Showing papers on "Chatbot published in 2022"

Interacting with educational chatbots: A systematic review

Abstract: Abstract Chatbots hold the promise of revolutionizing education by engaging learners, personalizing learning activities, supporting educators, and developing deep insight into learners’ behavior. However, there is a lack of studies that analyze the recent evidence-based chatbot-learner interaction design techniques applied in education. This study presents a systematic review of 36 papers to understand, compare, and reflect on recent attempts to utilize chatbots in education using seven dimensions: educational field, platform, design principles, the role of chatbots, interaction styles, evidence, and limitations. The results show that the chatbots were mainly designed on a web platform to teach computer science, language, general education, and a few other fields such as engineering and mathematics. Further, more than half of the chatbots were used as teaching agents, while more than a third were peer agents. Most of the chatbots used a predetermined conversational path, and more than a quarter utilized a personalized learning approach that catered to students’ learning needs, while other chatbots used experiential and collaborative learning besides other design principles. Moreover, more than a third of the chatbots were evaluated with experiments, and the results primarily point to improved learning and subjective satisfaction. Challenges and limitations include inadequate or insufficient dataset training and a lack of reliance on usability heuristics. Future studies should explore the effect of chatbot personality and localization on subjective satisfaction and learning effectiveness.

A Literature Survey of Recent Advances in Chatbots

Abstract: Chatbots are intelligent conversational computer systems designed to mimic human conversation to enable automated online guidance and support. The increased benefits of chatbots led to their wide adoption by many industries in order to provide virtual assistance to customers. Chatbots utilise methods and algorithms from two Artificial Intelligence domains: Natural Language Processing and Machine Learning. However, there are many challenges and limitations in their application. In this survey we review recent advances on chatbots, where Artificial Intelligence and Natural Language processing are used. We highlight the main challenges and limitations of current work and make recommendations for future research investigation.

The Impact of Chatbots on Customer Loyalty: A Systematic Literature Review

Abstract: More and more companies have implemented chatbots on their websites to provide support to their visitors on a 24/7 basis. The new customer wants to spend less and less time and therefore expects to reach a company anytime and anywhere, regardless of time, location, and channel. This study provides insight into the influence of chatbots on customer loyalty. System quality, service quality, and information quality are crucial dimensions that a chatbot must meet to give a good customer experience. To make a chatbot more personal, companies can alter the language style. Human-like chatbots lead to greater satisfaction and trust among customers, leading to greater adoption of the chatbot. The results of this study showed that a connection between chatbots and customer loyalty is very likely. Besides, some customers suffer from the privacy paradox because of personalization. Implications of this study are discussed.

Health-focused conversational agents in person-centered care: a review of apps

Abstract: Health-focused apps with chatbots ("healthbots") have a critical role in addressing gaps in quality healthcare. There is limited evidence on how such healthbots are developed and applied in practice. Our review of healthbots aims to classify types of healthbots, contexts of use, and their natural language processing capabilities. Eligible apps were those that were health-related, had an embedded text-based conversational agent, available in English, and were available for free download through the Google Play or Apple iOS store. Apps were identified using 42Matters software, a mobile app search engine. Apps were assessed using an evaluation framework addressing chatbot characteristics and natural language processing features. The review suggests uptake across 33 low- and high-income countries. Most healthbots are patient-facing, available on a mobile interface and provide a range of functions including health education and counselling support, assessment of symptoms, and assistance with tasks such as scheduling. Most of the 78 apps reviewed focus on primary care and mental health, only 6 (7.59%) had a theoretical underpinning, and 10 (12.35%) complied with health information privacy regulations. Our assessment indicated that only a few apps use machine learning and natural language processing approaches, despite such marketing claims. Most apps allowed for a finite-state input, where the dialogue is led by the system and follows a predetermined algorithm. Healthbots are potentially transformative in centering care around the user; however, they are in a nascent state of development and require further research on development, automation and adoption for a population-level health impact.

