The rapid evolution of technology has significantly transformed the educational landscape, with the advent of Large Language Models (LLMs) introducing new possibilities for personalized learning. This systematic review examines the educational impact of LLM-based learning systems compared to traditional educational approaches, focusing on six critical research questions. These questions explore the effectiveness of LLMs in enhancing student engagement, emotional and social development, real-time progress monitoring, and their role in creating fair and rigorous examination environments. Furthermore, the review addresses challenges such as ethical considerations, privacy concerns, and the extent to which LLMs can simulate real-world teaching experiences. A total of 55 studies, published between 2020 and 2024, were systematically analyzed to explore the impact of Large Language Models (LLMs) on educational outcomes, including emotional, social, and academic development. These studies included a combination of peer-reviewed articles, conference papers, and journal publications, which were selected through a set of predetermined inclusion and exclusion criteria. Quality assessment criteria ensured the inclusion of high-quality research focusing on the application of LLM-based AI technology in education. The review also highlights key challenges and limitations, including issues of accessibility, ethical dilemmas, and the integration of AI into traditional education systems. Findings underscore the potential of LLMs to revolutionize education through personalized learning, while also addressing the critical need for rigorous evaluation and ethical deployment to ensure equitable and effective outcomes.

Automatic speech recognition (ASR) is one of the ways used to transform acoustic speech signals into text. Over the last few decades, an enormous amount of research work has been done in the research area of speech recognition (SR). However, most studies have focused on building ASR systems based on adult speech. The recognition of children’s speech was neglected for some time, which means that the field of children’s SR research is wide open. Children’s SR is a challenging task due to the large variations in children’s articulatory, acoustic, physical, and linguistic characteristics compared to adult speech. Thus, the field became a very attractive area of research and it is important to understand where the main center of attention is, and what are the most widely used methods for extracting acoustic features, various acoustic models, speech datasets, the SR toolkits used during the recognition process, and so on. ASR systems or interfaces are extensively used and integrated into various real-life applications, such as search engines, the healthcare industry, biometric analysis, car systems, the military, aids for people with disabilities, and mobile devices. A systematic literature review (SLR) is presented in this work by extracting the relevant information from 76 research papers published from 2009 to 2020 in the field of ASR for children. The objective of this review is to throw light on the trends of research in children’s speech recognition and analyze the potential of trending techniques to recognize children’s speech.

As Sustainability becomes a central aspect of consumer values and corporate responsibility, marketing has evolved to incorporate concepts such as “green,” “sustainable,” and “smart” marketing, which are now being increasingly facilitated and amplified by digital technologies. This research investigates the intersection of digital transformation and green marketing, particularly emphasizing the transformative impact of technologies such as the Internet of Things (IoT), blockchain, artificial intelligence (AI), and digital platforms on sustainable marketing practices. The authors employ Latent Dirichlet allocation (LDA), a topic modeling technique, to analyze 2,061 research publications on green marketing from the Scopus database from 1991 to 2025. The analysis identifies the most prevalent themes and research clusters, which include sustainable consumption, digital marketing techniques, consumer purchasing intention, and green supply chains. An in-depth comprehension of contemporary research trends is provided by coherence-based evaluations, which generate solutions that encompass two, five, and ten topics. The primary scientific contribution of this work is the systematic mapping of fragmented literature and the identification of research voids, which has resulted in the proposal of a Theory–Context–Methodology (TCM) framework to guide future research. This investigation bolsters the theoretical underpinnings of green marketing and underscores the transformative influence of digital technology on promoting environmental Sustainability through marketing innovation.

Robotic Process Automation (RPA) is transforming the education sector by automating repetitive, rule-based tasks previously carried out by humans. This paper investigates the applications, benefits, challenges, and implications of RPA in education, focusing on its impact on administrative processes, student support services, and teaching methodologies. By analyzing 54 studies conducted between 2020 and 2024, this research explores how educational institutions have adopted RPA to enhance operational efficiency. Case studies reveal common success factors, including significant time savings, error reduction, and improved satisfaction for both students and staff. RPA has enabled institutions to streamline administrative workflows, optimize resource management, and offer personalized learning experiences. However, challenges such as implementation costs, data security, and the need for effective change management are also addressed. Additionally, this paper examines ethical concerns, particularly those surrounding job displacement and privacy. The findings underscore the transformative potential of RPA while highlighting the complexities of its integration into educational settings. The methodology followed PRISMA guidelines, with a systematic search of academic databases including IEEE Xplore, Scopus, Web of Science, and Google Scholar. Inclusion criteria focused on studies published between 2020 and 2024 that addressed RPA applications in education. Data analysis was conducted using thematic synthesis to identify key trends across the selected studies.

Robotic Process Automation (RPA), employing software robots or bots, has emerged as a pivotal technological advancement, automating repetitive, rule-based tasks within business operations. This leads to enhanced operational efficiency and substantial cost reductions. In this systematic review, information was extracted from 62 pertinent research articles on RPA published between 2016 and 2022. The findings elucidate the fundamental principles of RPA, predominant trends, and leading RPA frameworks, alongside their optimal industry applications. Moreover, the necessary procedural steps for RPA implementation in industries are delineated. The objectives of this study encompass highlighting contemporary RPA research directions and evaluating its potential in streamlining diverse business processes.