, Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42440.

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Titel: Chattbot den digitala kundtjänstmedarbetaren: En kvalitativ studie kring hur lojalitetsfaktorerna påverkas av konsumentmötet med en chattbot. Ämne: Kandidatuppsats i företagsekonomiinriktning marknadsföring, 15hp Författare: Sara Ahlström & Patricia Jansson Nyckelord: Varumärkeslojalitet, artificiell intelligens, chattbot   Syfte: Syftet med studien är att ur ett business-to-consumer perspektiv genomföra en djupgående undersökning av hur faktorerna bakom varumärkeslojalitet påverkas av konsumentmötet med en chattbot. Genom den ökade förståelsen eftersträvas att utveckla ett teoretiskt ramverk.   Metod: Studien är av det kvalitativa slaget och har genomförts med en abduktiv forskningsansats. Studiens slutliga resultat grundar sig i genomförda datorbaserade chattintervjuer med konsumenter.   Slutsats: Utfallet av studien har utökat förståelsen för hur faktorerna vilka ligger till grund för byggandet av varumärkeslojalitet förändras med den digitala teknologin chattbots. Genom studien har en förståelse för vilka komponenter bakom lojalitetsfaktorerna som är av stor vikt. Viktiga komponenter vilka uppmärksammats är utfallet av chattbotens service-kvalité bestående av kundnöjdhet och förtroende, och kvalité av interaktionen bestående av kommunikation och personlighet   Kunskapsbidrag: Praktiska och teoretiska kunskaper kring chattbots påverkan på kundnöjdhet, relationer, kommunikation och avsaknaden av den mänskliga kontakten har lyfts fram. Genom det teoretiska ramverket belyses viktiga delar att ta hänsyn till kring skapandet och utvecklandet av chattbots, vilket är av betydelse för företag som vill skapa en positiv upplevelse i mötet med den digitala medarbetaren
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Roghult, Alexander. "Chatbot trained on movie dialogue." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157637.

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A chatbot is a computer program that engages in written or spoken conversation with a human user. This project aims to investigate the possibility of training a chatbot in using movie dialogue in generating the response. Movie dialogue can be found in both movie scripts as well as subtitles, though using subtitles is much easier as they follow a special formatting. Using one subtitle as a response to each word found in the preceding subtitle, the implemented chatbot links together subtitles. The responses are stored in a frequency distribution table that maps each word to all responses found. Though the responses generated by the chatbot were not desirable, the responsese tmost often contained responses which would be more fitting. The result drawn from hisis that ,with further work and improvement,the chatbot could perform acceptably
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Amilon, Mattias. "Chatbot with common-sense database." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166745.

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In 1950 Alan Turing introduced the famous “Turing test” which tests if a machine can be as intelligent as a human by testing if it can communicate with a person in a “human” way. Inspired by this test, numerous so called chatbots, in the form of computer programs, that manage a written dialogue have been created. A so called commonsensedatabase consists of data that most humans would know andconsider as common knowledge, something that computers generally do not know very muchabout. This report describes the process of an attempt to implement a simple chatbot using the common-sense database ConceptNet. The behaviour, or the human-likeness, of this chatbot was then compared to that of the classic chatbot ELIZA and the 2008 Loebner prize winning chatbot Elbot, through a series of user tests. The results indicate that using a common-sense database for a chatbot shows some promisefor further investigation.
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Krantz, Amandus, and Petrus Lindblom. "Generating Topic-Based Chatbot Responses." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14800.

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With the rising popularity of chatbots, not just in entertainment but in e-commerce and online chat support, it’s become increasingly important to be able to quickly set up chatbots that can respond to simple questions. This study examines which of two algorithms for automatic generation of chatbot knowledge bases, First Word Search or Most Significant Word Search, is able to generate the responses that are the most relevant to the topic of a question. It also examines how text corpora might be used as a source from which to generate chatbot knowledge bases. Two chatbots were developed for this project, one for each of the two algorithms that are to be examined. The chatbots are evaluated through a survey where the participants are asked to choose which of the algorithms they thought chose the response that was most relevant to a question. Based on the survey we conclude that Most Significant Word Search is the algorithm that picks the most relevant responses. Most Significant Word Search has a significantly higher chance of generating a response that is relevant to the topic. However, how well a text corpus works as a source for knowledge bases depends entirely on the quality and nature of the corpus. A corpus consisting of written dialogue is likely more suitable for conversion into a knowledge base.
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Tor, Kjäll. "Visualization of Chatbot Survey Results." Thesis, Uppsala universitet, Människa-datorinteraktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414841.

