The traditional education system faces many problems, including overcrowded classes, lack of attention to students, different learning speeds and styles, and trying to keep up with the rapid evolution of technology and information. As the educational landscape changes over time, the emergence of AI-based chatbots appears as a promising solution to some of the emerging issues in education. Some educational institutions are increasingly turning to AI-based chatbots, while others are more cautious and in no rush to adopt modern learning environments (1).

This study aims to explore the experiences of freelancers with AI chatbots, identifying the main drivers behind their adoption, the challenges faced during their integration, and the overall impact on work and personal perceptions. Employing a qualitative research design, this study conducted semi-structured interviews with 29 freelancers from various professional backgrounds. Thematic analysis was utilized to distill the data into meaningful themes and categories, providing insights into freelancers' interactions with AI chatbots. Five main themes were identified: Adoption Drivers, Technological Challenges, Impact on Work, Ethical and Social Issues, and Personal Experiences and Reflections. Key findings include the efficiency gains and improved quality of work as primary adoption drivers, significant technological challenges related to integration and reliability, and varied impacts on job opportunities and financial aspects. Ethical concerns, notably regarding privacy, security, and bias, were prevalent. Personal reflections revealed a spectrum of perceptions on AI chatbots, from optimism about future prospects to concerns over professional identity and work-life balance. Freelancers' experiences with AI chatbots are marked by a complex interplay of benefits and challenges. While AI chatbots offer potential for efficiency and competitive advantage, their integration is hampered by technological hurdles and ethical dilemmas. Addressing these challenges, while fostering an environment that supports continuous learning and adaptation, is crucial for leveraging AI chatbots' full potential in freelancing.

The health insurance sector is undergoing a revolution with the help of digital technologies that are creating new ways of improving the quality of services and reducing costs. This study explores how modern solutions such as artificial intelligence, big data, the Internet of Things, and health informatic systems can transform health insurance's future.  These technologies have already demonstrated their potential. They are assisting the insurers in enhancing the risk appraisal, providing individualized care, and minimizing the hospital expenses—especially when integrated with a digital strategy. But the journey is not simple. Data security, global compliance, and algorithmic bias are a few of the significant challenges that have been highlighted. In Iran, the development has been made, but there are still some issues. Challenges include; limited IT infrastructure, poor referral systems, and limited internet access. To address these challenges, there is the need to enhance the current inadequate digital infrastructure, to review the existing legal framework and to train a workforce that will be conversant with these technologies.  The final success of digital transformation will depend on the perfect balance, meaning the right combination of advanced technologies and the ability to provide quality care at an affordable cost for all. Ultimately, these efforts can lead to development of a healthcare system that works for all stakeholders.

This article presents a thorough examination of eight state-of-the-art AI chatbots that are designed for general use. Through detailed descriptions and empirical research, which include the use of confusion matrices and a pairwise comparison methodology, the study systematically assesses these chatbots based on eight specific criteria. The main objective of this research is to pinpoint the most efficient AI chatbot for general purposes, thereby providing invaluable insights for both users and developers within the AI industry. By utilizing the results of this study, developers can gain access to crucial information and actionable recommendations that help in enhancing the performance of their chatbots. Furthermore, these findings act as a vital resource for budding developers who are venturing into the creation of their own chatbot projects. In a rapidly evolving technological landscape, where AI is increasingly infiltrating various aspects of daily life, such detailed evaluations play a crucial role. They guide both users and developers towards selecting and developing AI chatbots that are not just effective, but also user-friendly and adaptable to the shifting needs and preferences of their user base.

In the era of advancing artificial intelligence in education, this study investigates whether regular practice with AI-powered chatbots improves English speaking proficiency more effectively than traditional peer-to-peer conversation practice. Grounded in the interaction hypothesis (Long, 1996) and output hypothesis (Swain, 1985), which emphasize the role of interactive practice and feedback in language acquisition, the research employed an experimental design. Two groups of English learners participated over 12 weeks: an experimental group practiced speaking with Elsa AI, an AI chatbot equipped with natural language processing for real-time feedback on pronunciation, vocabulary, grammar, and fluency, while a control group engaged in peer-to-peer conversation practice. Speaking proficiency was assessed pre- and post-intervention using standardized tests (IELTS speaking), and statistical methods (e.g., t-tests or ANOVA) were used to compare proficiency gains. Statistical analyses revealed that both groups improved significantly over time, but the chatbot group achieved larger mean gains than the peer group. These findings suggest that AI chatbots offer scalable, on-demand practice with immediate corrective feedback, addressing limitations of human partners such as availability and consistency. The findings could inform the integration of chatbots in diverse English Language Teaching (ELT) settings, enhancing speaking practice opportunities globally.

