Conversational Software Research Articles

Computational Argumentation-based Chatbots: A Survey

Chatbots are conversational software applications designed to interact dialectically with users for a plethora of different purposes. Surprisingly, these colloquial agents have only recently been coupled with computational models of arguments (i.e. computational argumentation), whose aim is to formalise, in a machine-readable format, the ordinary exchange of information that characterises human communications. Chatbots may employ argumentation with different degrees and in a variety of manners. The present survey sifts through the literature to review papers concerning this kind of argumentation-based bot, drawing conclusions about the benefits and drawbacks that this approach entails in comparison with standard chatbots, while also envisaging possible future development and integration with the Transformer-based architecture and state-of-the-art Large Language models.

Unlocking AI-Powered Conversations and Code Excellence: Exploring Prompt Patterns in Conversational AI and Software Engineering

Unlocking AI-Powered Conversations and Code Excellence: Exploring Prompt Patterns in Conversational AI and Software Engineering

Evaluating ChatGPT's Ability to Solve Higher-Order Questions on the Competency-Based Medical Education Curriculum in Medical Biochemistry.

Background Healthcare-related artificial intelligence (AI) is developing. The capacity of the system to carry out sophisticated cognitive processes, such as problem-solving, decision-making, reasoning, and perceiving, is referred to as higher cognitive thinking in AI. This kind of thinking requires more than just processing facts; it also entails comprehending and working with abstract ideas, evaluating and applying data relevant to the context, and producing new insights based on prior learning and experience. ChatGPT is an artificial intelligence-based conversational software that can engage with people to answer questions and uses natural languageprocessing models. The platform has created a worldwide buzz and keeps setting an ongoing trend in solving many complex problems in various dimensions. Nevertheless, ChatGPT's capacity to correctly respond to queries requiring higher-level thinking in medical biochemistry has not yet been investigated. So, this research aimed to evaluate ChatGPT's aptitude for responding to higher-order questions on medical biochemistry. Objective In this study, our objective was to determine whether ChatGPT can address higher-order problems related to medical biochemistry.​​​​​​ Methods​​​ This cross-sectional study was done online by conversing with the current version of ChatGPT (14 March 2023, which is presently free for registered users).It was presented with 200 medical biochemistry reasoning questions that require higher-order thinking. These questions were randomly picked from the institution's question bank and classified according to the Competency-Based Medical Education (CBME) curriculum's competency modules. The responses were collected and archived for subsequent research. Two expert biochemistry academicians examined the replies on a zero to five scale. The score's accuracy was determined by a one-sample Wilcoxon signed rank test using hypothetical values. Result The AI software answered 200 questions requiring higher-order thinking with a median score of 4.0(Q1=3.50, Q3=4.50). Using a single sample Wilcoxon signed rank test, the result was less than the hypothetical maximum of five (p=0.001) and comparable to four (p=0.16). There was no difference in the replies to questions from different CBME modules in medical biochemistry (Kruskal-Wallis p=0.39). The inter-rater reliability of the scores scored by two biochemistry faculty members was outstanding (ICC=0.926 (95% CI: 0.814-0.971); F=19; p=0.001)​​​​​​ Conclusion The results of this research indicate that ChatGPT has the potential to be a successful tool for answering questions requiring higher-order thinking in medical biochemistry, with a median score of four out of five. However, continuous training and development with data of recent advances are essential to improve performance and make it functional for the ever-growing field of academic medical usage.

“Ask, ‘When You Do This, How Much Pain Are You In?’”: Content Preferences for a Conversational Pain Self-Management Software Application

The purpose of the current study was to examine older adults' preferences for conversational pain management content to incorporate in an interactive application (app) for pain self-management. Conversational statements and questions were written as a script to encourage evidence-based pain self-management behaviors. The content was converted from text to female chatbot speech and saved as four groups of MP3 files. A purposive sample of 22 older adults participated in a guided interaction through the MP3 files. One-on-one interviews were conducted to garner participants' conversational content preferences. Overall, participants want the conversational content to increase health care provider engagement in pain management communication. Older adults preferred the inclusion of conversational statements and questions for monitoring the multifaceted dimensions of pain, treatment accountability, guidance for alternative treatments, and undesirable effects from pain treatments. The design of mobile health apps must incorporate the needs and preferences of older adults. [Journal of Gerontological Nursing, 49(1), 11-17.].

Designing PairBuddy—A Conversational Agent for Pair Programming

From automated customer support to virtual assistants, conversational agents have transformed everyday interactions, yet despite phenomenal progress, no agent exists for programming tasks. To understand the design space of such an agent, we prototyped PairBuddy—an interactive pair programming partner—based on research from conversational agents, software engineering, education, human-robot interactions, psychology, and artificial intelligence. We iterated PairBuddy’s design using a series of Wizard-of-Oz studies. Our pilot study of six programmers showed promising results and provided insights toward PairBuddy’s interface design. Our second study of 14 programmers was positively praised across all skill levels. PairBuddy’s active application of soft skills—adaptability, motivation, and social presence—as a navigator increased participants’ confidence and trust, while its technical skills—code contributions, just-in-time feedback, and creativity support—as a driver helped participants realize their own solutions. PairBuddy takes the first step towards an Alexa-like programming partner.

