Chatbot technology, a rapidly growing field, uses Natural Language Processing (NLP) methodologies to create conversational AI bots. Contextual understanding is essential for chatbots to provide meaningful interactions. Still, to date chatbots often struggle to accurately interpret user input due to the complexity of natural language and diverse fields, hence the need for a Systematic Literature Review (SLR) to investigate the motivation behind the creation of chatbots, their development procedures and methods, notable achievements, challenges and emerging trends. Through the application of the PRISMA method, this paper contributes to revealing the rapid and dynamic progress in chatbot technology with NLP learning models, enabling sophisticated and human-like interactions on the trends observed in chatbots over the past decade. The results, from various fields such as healthcare, organization and business, virtual personalities, to education, do not rule out the possibility of being developed in other fields such as chatbots for cultural preservation while suggesting the need for supervision in the aspects of language comprehension bias and ethics of chatbot users. In the end, the insights gained from SLR have the potential to contribute significantly to the advancement of chatbots on NLP as a comprehensive field.

The research aims to investigate the intentions and behaviour of high school students regarding their use of ChatGPT. Self-directed learning can be challenging due to a lack of formal structure and external motivation. ChatGPT is a tool that can help address these challenges by providing personalized assistance, immediate feedback, and customized educational resources to enhance self-directed learning. The Unified Theory of Acceptance and Use of Technology (UTAUT) framework was adapted to align with the study’s case study requirements, with 385 respondents from four public high schools. The data analysis employed Structural Equation Modelling (SEM) through a Path Analysis approach. The conceptual model was developed, and hypotheses were tested. The validity and reliability of the measurement model were assessed through outer model analysis. The main contribution of this paper is investigating the factors that influence high school students’ intentions and behaviour in using ChatGPT by adapting the UTAUT framework to the context of ChatGPT usage in high schools. The study reveals that social influence, hedonic motivation, and habit significantly influence high school students’ intention to use ChatGPT. Peer recommendations, enjoyment, and satisfaction influence these factors. The primary drivers of actual usage are facilitating conditions, habit, and behavioural intention. Performance expectancy and Effort expectancy do not significantly affect students’ intention to use ChatGPT. The study emphasizes fostering a supportive social environment and positive habitual interactions to enhance ChatGPT adoption and usage. The research results among students are designed to be studied in other developing countries so that researchers can examine students’ self-learning motivation through AI.

In this advanced world of technology, games have become a fun entertainment that is loved by the people. This study aimed examine online otome game, which in this case study is Tears of Themis, using uses and gratification theory-based model that consists of enjoyment, fantasy, escapism, social interaction, social presence, achievement, self-presentation and also age moderation variable which affects the continuance intention to play Tears of Themis. The analysis testing and model development were performed using PLS-SEM method on 135 respondents who were Tears of Themis players from Indonesia. The results revealed that enjoyment and achievement have significantly affected the continuance intention of playing Tears of Themis. Meanwhile, the moderation test results showed no relation between all independent variables and continuance intention. But based on the age sub-groups, 16 – 19 years old subgroup are affected by enjoyment and self – presentation on Tears of Themis continuance intention and 20 – 24 years old subgroup are influenced by enjoyment on Tears of Themis continuance intention. These results can be used as insight for development of online otome games to improve the quality of games in the future.

This study delves into the adoption of chatbot technology in higher education, with a focus on Indonesian online learning environments. Recognizing the potential of AI-driven tools to address academic support gaps, particularly in developing regions, the research explores how performance expectancy, effort expectancy, and facilitating conditions influence students' behavioral intentions and subsequent adoption of chatbots for academic use. The study employs Structural Equation Modeling (SEM) to analyze survey data from a diverse sample of university students, enabling a nuanced understanding of the complex relationships among these factors. The findings reveal that performance expectancy—the belief that chatbots will enhance academic performance and facilitating conditions, such as internet access and institutional support, play significant roles in motivating students to adopt chatbots. However, effort expectancy, or the perceived ease of use, does not directly drive adoption intentions. This suggests that students prioritize practical benefits over user-friendliness, an insight valuable for universities aiming to implement effective chatbot systems. Moreover, the results align with the Socio-Technical Interaction Network (STIN) model, which emphasizes the need for a cohesive social and technical framework to foster technological acceptance. The STIN model’s perspective underscores that students' engagement with chatbots is not just a matter of usability but also of how well the technology is supported by the broader educational infrastructure. This study offers actionable insights for Indonesian universities and other institutions in similar contexts, proposing that enhancing campus resources, like reliable internet access and technical support, can drive chatbot adoption. By focusing on performance-based benefits and strengthening the socio-technical environment, universities can effectively integrate AI-based learning tools, addressing both technical and socio-cultural barriers. Such initiatives support students’ learning experiences and foster an adaptive academic ecosystem where AI tools serve as essential assets in overcoming resource limitations. Thus, the study contributes a practical roadmap for advancing e-learning in resource-constrained settings through strategic support of AI technology adoption.