AI and Procurement

Abstract: Problem definition: In this research, we study how buyers’ use of artificial intelligence (AI) affects suppliers’ price quoting strategies. Specifically, we study the impact of automation—that is, the buyer uses a chatbot to automatically inquire about prices instead of asking in person—and the impact of smartness—that is, the buyer signals the use of a smart AI algorithm in selecting the supplier. Academic/practical relevance: In a world advancing toward AI, we explore how AI creates and delivers value in procurement. AI has two unique abilities: automation and smartness, which are associated with physical machines or software that enable us to operate more efficiently and effectively. Methodology: We collaborate with a trading company to run a field experiment on an online platform in which we compare suppliers’ wholesale price quotes across female, male, and chatbot buyer types under AI and no recommendation conditions. Results: We find that, when not equipped with a smart control, there is price discrimination against chatbot buyers who receive a higher wholesale price quote than human buyers. In fact, without smartness, automation alone receives the highest quoted wholesale price. However, signaling the use of a smart recommendation system can effectively reduce suppliers’ price quote for chatbot buyers. We also show that AI delivers the most value when buyers adopt automation and smartness simultaneously in procurement. Managerial implications: Our results imply that automation is not very valuable when implemented without smartness, which in turn suggests that building smartness is necessary before considering high levels of autonomy. Our study unlocks the optimal steps that buyers could adopt to develop AI in procurement processes.

StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement

Abstract: Despite its benefits for children’s skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with their child due to limited availability or challenges in coming up with appropriate questions. While recent advances made AI generation of questions from stories possible, the fully-automated approach excludes parent involvement, disregards educational goals, and underoptimizes for child engagement. Informed by need-finding interviews and participatory design (PD) results, we developed StoryBuddy, an AI-enabled system for parents to create interactive storytelling experiences. StoryBuddy’s design highlighted the need for accommodating dynamic user needs between the desire for parent involvement and parent-child bonding and the goal of minimizing parent intervention when busy. The PD revealed varied assessment and educational goals of parents, which StoryBuddy addressed by supporting configuring question types and tracking child progress. A user study validated StoryBuddy’s usability and suggested design insights for future parent-AI collaboration systems.

“I am chatbot, your virtual mental health adviser.” What drives citizens’ satisfaction and continuance intention toward mental health chatbots during the COVID-19 pandemic? An empirical study in China

Abstract: Introduction In order to address the psychological problems during the COVID-19 pandemic, mental health chatbots have been extensively used by public sectors. According to Theory of Consumption Values, this paper proposed an analytical framework to investigate the determinants behind users’ satisfaction and continuance intention toward mental health chatbots. Methods The empirical study was conducted through an online survey, facilitated by the use of questionnaire posted on the WeChat platform. Seven-point Likert scale and closed-ended questions were employed. Totally 371 valid samples were collected. The research data was tested via the partial least squares structural equation modeling. Gender, age, and income were included as control variables. Results Analysis of samples collected from 371 Chinese users suggested that personalization (functional value), enjoyment (emotional value), learning (epistemic value), and the condition of the COVID-19 pandemic (conditional value) have positive influences on user satisfaction and continuance intention, but such effects were weak. The findings also revealed that user satisfaction has weakly positive impact on continuance intention. However, voice interaction (functional value) was an insignificant predictor of user satisfaction and continuance intention. Discussion This study developed a critical perspective on the role of Theory of Consumption Values in the context of mental health chatbot usage, while Theory of Consumption Value has been increasingly employed to explain the use of AI-based public services. Thus, this research devotes to the enhancement of theoretical frameworks regarding the usage of mental health chatbots.

Human vs. AI: Understanding the impact of anthropomorphism on consumer response to chatbots from the perspective of trust and relationship norms

Abstract: Text-based chatbots are being touted as a disruptive innovation with unprecedented business potential. However, frequent failures in human–chatbot conversations have led to consumer pushback. This study investigates the response of consumers to chatbots in terms of their intention to switch to human agents. Drawing upon the stimulus–organism–response (SOR) framework, focus is placed on how the anthropomorphic attributes of chatbots influence consumers’ perceived trust in chatbots and its implications for switching intention. Further, the moderating role of relationship norms in the relationships between the anthropomorphic attributes and trust in chatbots is examined. A mixed-methods approach is used; the qualitative analysis reveals three main anthropomorphic attributes of chatbots, two types of relationship norms and the specific response to chatbots. The quantitative results suggest that the anthropomorphic attributes of perceived warmth and perceived competence positively affect consumers’ perceived trust in chatbots, whereas communication delay negatively affects it. Relationship norms are found to moderate some of these effects such that exchange relationships strengthen the importance of perceived competence on trust, although communal relationships do not moderate the effects of perceived warmth on trust. Trust in chatbots negatively affects consumers’ intention to switch to a human agent. Theoretical and managerial implications are also discussed for scholars and practitioners in ways to improve the design and maximize the utility of chatbots.

