Jul 2026· Journal of Electronic Commerce in Organizations· 0 citations
TL;DR
The findings show that AI-based chatbots in Metaverse platforms can be embedded in subscription-based licensing, pay-per-use, or feature-based licensing models, thereby supporting flexible monetization strategies across heterogeneous user segments.
Abstract
This paper introduces a taxonomy of artificial intelligence (AI)-based chatbots within Metaverse platforms. Drawing on a systematic literature review of 59 academic publications and an analysis of 49 real-world AI-based chatbots deployed on Metaverse platforms, this study identifies the key dimensions, features, and corresponding archetypes of these systems. The resulting taxonomy comprises 11 dimensions and 62 specific features that characterize AI-based chatbots in Metaverse environments. Beyond systematizing their technological and interaction-related characteristics, the taxonomy highlights the business model dimension as a central differentiator. In particular, the findings show that AI-based chatbots in Metaverse platforms can be embedded in subscription-based licensing, pay-per-use, or feature-based licensing models, thereby supporting flexible monetization strategies across heterogeneous user segments. Overall, the taxonomy provides a structured foundation for analyzing, designing, and commercially positioning AI-based chatbots in Metaverse platforms.
This study examines the effects of generative AI (GenAI) chatbots versus traditional web-based resources (TWRs) on group cohesion, motivation, and writing performance during face-to-face synchronous collaborative writing (SCW) tasks among L2 learners. Despite growing adoption of AI in language education, key gaps remain: limited psychological research on AI-supported collaborative writing, scarce attention to motivation and self-efficacy in group contexts, and absence of direct comparisons between AI chatbots and conventional tools in real-time, in-class settings. Sixty-three English-major sophomore students at a Chinese university were randomly assigned to two classes: an experimental cohort (n = 33) using Kimi AI chatbot and a control cohort (n = 30) using TWRs (e.g., dictionaries, search engines). Both groups worked in teams of 5-6 on a shared cloud document to revise a 500-word descriptive essay. Mixed-methods data included screen recordings, writing assessments, typing speed measures, pre/post motivation, perception questionnaires, and individual notes on team cohesion. Quantitative results showed the AI group completed writing and proofreading significantly faster (EWT: M = 24:20 vs. 41:10; EPT: M = 9:33 vs. 15:25), achieved higher essay scores (M = 90.28 vs. 81.11, p = .001, d = 2.43), and reported substantially greater post-task motivation (ANCOVA, p < .001, η2 = 0.888). Qualitative analysis of the cohesion notes suggested that AI helped streamline communication, reduce information overload, and support engagement, but it also introduced challenges, as its rapid and uniform responses could reduce discussion, limit exploration of alternatives, and encourage over-reliance on the tool. From a Group Cohesion Theory (GCT) perspective, AI appeared to strengthen task cohesion more than social cohesion, improving coordination and performance, though it did not always deepen peer interaction or shared meaning-making.
The architectural evaluation demonstrates that WA Daksha provides an accessible and scalable framework for automating realtime academic services, facilitating multi-user interaction among students, lecturers, and the public.
Rizky Basatha, B. Putra, S. A. Alamsyah et al.· E3S Web of Conferences· 0 citations
While research on artificial intelligence (AI) tools is growing, studies on chatbot integration for customer relationship management (CRM) in E-commerce remain limited. This review addressed this gap by analyzing chatbot features within a unified model. Using a literature review approach, inclusion criteria, and document analysis, 80 scholarly articles published between 2020 and 2025 were synthesized. Chatbots, originating with ELIZA in the 1960s, are categorized as AI-based, rule-based, or voice-activated. They play a substantial role in digital marketing by managing customer expectations, increasing satisfaction, and fostering long-term relationships on major platforms such as Amazon, Shopify, Alibaba, Domino’s Pizza, and eBay. Key functions, including quick responses, personalized interactions, automated replies, and 24/7 support, enhance information quality, customer satisfaction, retention, and loyalty. The review suggesting future research to further explore chatbot integration in E-commerce platforms across industries and contexts.
Chhayna Cheng· Journal of Human Centered Te...· 0 citations
The internet and the growth in internet users have undoubtedly influenced the evolution of chatbot development and adoption. Various industries are increasingly adopting Artificial Intelligence (AI) and chatbot technologies to manage customer interactions and enhance customer service. As these industries increase the deployment of AI chatbot to support their customers and improve their user-experience, its therefore significant for them to understand the knowledge-centric service delivery. This study investigates the knowledge-centric quality service and responsiveness of a rule-based WhatsApp chatbot implemented by a South African mobile telecommunications service provider in resolving customer queries. The study adopted a mono-method approach within the interpretivism paradigm, were qualitative insights gathered through thematic analysis of interviews with three employees and a further platform evaluation. The chatbot immediately replies and presents the main menu with various options for the user to select. The matrix shows how the chatbot performed in each main menu task/item relative to usability, accuracy, performance, and interactive responsiveness. Findings highlight the importance of ongoing performance monitoring and iterative improvements in AI-driven knowledge interface design to align with evolving customer expectations and industry benchmarks for service quality. This study contributes to the growing discourse on AI-driven customer knowledge-centric service solutions in South African mobile telecommunications and other industries by offering a comprehensive framework for evaluating and enhancing chatbot effectiveness. This research paper position chatbots as a knowledge-mediation interface that translates organizational information into task-oriented exchanges, shaping escalation pathways, retention of operational know how and customer experience.
Sithembiso Khumalo, Tryphosa B. Mashigo, Wafeequa Ben· European Conference on Knowl...· 0 citations
This study investigated how ChatGPT-based chatbots can support preservice mathematics teachers’ (PMTs) understanding of core teaching practices, specifically collective mathematical argumentation. Drawing on a situated learning perspective and principles of practice-based teacher education, we focused on the first two phases of the Generative Role-play AI Simulation for Pedagogy (GRASP) model, in which we designed customized AI chatbots to function as both knowledge builders and situation generators. Ten PMTs enrolled in a mathematics education course at a U.S. research university engaged with these chatbots to learn foundational ideas of collective argumentation, analyze argument structure, and examine the productivity of the provided classroom scenarios. Data sources included written assignments, reflections, and pre- and post-surveys. Analyses across multiple data sources indicated that most PMTs in this study developed a robust understanding of collective mathematical argumentation and of the teacher moves that facilitate it through their interactions with the customized AI chatbots, while also demonstrating awareness of the chatbots’ usefulness and inherent limitations. These findings highlight both the promise and the constraints of integrating AI tools into teacher preparation and point to design considerations for creating effective AI-supported learning environments.
Yuling Zhuang, Wisdom Yao Nudze, Xiangquan Yao et al.· European Journal of STEM Edu...· 0 citations
Experimental comparisons with GPT-4o vanilla across three roles, evaluated through an ablation study and a multi-evaluator panel combining LLM-based and human judges, consistently rank XBot as the best performing system, demonstrating superior empathy, role stability and conversational depth, while GPT-4o vanilla exhibits pervasive persona drift across all experimental scenarios.
Luciano Caroprese, Ester Zumpano, M. Aracne et al.· Discover Artificial Intellig...· 0 citations