AI-Based Chatbot to Solve Modern-Day Enterprise Business Problems

Communicating with employees through forums and emails has become an increasingly popular way for many multinational companies to provide human resource services in real time. Today, employee chat service agents are often replaced by conversational software agents or chatbots. These systems are designed to communicate with human users through natural language, generally based on artificial intelligence (AI). Time and cost saving opportunities have led to the widespread deployment of AI-based chatbots. Chatbots are one of the most basic and popular examples of human-computer intelligent interaction (HCI). Designed to convincingly simulate the way humans behave as dialogue partners. In the proposed system, we propose a chat robot that can dynamically respond to employee human resource queries. The proposed HR system is based on the Microsoft Cognitive Services chatbot. This Microsoft Teams-based platform provides a broad foundation of intelligence and is trained based on various data sets provided by the organization's HR.

Performance Analysis and Study of Chatbots

Artificial intelligence, machine learning and deep learning machines are being used as conversational agents. They are used to impersonate a human and provide the user a human-like experience. Conversational software agents that use natural language processing is called a chatbot and it is widely used for interacting with users. It provides appropriate and satisfactory answers to the user. In this paper we have analyzed and compared various chatbots and provided a score to each of them on different parameters. We have asked each chatbot the same questions, and we have evaluated each answer, whether it’s satisfactory or not. This analysis is based on user experience rather than analyzing the software of each chatbot. This paper proves that even though chatbot performance has highly increased compared to the past, there is still quite a lot of room for improvement.

Study protocol of a randomised controlled trial on SISU, a software agent providing a brief self-help intervention for adults with low psychological well-being

IntroductionOnly a minority of people living with mental health problems are getting professional help. As digitalisation moves on, the possibility of providing internet/mobile-based interventions (IMIs) arises. One type of IMIs...

Conceptualising a Library Chatbot using Open Source Conversational Artificial Intelligence

Conversational software, or chatbots in popular parlance, has been in vogue amongst organizations for a few decades now. In sync with the trend, various libraries and knowledge resource centres have also adopted them within their technological fold, with an aim to provide improved services to patrons. The only bottleneck towards such implementation has been the dearth of open source conversational software platforms. The purpose of the paper is to conceptualize a library chatbot using a recently developed, artificial intelligence-powered open source conversational software platform named Rasa Stack. It introduces the essence of chatbot technology and their present day application in libraries, illustrates how the technical underpinnings of Rasa Stack can be leveraged to develop a library chatbot, and reflects on the potential future research in this direction.

Conversational artificial intelligence - demystifying statistical vs linguistic NLP solutions

This paper aims to demystify the hype and attention on chatbots and its association with conversational artificial intelligence. Both are slowly emerging as a real presence in our lives from the impressive technological developments in machine learning, deep learning and natural language understanding solutions. However, what is under the hood, and how far and to what extent can chatbots/conversational artificial intelligence solutions work – is our question. Natural language is the most easily understood knowledge representation for people, but certainly not the best for computers because of its inherent ambiguous, complex and dynamic nature. We will critique the knowledge representation of heavy statistical chatbot solutions against linguistics alternatives. In order to react intelligently to the user, natural language solutions must critically consider other factors such as context, memory, intelligent understanding, previous experience, and personalized knowledge of the user. We will delve into the spectrum of conversational interfaces and focus on a strong artificial intelligence concept. This is explored via a text based conversational software agents with a deep strategic role to hold a conversation and enable the mechanisms need to plan, and to decide what to do next, and manage the dialogue to achieve a goal. To demonstrate this, a deep linguistically aware and knowledge aware text based conversational agent (LING-CSA) presents a proof-of-concept of a non-statistical conversational AI solution.

AI-based chatbots in customer service and their effects on user compliance

Communicating with customers through live chat interfaces has become an increasingly popular means to provide real-time customer service in many e-commerce settings. Today, human chat service agents are frequently replaced by conversational software agents or chatbots, which are systems designed to communicate with human users by means of natural language often based on artificial intelligence (AI). Though cost- and time-saving opportunities triggered a widespread implementation of AI-based chatbots, they still frequently fail to meet customer expectations, potentially resulting in users being less inclined to comply with requests made by the chatbot. Drawing on social response and commitment-consistency theory, we empirically examine through a randomized online experiment how verbal anthropomorphic design cues and the foot-in-the-door technique affect user request compliance. Our results demonstrate that both anthropomorphism as well as the need to stay consistent significantly increase the likelihood that users comply with a chatbot’s request for service feedback. Moreover, the results show that social presence mediates the effect of anthropomorphic design cues on user compliance.

New Learning Scenarios with Chatbots – Conversational Learning with Jix: from Digital Tutors to Serious Interactive Fiction Games

Conversational user interfaces, aka chatbots, offer new ways of interaction that can be used not only for task-led applications, but also for learning itself. Conversational learning offers a variety of new and extensive options to support individuals through the learning process and to push the boundaries of classroom based learning. This document shows three application scenarios that have been realized with time4you’s conversational learning software Jix. After having read this text, you will have a better idea of what conversational learning is, how it can be used in learning and development, and what is critical for its success.