Today, artificial intelligence (AI)-based research assistants are used in various stages of qualitative studies, including methodology, data collection, group interviews, writing, editing, and qualitative data analysis (1). However, it seems that chatbots can also be used as a data source in human-computer interaction (2). One of the key elements in qualitative research is reaching theoretical saturation, meaning that data collection reaches a stage where no new data is generated, and the researcher considers continuing the interview unnecessary (3). Perhaps at this stage, conversations with chatbots can be used as a complementary or even alternative data source in qualitative study interviews. Obviously, all aspects related to entry and exit criteria, such as the interviewee's previous experiences and cultural backgrounds, which are very important in the interview, must be observed. Perhaps AI can access diverse data from a wide range of sources to produce conceptually rich and relevant data and provide new perspectives. However, research integrity must be respected, but not necessarily in the same way as human studies. For example, we cannot define and identify specific inclusion criteria such as the real work experience of a human in an organization, the years of experience of a patient with a disease in real conditions, or the cultural and ideological backgrounds of the participant in the case of a chatbot. Therefore, interviewing with chatbots does not yield theoretical saturation and may produce incomplete and artificial results. Thus, in addition to the transparency of research and data collection, it is also necessary to define the framework for the ethical and correct use of chatbots instead of humans in interviews. This editorial highlights a new perspective on the use of AI-based chatbots in qualitative research, where the chatbot serves as a data source rather than as an analyst, methodologist, or assistant writer. Although AI provides opportunities for qualitative research, it also faces challenges that reviewers and authors should be aware of until the necessary technology is developed. Some of the opportunities and challenges of using AI chatbots in qualitative research can be the following: The use of AI and recommender systems in qualitative interviews helps reduce the cost and time of research, creates a sense of security, greater comfort for the interviewee, and allows them to express information without worry and bias (4), which helps with the depth of the data. Also, when reaching people who are geographically remote or specific groups that are not easily accessible, AI chatbots trained for specific purposes can be used. However, one must also recognize the challenges ahead and address them with appropriate policies. Among the most important of these is the depth of human feelings and emotions as they may arise in specific situations, which has not yet been defined for the machine. Also, informed consent, maintaining information security, and privacy are serious challenges and ethical issues for chatbots instead of humans (5). Ultimately, chatbots may be subject to a variety of errors, not from human error but from the data available to the AI, language limitations when translating data into the researcher's language, and even in countries like Iran, where access and use of IP from other countries are restricted. These technological challenges are unavoidable. Therefore, journal editors and authors should be cautious when using chatbots for various purposes, including as a substitute or complement to interviews and a source of data collection in qualitative studies.

This study delves into the factors influencing reuse intention and trust toward generative artificial intelligence (AI) chatbots, with a particular focus on the Iranian context—a region where the adoption of such technologies is rapidly evolving yet remains underexplored. Utilizing a framework grounded in the Technology Acceptance Model and the Diffusion of Innovation theory, this research complements those theories with construct privacy concerns. Data were collected via a questionnaire distributed to 567 Iranian ChatGPT users and analyzed using Partial Least Squares Structural Equation Modelling. The findings reveal that perceived usefulness, perceived ease of use, and compatibility significantly enhance trust toward ChatGPT among Iranian users. The findings also reveal that compatibility, perceived usefulness, and trust enhance ChatGPT users’ reuse intention. Interestingly, privacy concerns negatively impact trust but not reuse intention, highlighting a privacy paradox. This study expands upon existing literature by highlighting the intricate dynamics of trust and adoption, especially in an understudied context, Iran, offering practical insights for developers to enhance user experience through human-centric design. The results underscore the necessity for usability, compatibility with user values, and robust privacy measures to foster sustained user engagement and trust in the context of generative AI chatbots.