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Chatbots are an increasingly popular technique that has seen recent steps in development thanks to artificial intelligence. There is research conducted about chatbots in different areas, but one area that is overlooked is the presentation of data collected by the chatbot. This study aimed to explore what to think about in terms of visualizations when designing an interface in order to present chatbots results to novice users. Conducting a user study with several iterations of design, the research question was: How do you visualize the results of a chatbot survey for novice users to facilitate the understanding of the data? To answer this question, four design iterations with a total of 17 participants were conducted, resulting in a final prototype. The data gathered was then analyzed using thematic analysis. The results failed to help answering the research question, but did give some suggestions on interface visualization, mainly the importance of using visualizations that are carefully selected to improve the understanding of the data presented. Moreover, this study suggests the importance of focusing on the participant’s deeper level of understanding and their conceptual models.
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Novák, Miroslav. "Chatbot v podnikovém informačním systému." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403114.

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This diploma thesis deals with problems of development of chatbots. The theoretical part of the thesis introduces the concept of the conversational interface in general and analyzes available technologies for its development. The practical part deals with the design and implementation of a particular chatbot, whose goal is to be a virtual assistant in the process of selecting and purchasing goods. This is accomplished by connecting the chatbot to the product information management system using OData web services. One of the biggest problems was to determine the order of questions asked about product properties. For the implementation was used decision tree theory.
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Choque, Díaz Marilyn Lizet. "Chatbot académico utilizando tecnologías cognitivas." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2018. http://hdl.handle.net/10757/624418.

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El presente proyecto “Chatbot académico utilizando tecnologías cognitivas” tiene como objetivo principal implementar un modelo de tecnología cognitiva para mejorar los servicios de soporte académico con chatbots en instituciones universitarias. La diferencia entre los modelos convencionales y el modelo propuesto en este proyecto radica, aparte de basarse en computación cognitiva, en adicionar patrones Customer Experience, los cuales mejoran sustancialmente la interacción que se tiene con el estudiante, así como la experiencia y satisfacción con su institución universitaria. El surgimiento de nuevas tecnologías y la omnipresencia de Internet están brindando oportunidades de nuevas formas de interacción entre clientes y proveedores de servicios. Una de esas tecnologías son los chatbots capaces de responder a consultas del cliente permitiendo ahorrar tiempo y trabajo humano. Los chatbots han abierto una nueva puerta para la comunicación y, sobre todo, en cuanto a la atención al cliente se refiere. Existe una creciente expectación sobre los chatbots y sobre cómo afectarán en el futuro de la experiencia del cliente, ya que se están utilizando cada vez más para liberar a los humanos de sus tareas dentro de un centro de atención al cliente y, aunque parezca contradictorio, están ayudando a brindar una experiencia más personalizada. El aporte de este proyecto es la propuesta de un modelo de tecnología cognitiva para mejorar los servicios de soporte académico con chatbots, que pueda impulsar y potenciar iniciativas que propicien un mayor aprovechamiento de la interacción con el estudiante en la Universidad Peruana de Ciencias Aplicadas (UPC).
This project "Academic chatbot using cognitive technologies" aims to implement a cognitive technology model to enhance academic support services with chatbots at university institutions. The difference between the conventional models and the model proposed in this project lies, apart from being based on cognitive computing, in adding Customer Experience patterns. This could substantially improve the interaction with student, as well as the experience and satisfaction with its university institution. The emergence of new technologies and the pervasiveness of the Internet are providing opportunities for new forms of interaction between customers and service providers. One of these technologies are chatbots capable of responding to customer queries saving time and human work. Chatbots have opened a new door for communication and, above all, in terms of customer service. There is growing expectation about chatbots and how they will affect the future of the customer experience as they are increasingly being used to free humans from their tasks within a customer service center and, although it may seem contradictory, they are helping to provide a more personalized experience. The contribution of this project is the proposal of a cognitive technology model to enhance academic support services with chatbots, which can promote and empower initiatives that facilitate greater use of interaction with the student at Universidad Peruana de Ciencias Aplicadas (UPC).
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Books on the topic "Chatbot"

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Prinz, Konstantin. The Smiling Chatbot. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-40028-6.