Chatbots Employing Deep Learning for Big Data

With the evolution of artificial intelligence to deep learning, the age of perspicacious machines has pioneered that can even mimic as a human. A Conversational software agent is one of the best-suited examples of such intuitive machines which are also commonly known as chatbot actuated with natural language processing. The paper enlisted some existing popular chatbots along with their details, technical specifications, and functionalities. Research shows that most of the customers have experienced penurious service. Also, the inception of meaningful cum instructive feedback endure a demanding and exigent assignment as enactment for chatbots builtout reckon mostly upon templates and hand-written rules. Current chatbot models lack in generating required responses and thus contradict the quality conversation. So involving deep learning amongst these models can overcome this lack and can fill up the paucity with deep neural networks. Some of the deep Neural networks utilized for this till now are Stacked Auto-Encoder, sparse auto-encoders, predictive sparse and denoising auto-encoders. But these DNN are unable to handle big data involving large amounts of heterogeneous data. While Tensor Auto Encoder which overcomes this drawback is time-consuming. This paper has proposed the Chatbot to handle the big data in a manageable time.

An Evaluation Of A Linguistically Motivated Conversational Software Agent Framework

<p>This paper presents a critical evaluation framework for a linguistically motivated conversational software agent (CSA). The CSA prototype investigates the integration, intersection and interface of the language, knowledge, and speech act constructions (SAC) based on a grammatical object, and the sub-model of belief, desires and intention (BDI) and dialogue management (DM) for natural language processing (NLP). A long-standing issue within NLP CSA systems is refining the accuracy of interpretation to provide realistic dialogue to support human-to-computer communication. This prototype constitutes three phase models: (1) a linguistic model based on a functional linguistic theory – Role and Reference Grammar (RRG), (2) an Agent Cognitive Model with two inner models: (a) a knowledge representation model, (b) a planning model underpinned by BDI concepts, intentionality and rational interaction, and (3) a dialogue model. The evaluation strategy for this Java-based prototype is multi-approach driven by grammatical testing (English language utterances), software engineering and agent practice. A set of evaluation criteria are grouped per phase model, and the testing framework aims to test the interface, intersection and integration of all phase models. The empirical evaluations demonstrate that the CSA is a proof-of-concept, demonstrating RRG’s fitness for purpose for describing, and explaining phenomena, language processing and knowledge, and computational adequacy. Contrastingly, evaluations identify the complexity of lower level computational mappings of NL – agent to ontology with semantic gaps, and further addressed by a lexical bridging solution.</p>

Applying Chatbots to the Internet of Things: Opportunities and Architectural Elements

Internet of Things (IoT) is emerging as a significant technology in shaping the future by connecting physical devices or things with the web. It also presents various opportunities for the intersection of other technological trends which can allow it to become even more intelligent and efficient. In this paper, we focus our attention on the integration of Intelligent Conversational Software Agents or Chatbots with IoT. Prior literature has covered various applications, features, underlying technologies and known challenges of IoT. On the other hand, Chatbots are a relatively new concept, being widely adopted due to significant progress in the development of platforms and frameworks. The novelty of this paper lies in the specific integration of Chatbots in the IoT scenario. We analyzed the shortcomings of existing IoT systems and put forward ways to tackle them by incorporating chatbots. A general architecture is proposed for implementing such a system, as well as platforms and frameworks – both commercial and open source – which allow for the implementation of such systems. Identification of the newer challenges and possible future research directions with this new integration have also been addressed.

Integrando Redes Sociales y Técnicas de Inteligencia Artificial en Entornos Educativos

In this research we explored a methodological proposal whose main purpose is to show and clarify the use of Information Technology and Communication in teacher education, using Conversational Software Agents and Social Network Analysis. In this context, the real-time work with the teacher is emphasized, and is feasible therefore to generate a practical and useful software tool, called Agent SocialMetric.

Interactive Information Management in Exploration: ABSTRACT

Conversational software provides a continuous dialog between the explorationist and the computer. Interactive procedures of this type create an environment in which storage, retrieval, manipulation, reporting, and mapping of information are integrated into one system. Automation of an exploration office provides increased speed, accuracy, and flexibility to the exploration effort. With present technology, noncomputer-orientated employees can easily access existing data files with computer programs, which produce a variety of information. From the creation of detailed base maps to interpretative geological mapping, interactive software has proven its usefulness. A rapid preliminary evaluation of an area will be performed using interactive information systems. A hypothetical geologic setting in Plaquemines Parish, Louisiana, serves as the setting for this reconnaissance study. All available information will be retrieved, manipulated, and displayed using conversational software. Base maps and reports created from existing well, seismic, velocity, and cartographic computer files comprise the initial information. Existing well marker depths and seismic times will aid in a more accurate interpretation of the geology. As the interpretation evolves, information is periodically updated and mapped to further develop the study. Computer-generated contour maps provide a rapid and reliable evaluation of the subsurface geology. End_of_Article - Last_Page 233------------

Experience with an On-Line, Conversational Control Software System

Experience with an On-Line, Conversational Control Software System