The Use of Artificial Intelligence–Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical Recommendations

Abstract: Background Overweight and obesity have now reached a state of a pandemic despite the clinical and commercial programs available. Artificial intelligence (AI) chatbots have a strong potential in optimizing such programs for weight loss. Objective This study aimed to review AI chatbot use cases for weight loss and to identify the essential components for prolonging user engagement. Methods A scoping review was conducted using the 5-stage framework by Arksey and O’Malley. Articles were searched across nine electronic databases (ACM Digital Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science) until July 9, 2021. Gray literature, reference lists, and Google Scholar were also searched. Results A total of 23 studies with 2231 participants were included and evaluated in this review. Most studies (8/23, 35%) focused on using AI chatbots to promote both a healthy diet and exercise, 13% (3/23) of the studies used AI chatbots solely for lifestyle data collection and obesity risk assessment whereas only 4% (1/23) of the studies focused on promoting a combination of a healthy diet, exercise, and stress management. In total, 48% (11/23) of the studies used only text-based AI chatbots, 52% (12/23) operationalized AI chatbots through smartphones, and 39% (9/23) integrated data collected through fitness wearables or Internet of Things appliances. The core functions of AI chatbots were to provide personalized recommendations (20/23, 87%), motivational messages (18/23, 78%), gamification (6/23, 26%), and emotional support (6/23, 26%). Study participants who experienced speech- and augmented reality–based chatbot interactions in addition to text-based chatbot interactions reported higher user engagement because of the convenience of hands-free interactions. Enabling conversations through multiple platforms (eg, SMS text messaging, Slack, Telegram, Signal, WhatsApp, or Facebook Messenger) and devices (eg, laptops, Google Home, and Amazon Alexa) was reported to increase user engagement. The human semblance of chatbots through verbal and nonverbal cues improved user engagement through interactivity and empathy. Other techniques used in text-based chatbots included personally and culturally appropriate colloquial tones and content; emojis that emulate human emotional expressions; positively framed words; citations of credible information sources; personification; validation; and the provision of real-time, fast, and reliable recommendations. Prevailing issues included privacy; accountability; user burden; and interoperability with other databases, third-party applications, social media platforms, devices, and appliances. Conclusions AI chatbots should be designed to be human-like, personalized, contextualized, immersive, and enjoyable to enhance user experience, engagement, behavior change, and weight loss. These require the integration of health metrics (eg, based on self-reports and wearable trackers), personality and preferences (eg, based on goal achievements), circumstantial behaviors (eg, trigger-based overconsumption), and emotional states (eg, chatbot conversations and wearable stress detectors) to deliver personalized and effective recommendations for weight loss.

Educational AI Chatbots for Content and Language Integrated Learning

Abstract: Using advanced artificial intelligence (AI) technology in learning environments is one of the latest challenges for educators and education policymakers. Conversational AI brings new possibilities for alternative and innovative Information and Communication Technologies (ICT) tools, such as ΑΙ chatbots. This paper reports on field experiments with an AI chatbot and provides insights into its contribution to Content and Language Integrated Learning (CLIL). More specifically, this paper presents an experimental use case of an educational AI chatbot called AsasaraBot, designed to teach high school students cultural content in a foreign language, i.e., English or French. The content is related to the Minoan Civilization, emphasizing the characteristic figurine of the Minoan Snake Goddess. The related chatbot-based educational program has been evaluated at public and private language schools in Greece. The findings from these experiments show that the use of AI chatbot technology for interactive ICT-based learning is suitable for learning foreign languages and cultural content at the same time. The AsasaraBot AI chatbot has been designed and implemented in the context of a postgraduate project using open-source and free software.

