GR-397 Conceptualizing a TOC-Enhanced Chatbot: Pattern Recognition and Interaction

A chatbot is a software which is capable of communicating with human by using natural language processing. In our project, we plan to develop a Python-based chatbot that integrates theory of computation (TOC) concepts, including finite automata and regular expressions. The chatbot will interact with users, recognizing patterns and keywords in their inputs. We’ll begin by defining initial regular expressions for basic user interactions including greetings and inquiries.Future developments may enhance regular expressions and broaden the chatbot’s TOC-related capabilities, creating a versatile educational tool with practical TOC applications

Towards Designing a Conversation Mining System for Customer Service Chatbots

Chatbots are increasingly used to provide customer service. However, despite technological advances, customer service chatbots frequently reach their limits in customer interactions. This is not immediately apparent to both chatbot operators (e.g., customer service managers) and chatbot developers because analyzing conversational data is difficult and labor-intensive. To address this problem, our ongoing design science research project aims to develop a conversation mining system for the automated analysis of customer-chatbot conversations. Based on the exploration of large dataset (N= 91,678 conversations) and six interviews with industry experts, we developed the backend of the system. Specifically, we identified and operationalized important criteria for evalu-ating conversations. Our next step will be the evaluation with industry experts. Ultimately, we aim to contribute to research and practice by providing design knowledge for conversation mining systems that leverage the treasure trove of data from customer-chatbot conversations to generate valuable insights for managers and developers

Claim success, but blame the bot? User reactions to service failure and recovery in interactions with humanoid service robots

Service robots are changing the nature of service delivery in the digital economy. However, frequently occurring service failures represent a great challenge to achieve service robot acceptance. To understand how different service outcomes in interactions with service robots affect usage intentions, this research investigates (1) how users attribute failures committed by humanoid service robots and (2) whether responsibility attribution varies depending on service robot design. In a 3 (success vs. failure vs. failure with recovery) ✕ 2 (warm vs. competent service robot design) between-subject online experiment, this research finds evidence for the self-serving bias in a service robot context, that is, attributing successes to oneself, but blaming others for failures. This effect emerges independently from service robot design. Furthermore, recovery through human intervention can mitigate consequences of failure only for robots with warm design. The authors discuss consequences for applications of humanoid service robots and implications for further research

Designing a Conversation Mining System for Customer Service Chatbots

As chatbots are gaining popularity in customer service, it is critically important for companies to continuously analyze and improve their chatbots’ performance. However, current analysis approaches are often limited to the question-answer level or produce highly aggregated metrics (e.g., conversations per day) instead of leveraging the full potential of the large volume of conversation data to provide actionable insights for chatbot developers and chatbot managers. To address this challenge, we developed a novel chatbot analytics approach — conversation mining — based on concepts and methods from process mining. We instantiated our approach in a conversation mining system that can be used to visually analyze customer-chatbot conversations at the process level. The results of four focus group evaluations suggest that conversation mining can help chatbot developers and chatbot managers to extract useful insights for improving customer service chatbots. Our research contributes to research and practice with novel design knowledge for conversation mining systems

Designing a Conversation Mining System for Customer Service Chatbots

As chatbots are gaining popularity in customer service, it becomes increasingly important for companies to continuously analyze and improve their chatbots’ performance. However, current analysis ap-proaches are often limited to the level of question-answer pairs or produce highly aggregated metrics (e.g., average intent scores, conversations per day) rather than leveraging the full potential of the large volume of conversation data to extract actionable insights for chatbot developers and chatbot operators (e.g., customer service managers). To address this challenge, we developed a novel chatbot analytics approach — conversation mining — based on concepts and methods from process mining. We instanti-ated our approach in a conversation mining system that can be used to visually analyze customer-chatbot conversations at the process level. The findings of four focus group evaluations show that our system can help chatbot developers and operators identify starting points for chatbot improvement. Our re-search contributes novel design knowledge for conversation mining systems