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Følstad, Asbjørn, Theo Araujo, Symeon Papadopoulos, Effie L. C. Law, Ewa Luger, Morten Goodwin, and Petter Bae Brandtzaeg, eds. Chatbot Research and Design. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94890-0.

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Singh, Abhishek, Karthik Ramasubramanian, and Shrey Shivam. Building an Enterprise Chatbot. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5034-1.

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Følstad, Asbjørn, Theo Araujo, Symeon Papadopoulos, Effie Lai-Chong Law, Ole-Christoffer Granmo, Ewa Luger, and Petter Bae Brandtzaeg, eds. Chatbot Research and Design. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39540-7.

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Følstad, Asbjørn, Theo Araujo, Symeon Papadopoulos, Effie L. C. Law, Ewa Luger, Morten Goodwin, and Petter Bae Brandtzaeg, eds. Chatbot Research and Design. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68288-0.

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Følstad, Asbjørn, Theo Araujo, Symeon Papadopoulos, Effie L. C. Law, Ewa Luger, Morten Goodwin, and Petter Bae Brandtzaeg, eds. Chatbot Research and Design. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25581-6.

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Følstad, Asbjørn, Theo Araujo, Symeon Papadopoulos, Effie L. C. Law, Ewa Luger, Morten Goodwin, Sebastian Hobert, and Petter Bae Brandtzaeg, eds. Chatbot Research and Design. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54975-5.

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Kurpicz-Briki, Mascha. More than a Chatbot. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37690-0.

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Kurpicz-Briki, Mascha. Mehr als ein Chatbot. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58545-6.

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Concas, Andrea. Banksy: 100 domande, 150 risposte : il primo libro chatbot. Milano: Mondadori, 2019.

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Book chapters on the topic "Chatbot"

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Leung, Rosanna, and Iris Sheungting Lo. "Can ChatGPT Inspire Me? Evaluate Students’ Questioning Techniques on AI Tool for Overcoming Fixation." In Information and Communication Technologies in Tourism 2024, 75–86. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58839-6_9.

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AbstractAI-powered large language models are shaping a new era of learning. Students use AI chatbots for information search and idea inspiration. However, are students’ questioning skills effective enough to interact with an AI Chatbot? This study explores the interaction between students and ChatGPT on idea generation and identifies whether participants can effectively use AI chatbots to simulate creativity for idea generation. The results indicated that, rather than discussing their idea with AI Chatbot for suggestions and recommendations to enhance the existing ideas, many students ask AI to generate more ideas without providing directions. Participants reflected that ChatGPT provided generic ideas and were unsatisfied with its creativity. They are more positive towards using the question guide, developed using SCAMPER questioning technique combined with a narrative approach by the researcher, compared to ChatGPT because the question guide enables perspective-shifting to generate ideas from a new perspective.
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Prakash, Kolla Bhanu, A. J. Sravan Kumar, and G. R. Kanagachidambaresan. "Chatbot." In Programming with TensorFlow, 75–85. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57077-4_9.

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Pesonen, Joonas A. "‘Are You OK?’ Students’ Trust in a Chatbot Providing Support Opportunities." In Learning and Collaboration Technologies: Games and Virtual Environments for Learning, 199–215. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77943-6_13.

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AbstractChatbots show promise as a novel way to provide support to students. However, a central issue with new technologies such as chatbots is whether students trust the technology. In the present study, we use a chatbot to proactively offer academic and non-academic support to students (N = 274) in a Finnish vocational education and training (VET) organization. Students responded to the chatbot with a very high response rate (86%), and almost one-fifth (19%) of the respondents disclosed a need for support. Survey with a subset of participants (N = 49) showed satisfactory trust (total trust score 71% as measured by a human-computer trust scale) and satisfaction (average of 3.83 as measured by a five-point customer satisfaction instrument) with the chatbot. Trust was positively correlated with satisfaction as well as students’ likelihood to respond to the chatbot. Our results show that this kind of approach is applicable for recognizing students’ latent needs for support. Future studies should target the formation of trust in more detail and cultural differences in trusting chatbots.
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Marcondes, Francisco S., José João Almeida, and Paulo Novais. "Chatbot Theory." In Intelligent Data Engineering and Automated Learning – IDEAL 2018, 374–84. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03493-1_40.

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Sabharwal, Navin, and Amit Agrawal. "Chatbot Enrichment." In Cognitive Virtual Assistants Using Google Dialogflow, 119–81. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5741-8_4.