Human-Computer Interaction in Customer Service: The Experience with AI Chatbots—A Systematic Literature Review

Abstract: Artificial intelligence (AI) conversational agents (CA) or chatbots represent one of the technologies that can provide automated customer service for companies, a trend encountered in recent years. Chatbot use is beneficial for companies when associated with positive customer experience. The purpose of this paper is to analyze the overall customer experience with customer service chatbots in order to identify the main influencing factors for customer experience with customer service chatbots and to identify the resulting dimensions of customer experience (such as perceptions/attitudes and feelings and also responses and behaviors). The analysis uses the systematic literature review (SLR) method and includes a sample of 40 publications that present empirical studies. The results illustrate that the main influencing factors of customer experience with chatbots are grouped in three categories: chatbot-related, customer-related, and context-related factors, where the chatbot-related factors are further categorized in: functional features of chatbots, system features of chatbots and anthropomorphic features of chatbots. The multitude of factors of customer experience result in either positive or negative perceptions/attitudes and feelings of customers. At the same time, customers respond by manifesting their intentions and/or their behaviors towards either the technology itself (chatbot usage continuation and acceptance of chatbot recommendations) or towards the company (buying and recommending products). According to empirical studies, the most influential factors when using chatbots for customer service are response relevance and problem resolution, which usually result in positive customer satisfaction, increased probability for chatbots usage continuation, product purchases, and product recommendations.

Classifying and measuring the service quality of AI chatbot in frontline service

Abstract: AI chatbots have been widely applied in the frontline to serve customers. Yet, the existing dimensions and scales of service quality can hardly fit the new AI environment. To address this gap, we define the dimensions of AI chatbot service quality (AICSQ) and develop the associated scales with a mixed-method approach. In the qualitative analysis, with the coding of the interviews from 55 global organizations in 17 countries and 47 customers, we develop new multi-level dimensions of AICSQ, including seven second-order and 18 first-order constructs. Then we follow a 10-step scale development method to establish the valid scales. The nomological test result shows that AICSQ positively influences customers’ satisfaction with, perceived value of, and intention of continuous use of AI chatbots. The innovative dimensions and scales of AI chatbot service quality provide conceptual classification and measurement instruments for the future study of chatbot service in the frontline.

FAQ chatbot and inclusive learning in massive open online courses

Abstract: Recognizing the research gap involving the lack of equity considerations in new technology implementation, this study compares students' learning experiences when using an FAQ chatbot with using an FAQ webpage. We trained a natural language processing–based chatbot utilizing content from an FAQ webpage and deployed it in two journalism massive open online courses (MOOCs) with 46 students and compared their experiences with 74 students' experiences with the FAQ webpage as a baseline. There were equal numbers of male and female students, their ages ranged from 18 to 65+, and they hailed from 45 unique countries. Considering the importance of supporting students with an inclusive Q&A experience before implementing any new technology into real-world operation, this study investigates students' disparate Q&A experiences by measuring their intention to use the interface as well as perceived Q&A service quality, enjoyment, and barriers utilizing a between-subjects online experiment. The results indicate that the students preferred an FAQ webpage over an FAQ chatbot, and the chatbot users experienced a higher magnitude of barriers compared to the webpage users. For the chatbot users, we found that region and native language factors influenced their Q&A experiences significantly. We discussed the meaning of the students’ disparate experiences from multiple perspectives—namely, human-computer interaction, MOOC context, and technologies as social practice aspects. Lastly, we determined how feasible it is to provide an inclusive learning experience for the MOOC population with the FAQ chatbot, based on the contextualized meaning of MOOC inclusiveness in current literature. This study suggests multi-faceted aspects to consider when adopting new technologies in MOOCs to provide an inclusive learning experience, and underscores the need for more active research in chatbot use to serve diverse student needs in MOOCs.

A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring

Abstract: Mental health issues are at the forefront of healthcare challenges facing contemporary human society. These issues are most prevalent among working-age people, impacting negatively on the individual, his/her family, workplace, community, and the economy. Conventional mental healthcare services, although highly effective, cannot be scaled up to address the increasing demand from affected individuals, as evidenced in the first two years of the COVID-19 pandemic. Conversational agents, or chatbots, are a recent technological innovation that has been successfully adapted for mental healthcare as a scalable platform of cross-platform smartphone applications that provides first-level support for such individuals. Despite this disposition, mental health chatbots in the extant literature and practice are limited in terms of the therapy provided and the level of personalisation. For instance, most chatbots extend Cognitive Behavioural Therapy (CBT) into predefined conversational pathways that are generic and ineffective in recurrent use. In this paper, we postulate that Behavioural Activation (BA) therapy and Artificial Intelligence (AI) are more effectively materialised in a chatbot setting to provide recurrent emotional support, personalised assistance, and remote mental health monitoring. We present the design and development of our BA-based AI chatbot, followed by its participatory evaluation in a pilot study setting that confirmed its effectiveness in providing support for individuals with mental health issues.

