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The development of the digital economy has driven increased customer interaction through online chat services, making customer satisfaction a key factor in business success. Response speed and chat service quality are two important aspects in shaping the customer experience, but previous research has tended to examine them separately. This study aims to analyze the influence of online chat services and response speed on customer satisfaction partially and simultaneously. The method used is a qualitative approach with a literature review of 12 scientific articles from 2020–2025 obtained from academic databases such as Google Scholar and SINTA. The analysis technique used is descriptive-critical through the identification, comparison, and synthesis of previous research findings. The results show that online chat services have a positive effect on customer satisfaction, primarily through interaction quality such as information accuracy, ease of use, and problem-solving ability. Response speed has also proven to be an important determinant, where a fast response significantly increases customer satisfaction. However, speed without quality has the potential to decrease satisfaction. The discussion shows that the two variables have a complementary and inseparable relationship. Online chat services function as a medium for interaction, while response speed is a quality attribute that determines the effectiveness of the service. Therefore, the integration of both in one model is the main contribution of this research in filling the literature gap, especially in the context of e-commerce in Indonesia.

The advancement of Artificial Intelligence (AI) has brought significant changes to various aspects of life, including the field of education, particularly Islamic Education. This study aims to explore various AI-based learning strategies that can be implemented to create a more effective, interactive, and contemporary learning process. The method employed in this study is a literature review by examining various scholarly sources, such as journals, books, and academic articles discussing the implementation of AI in education. The findings indicate that the integration of AI can be realized through personalized learning tailored to students’ needs, the use of chatbots as learning assistants, the analysis of students’ progress and learning outcomes, as well as the utilization of technology-based learning media. These strategies are not only capable of improving students’ cognitive aspects but can also strengthen the internalization of Islamic values when applied appropriately and systematically. Furthermore, this study emphasizes that teachers’ technological competence, infrastructure readiness, and the development of an adaptive curriculum are crucial factors in the successful implementation of AI-based learning within the context of Islamic Education.

This study aims to analyze the implementation of Artificial Intelligence (AI)-based learning media and its implications for student learning motivation in Islamic Religious Education (PAI). This study uses a systematic literature review approach by examining various reputable scientific articles discussing the integration of AI in education and the dynamics of learning motivation in the context of PAI. The results of the study indicate that the use of AI through adaptive learning systems, educational chatbots, gamification, and learning analytics can increase the effectiveness, personalization, and interactivity of learning. This implementation has a positive impact on cognitive motivation through increased conceptual understanding, affective motivation through active participation and emotional engagement, and spiritual motivation through strengthening reflection and internalization of Islamic values. However, ethical challenges, the risk of depersonalization of the teacher's role, and inequality in digital access are crucial issues that require policy attention and human-centered pedagogical design. Theoretically, this study offers an integrative conceptual framework that combines technological innovation with Islamic educational epistemology. Practically, the results of this study provide recommendations for teachers, schools, and policymakers to develop AI-based PAI learning models that are adaptive, ethical, and oriented towards character building.

This research aims to develop a customized chatbot based on a local large language model (LLM) using Ollama Anything as a form of psychosocial support for Pencak Silat athletes. Mental toughness is a critical factor for Pencak Silat athletes, particularly when coping with competitive failure or sports-related injuries. Injuries sustained in Pencak Silat competitions often involve psychological consequences, including trauma, fear, anxiety, and disturbances in self-identity. To address these challenges, the proposed chatbot functions as a screen-integrated psychosocial support system for athletes. This research used an experimental method combined with Natural Language Processing (NLP) techniques was employed to construct a digital twin chatbot capable of simulating athlete-centered conversations. The Pencak Silat Athlete Chatbot is designed to assist athletes by providing responsive support when they experience defeat or performance setbacks during competitions. The research findings indicate that, although the chatbot is functional, its conversational responses remain relatively rigid, access times are prolonged, and further testing with Pencak Silat athletes in controlled settings is required. Overall, the development of the Pencak Silat Athlete Digital Twin Chatbot represents an ongoing effort to advance digital innovation and strengthen the ecosystem of sports achivements development in Indonesia.

The application of Large Language Models (LLM) in public services encourages government agencies to adopt Retrieval Augmented Generation (RAG)-based chatbots as interfaces for regulatory knowledge and official documents. Although RAG is designed to increase the supportability of answers to authoritative sources, various studies show that this system is still vulnerable to hallucinations, which have the potential to reduce public trust and pose legal risks. This article presents a Systematic Literature Review (SLR) on the use of RAG in government chatbots with a focus on the definition, mitigation strategies, and evaluation of groundedness. The literature search was conducted in the period 2021–2025 through the SpringerLink, Scopus, and Taylor & Francis databases, resulting in 7,947 articles filtered using the PRISMA framework to obtain 100 articles Q1–Q2. Based on eight research questions, this study maps publication trends, document domains, RAG architecture, retrieval strategies, definitions of groundedness, and evaluation metrics used. The SLR results indicate conceptual fragmentation in the definition and measurement of groundedness, with the dominance of text-similarity-based metrics that are inadequate for regulatory contexts. As a conceptual contribution, this article formulates the Semantic Alignment Score (SAS) as a groundedness metric based on semantic alignment, evidence coverage, and entailment relationships, positioned to support the evaluation and auditing of government document chatbots.

The rapid advancement of Information and Communication Technology (ICT) has accelerated the digital transformation of public services, including land administration. However, public complaint services at the Land Office of Southwest Sumba Regency still encounter challenges such as unstructured complaint procedures, manual data processing, risk of data loss, and limited public access to clear information. These issues highlight the need for an innovative and accessible complaint information system. This study aims to design and implement a chatbot-based public complaint service information system to enhance accessibility, transparency, and service effectiveness. A qualitative research method with a system development approach was applied. Data were obtained through interviews, observations, and documentation. The system was developed using a rule-based approach with a Finite State Machine (FSM) algorithm and implemented through the Typebot.io platform. The findings indicate that the chatbot provides structured, consistent, and user-friendly information, reduces manual workload, and improves public readiness before submitting complaints directly, while supporting future integration and system enhancement.

The development of Artificial Intelligence (AI) in the 21st century has brought significant transformation to the field of education, including Islamic Religious Education (PAI). One of the most practical implementations of this technology is the chatbot an automated conversational system capable of providing quick and contextual responses to user queries. This study aims to analyze the utilization of AI-based chatbots in addressing Islamic-related questions among students of the Islamic Education Department at IAIN Kerinci. The research employs a descriptive qualitative approach, with data collected through interviews, observations, and documentation. The findings reveal that students use chatbots as an initial source of information to understand Islamic concepts such as fiqh, tafsir, and hadith. Chatbots serve as learning aids that promote active learning and enhance students’ digital religious literacy. However, the study also identifies limitations related to the accuracy and validity of the sources used by the system, indicating that students still need verification from lecturers and authoritative Islamic literature. Overall, AI-based chatbots hold great potential to support interactive and contextual Islamic learning, provided their use is guided by academic supervision rooted in Islamic values.