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Waghmare, Charles. "Chatbot Integration." In Introducing Azure Bot Service, 111–46. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4888-1_5.

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Amatullo, Mariana, Bryan Boyer, Jennifer May, and Andrew Shea. "Kamu Chatbot." In Design for Social Innovation, 373–78. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003021360-50.

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Dufaux, Marius O. "Der Chatbot." In essentials, 3–19. Wiesbaden: Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-45722-8_2.

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Segura, Carlos, Àlex Palau, Jordi Luque, Marta R. Costa-Jussà, and Rafael E. Banchs. "Chatbol, a Chatbot for the Spanish “La Liga”." In Lecture Notes in Electrical Engineering, 319–30. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9443-0_28.

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Smestad, Tuva Lunde, and Frode Volden. "Chatbot Personalities Matters." In Internet Science, 170–81. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17705-8_15.

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Conference papers on the topic "Chatbot"

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Manu Mohan, A. J. V., Sowmya Lakshmi B S, Nanditha M, and Mayank H. M. "Medical Chatbot." In 2024 2nd International Conference on Networking, Embedded and Wireless Systems (ICNEWS), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icnews60873.2024.10730884.

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Khoza, Mahlori, Moses Olaifa, Pius Owolawi, and Vusi Malele. "Intelligent Doctor Chatbot." In 2024 4th International Multidisciplinary Information Technology and Engineering Conference (IMITEC), 159–65. IEEE, 2024. https://doi.org/10.1109/imitec60221.2024.10851082.

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V K, Harini, Bhavadharini R M, Charvi Upreti, and Libin Alex. "Mental Health Chatbot." In 2024 IEEE Students Conference on Engineering and Systems (SCES), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/sces61914.2024.10652390.

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Koualty, Rand, Nien-Ying Chou, and Suleiman Alabdallah. "Generative AI Agents, Build a Multilingual ChatGPT-based Customer Service Chatbot." In 2024 2nd International Conference on Foundation and Large Language Models (FLLM), 5–10. IEEE, 2024. https://doi.org/10.1109/fllm63129.2024.10852436.

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Hasan, F., L. Kaveesha, S. Sankalpa, A. Jayasinghe, and S. Sathsarana. "A Study of smart enhancements in specialized AI applications comparing initial versions to upgraded models within sustainable urban planning integration." In Spatial Dynamics: Envisioning tomorrow’s Design through Advanced Practices, 80–89. Faculty of Architecture Research Unit, 2024. https://doi.org/10.31705/faru.2024.9.

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This study examines the development of specialist urban planning chatbots, with a specific focus on the development process from Chatbot 1.0 to Chatbot 2.0. The paper offers a comprehensive examination of the chatbot concept and its use in urban planning. The study highlights the need of involving citizens, sharing information, and making decisions based on data. The report provides an analysis of Chatbot 1.0, including its development process, inputs from stakeholders, significant restrictions, and features influenced by the ChatGPT API. The feedback received from stakeholders indicates a resounding approval for the urban planning chatbot, as a significant majority of respondents reported satisfaction and acknowledged its significance. Nevertheless, several constraints have been observed, including the inability to remember past discussions, do specific tasks, interpret visuals, and comprehend geographic information. The study focuses further into the justification for creating Chatbot 2.0, emphasizing the requirement for a superior, adaptable, and economical solution. Key improvements in Chatbot 2.0 encompass the incorporation of the Hugging Chat API, the ability to conduct real-time web searches, and the utilization of reliable data from Urban Development Authorities (UDA) for training purposes. Moreover, the chatbot's functionality and user experience are enhanced with features such as 'Pro Planning Mode' and a 'user-centric graphical user interface'. This study provides a comprehensive overview of the creation and improvement of a specific chatbot for urban planning. It describes the evolution of the chatbot from its initial form to address its initial constraints
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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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Donnellan, Mark, Michael Wilkins, and Kane Linton. "An investigation of the efficacy of a ChatGPT-powered chatbot." In EuroCALL 2023: CALL for all Languages. Editorial Universitat Politécnica de Valéncia: Editorial Universitat Politécnica de Valéncia, 2023. http://dx.doi.org/10.4995/eurocall2023.2023.16976.