Exploring AI chatbot affordances in the EFL classroom: young learners’ experiences and perspectives

Abstract: Professionals within the field of language learning have predicted that chatbots would provide new opportunities for the teaching and learning of language. Despite the assumed benefits of utilizing chatbots in language classrooms, such as providing interactional chances or helping to create an anxiety-free atmosphere, little is known about learners’ actual use of chatbots during language classes or how chatbots affect their motivation to learn a language. To address these gaps, this exploratory study aimed to create an inventory of affordances that chatbots provide in the primary English as a foreign language (EFL) classroom and to explore how the affordances affect psychological aspects in language learners, particularly regarding their motivation to learn English through chatbots. Thirty-six Korean primary school learners participated in a 16-week EFL course that utilized customized chatbots. These chatbots were created using Google’s Dialogflow. After the course, individual in-depth interviews were conducted regarding the participants’ experiences and perceptions of the chatbots. Student-chatbot interaction logs produced during the course were also collected to supplement the interview data. Qualitative analysis of the interview transcripts and interaction logs revealed the presence of pedagogical, technological, and social affordances. Depending on the learner, the chatbot affordances were perceived differently; thus, each affordance acted as either an opportunity or a constraint for English language learning. In addition, this study specifically discussed how these chatbot affordances might have affected psychological states in language learners. Future recommendations regarding the use of chatbots in language classrooms were suggested from both pedagogical and technological perspectives.

Study of Kumaon Language for Natural Language Processing in End-to-End Conversation Scenario

Abstract: Natural language processing is one of the essential activities of artificial intelligence. There is an excellent need for chatbots that require integration into artificial intelligence applications. The local language makes the process easier. Our research aims to analyze the possibilities and challenges of implementing a chatbot in the Kumaon language. We also provide a detailed survey of the Kumaon language and map it to other languages to make it easier to process it for industrial use. This chatbot can help with various needs and services in the Kumaon language. The method used in this research is a study analysis of the Kumaon language to deal with language extinction. The novelty in this research is a chatbot in the Kumaon language with end-to-end encryption so that the service user has good security.

A survey on providing customer and public administration based services using AI: chatbot

Abstract: A chatbot is emerged as an effective tool to address the user queries in automated, most appropriate and accurate way. Depending upon the complexity of the subject domain, researchers are employing variety of soft-computing techniques to make the chatbot user-friendly. It is observed that chatbots have flooded the globe with wide range of services including ordering foods, suggesting products, advising for insurance policies, providing customer support, giving financial assistance, schedule meetings etc. However, public administration based services wherein chatbot intervention influence the most, is not explored yet. This paper discuses about artificial intelligence based chatbots including their applications, challenges, architecture and models. It also talks about evolution of chatbots starting from Turing Test and Rule-based chatbots to advanced Artificial Intelligence based Chatbots (AI-Chatbots). AI-Chatbots are providing much kind of services, which this paper outlines into two main aspects including customer based services and public administration based services. The purpose of this survey is to understand and explore the possibility of customer & public administration services based chatbot. The survey demonstrates that there exist an immense potential in the AI assisted chatbot system for providing customer services and providing better governance in public administration services.

The impact of a virtual teaching assistant (chatbot) on students' learning in Ghanaian higher education

Abstract: Abstract Chatbot usage is evolving rapidly in various fields, including higher education. The present study’s purpose is to discuss the effect of a virtual teaching assistant (chatbot) that automatically responds to a student’s question. A pretest–posttest design was implemented, with the 68 participating undergraduate students being randomly allocated to scenarios representing a 2 × 2 design (experimental and control cohorts). Data was garnered utilizing an academic achievement test and focus groups, which allowed more in depth analysis of the students’ experience with the chatbot. The results of the study demonstrated that the students who interacted with the chatbot performed better academically comparing to those who interacted with the course instructor. Besides, the focus group data garnered from the experimental cohort illustrated that they were confident about the chatbot’s integration into the course. The present study essentially focused on the learning of the experimental cohort and their view regarding interaction with the chatbot. This study contributes the emerging artificial intelligence (AI) chatbot literature to improve student academic performance. To our knowledge, this is the first study in Ghana to integrate a chatbot to engage undergraduate students. This study provides critical information on the use and development of virtual teaching assistants using a zero-coding technique, which is the most suitable approach for organizations with limited financial and human resources.