Potentials of Chatbot Technologies for Higher Education: A Systematic Review

Chatbots are used in different areas such as customer service, healthcare and education. The potential for improving outcomes and processes in education is high but differs for different types of chatbots. As universities want to provide excellent teaching, it is important to find the chatbot technologies with the greatest possible benefit. This paper presents a systematic review of chatbot technologies in five application areas. For each application area, the ten most cited publications are analysed and a possible categorisation scheme for chatbot technologies is derived. Furthermore, it is investigated which chatbot technology types are used and their suitability for higher education is analysed. The results show that chatbots can be categorised using five categories derived from the 50 publications. A total of 14 different types of chatbot technologies are found in the five areas. Nine of them are suitable for use in higher education

Human-technology integration with industrial conversational agents: A conceptual architecture and a taxonomy for manufacturing

Conversational agents are systems with great potential to enhance human-computer interaction in industrial settings. Although the number of applications of conversational agents in many fields is growing, there is no shared view of the elements to design and implement for chatbots in the industrial field. The paper presents the combination of many research contributions into an integrated conceptual architecture, for developing industrial conversational agents using Nickerson's methodology. The conceptual architecture consists of five core modules; every module consists of specific elements and approaches. Furthermore, the paper defines a taxonomy from the study of empirical applications of manufacturing conversational agents. Indeed, some applications of chatbots in manufacturing are available but those have never been collected in single research. The paper fills this gap by analyzing the empirical cases and presenting a qualitative analysis, with verification of the proposed taxonomy. The contribution of the article is mainly to illustrate the elements needed for the development of a conversational agent in manufacturing: researchers and practitioners can use the proposed conceptual architecture and taxonomy to more easily investigate, define, and develop all the elements for chatbot implementation

Chatbot del proceso de aprendizaje universitario: Una revisión sistemática

Toda organización independientemente del rubro obedece de alguna forma u otra forma a las TICs (Tecnologías de Información y Comunicación) las cuales representan una gran ayuda para la rápida adaptación a los cambios y la entrega de valor. Además, en la era de la Inteligencia Artificial, el mercado de los Chatbot ha venido adquiriendo popularidad en varios sectores. No obstante, en el campo de la educación como herramienta de aprendizaje aún se encuentra iniciando y tiene mucho por mejorar. En la presente revisión sistemática evaluaremos la suficiencia y conveniencia que tiene el uso de los Chatbot aplicados a la educación, así como conocer los factores que influyen en la adopción de esta tecnología para la mejora del proceso de aprendizaje en el ámbito universitario. Entre las principales conclusiones se tiene que de acuerdo a los estudios realizados que la aplicación de los Chatbots tiene mayor incidencia en el sector salud; asimismo, se precisaron que las plataformas más utilizadas para el desarrollo de un Chatbot es el Amazon Lex; de igual modo, se precisaron que los países donde más se utiliza los Chatbots para el proceso de aprendizaje pertenecen al continente asiático y europeo

KritiaBot: herramienta para el desarrollo de chatbot de atención postoperatoria en oftalmología

Brinda una herramienta para la creación de chatbots para una afección oftalmológica, atendiendo las preguntas de los pacientes y mejorando la calidad de la atención postoperatoria. El éxito de una cirugía depende mucho de una adecuada atención postoperatoria, detectando y previniendo situaciones perjudiciales para el paciente, atendiendo oportunamente sus consultas. Por esta razón, los chatbots son cada vez más utilizados en el área médica, por ser agentes conversacionales que promueven la salud e impulsan un cambio de comportamiento. Para su validación, se incorporó un escenario de caso de uso, describiendo los inconvenientes del paciente y el posterior uso del sistema, identificando las ventajas obtenidas como resultado de esta interacción. Asimismo, se realizó un análisis comparativo entre las funciones de KritiaBot y los sistemas en el sector salud. Como resultado, se observó beneficios en la reducción de tiempos para la atención de las consultas de los pacientes y la disminución de los costos del seguimiento del estado de salud, por su disponibilidad 24/7. La comparativa realizada permitió ver que KritiaBot es un chatbot muy completo en este sector, pues cumple con las funcionalidades deseables, además de permitir crear chatbots de forma simple y rápida. Como conclusión, se puede entender que el contar con los servicios necesarios para garantizar una mejor calidad de vida es primordial. Por esta razón, un servicio óptimo de atención postoperatoria, con los medios necesarios para un seguimiento constante del estado de salud del paciente, permite hacer frente a posibles situaciones perjudiciales