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One of the newest tools for language learning is AI-powered chatbots which allow students to engage in interactive conversations with a chatbot. English Central is a digital language learning platform that allows students to watch videos, study vocabulary, and produce spoken language through short interactive videos. In addition to these three core components, English Central has recently added a chatbot powered by ChatGPT. This paper shares the results of a pilot study that analysed data from Japanese university students who engaged in interactions with this chatbot. The authors introduce quantitative data showing the quantity and quality of the interactions and discuss the strategies they implemented to encourage longer interactions. A student feedback survey was conducted and the results of this are also shared. The results suggested that although many students reported benefits to using a chatbot, the strategies implemented did not lead to significantly longer interactions, and that many students felt frustrated when the chatbot did not understand their utterances.
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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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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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Thapa, Astha, and Rajvardhan Patil. "ChatGPT based ChatBot Application." In SoutheastCon 2024. IEEE, 2024. http://dx.doi.org/10.1109/southeastcon52093.2024.10500264.

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Reports on the topic "Chatbot"

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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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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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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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Lin, Shih-Jung, Yi-No Kang, and Chieh-feng Chen. Perioperative Application of Chatbot: A Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, July 2023. http://dx.doi.org/10.37766/inplasy2023.7.0080.

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Jeziorek, Marika. Martynka’s Refugee Support on Third-party Platforms Risks Data Security. Balsillie School of International Affairs, January 2025. https://doi.org/10.51644/bcs006.

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Martynka is a digital platform created in March 2022 by Nastya Podorozhnya, a Ukrainian living in Poland at the time, to provide legal aid, geolocation tracking and psychosocial support to Ukrainian refugee women. Built as a chatbot on Telegram, the platform offers services in Ukrainian, Russian, Polish and English and is accessible to women both in Ukraine and in Poland. However, Martynka faces several governance challenges, including significant concerns about data privacy and security due to its reliance on Telegram, a third-party application with a controversial reputation for data handling. Additionally, digital inequality presents a challenge, in particular within conflict zones, where access to technology and digital literacy are limited. As Martynka continues to serve women refugees, it faces increasing pressure to balance real-time security needs, such as geolocation tracking, with data privacy concerns. These governance challenges, along with operational hurdles such as financial sustainability, highlight the platform’s dependence on partnerships with state and non-state actors to address gaps in protection and support.
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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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8

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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9

Sereni, Alessandro. The crucial role of international student support experts: Creative initiatives empowering international students at the University of Adelaide. Journal of the Australian and New Zealand Student Services Association, October 2024. http://dx.doi.org/10.30688/janzssa.2024-2-09.

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Abstract:
This paper advocates for the role of “international student support experts” in Australian higher education by showcasing four creative, cost-effective initiatives developed by the University of Adelaide’s International Student Support team. These initiatives address the nuanced needs of international students, countering the trend towards centralised support models that may overlook their specific circumstances. The first initiative is a dynamic online space on the University’s Canvas learning management system (LMS), “MyUni”, offering essential resources, community-building activities, and an effective communication channel. The second, “Fantastic Race”, is a game app that promotes cultural exchanges, teamwork, and a sense of belonging among commencing international students. The third initiative is a self-guided campus tour chatbot that allows students to explore facilities and services at their own pace and complements traditional campus exploration in an enjoyable, informative manner. The fourth, “Adelaide Ready”, is a comprehensive pre-departure program that includes student reels; staff presentations; and interactive webinars on essential topics, such as accommodation, healthcare, and campus life. These initiatives epitomise the University of Adelaide’s commitment to providing an outstanding international student experience and to fostering a sense of community and belonging. Their successful implementation underscores the crucial role of international student support experts as advocates, mentors, and guides. These professionals bridge cultural gaps, anticipate diverse needs, and tailor support services to ensure the holistic well-being of international students. In the evolving landscape of global education, they are essential for attracting, nurturing, and retaining international talent in Australia.
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Melnyk, Yuriy, and Iryna Pypenko. Artificial intelligence as a factor revolutionizing higher education. KRPOCH, 2024. https://doi.org/10.26697/krpoch.melnyk.pypenko.report.pppmsf.2024.

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The role of artificial intelligence-based chatbots in higher education practice was considered. The use of chatbots among higher education stakeholders (students and faculty) was studied. A model of stakeholder behaviour was developed. This model describes two ways of solving problems: with and without the use of artificial intelligence. Trends in the use of chatbots in higher education were identified: students were 26.9% more likely than faculty to use artificial intelligence-based chatbots to prepare for classes or complete assignments at their college/university; almost all students (68.0% of 68.3% who use chatbots) edited the results returned by generative chatbots at their request; students were 30.1% more likely than faculty to edit these results.
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