Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

Brain–computer interface (BCI) technology has the potential to positively contribute to the educational learning environment, which faces many challenges and shortcomings. Cognitive and affective BCIs can offer a deep understanding of brain mechanisms, which may improve learning strategies and increase brain-based skills. They can offer a better empirical foundation for teaching–learning methodologies, including adjusting learning content based on brain workload, measuring student interest of a topic, or even helping students focus on specific tasks. The latest findings from emerging BCI technology, neuroscience, cognitive sciences, and psychology could be used in learning and teaching strategies to improve student abilities in education. This study investigates and analyzes the research on BCI patterns and its implementation for enhancing cognitive capabilities of students. The results showed that there is insufficient literature on BCI that addresses students with disabilities in the learning process. Further, our analysis revealed a bias toward the significance of cognitive process factors compared with other influential factors, such as the learning environment and emotions that influence learning. Finally, we concluded that BCI technology could improve students’ learning and cognitive skills—when consistently associated with the different pedagogical teaching–learning strategies—for better academic achievement.

This study explores the convergence of biometric analytics and machine learning in online education, where the level of student participation directly impacts academic achievement. In this study, various machine learning models were employed to identify cognitive states using eye-tracking and electroencephalogram data, which can provide quantitative indicators of cognitive activity. A comparative analysis of convolutional neural network (CNN), logistic regression, decision tree, random forest, gradient boosting, support vector machine, and K-nearest neighbors (KNN) models was conducted. The receiver operating characteristic curve was used as a benchmark to assess the effectiveness of the models in interpreting the subtle patterns in biometric feedback. The results show that the CNN model exhibited outstanding performance, achieving an area under the curve value of 0.98 in interpreting intricate data that reflect the levels of student involvement. In addition, the gradient boosting and KNN models exhibited noteworthy accuracy, and the logistic regression and random forest models realized a crucial equilibrium by providing a high level of interpretability and strong performance. This is particularly important for educational applications where the model’s rationale must be transparent. The findings of this study support the idea that machine learning can considerably improve remote learning platforms, leading to more customized and interactive educational experiences. However, the findings also acknowledge relevant ethical concerns and the need to understand these advanced technologies comprehensively.

The integration of artificial intelligence and natural language processing technologies has facilitated the adoption of advanced systems, such as ChatGPT, across various sectors, particularly in education and healthcare. These innovative technologies promise to deliver rapid and personalized services to key stakeholders, including educators, healthcare professionals, students, and patients. This paper presents a rigorous scoping review of prior research on the application of ChatGPT in the education and healthcare sectors. The comprehensive review methodology involved a meticulous analysis of 71 articles from reputable digital databases. The outcomes of this review provide a comprehensive understanding of the existing body of research related to the applications of ChatGPT in education and healthcare, encompassing insights from previous studies, a discussion of encountered limitations and challenges, exploration of potential research directions for implementing ChatGPT technology, and a deeper analysis of the ethical implications of its use and how to address them. We discuss the implications of these findings and recommendations, contributing to a growing body of knowledge regarding the application of artificial intelligence in these vital and sensitive sectors.

Remote phenomenon is of expanding significance due to the increasing popularity of distance education. In this study, we examines the occurrence of brain-to-brain synchronization during remote learning using electroencephalography (EEG). Participants actively participated in a remote learning challenge by attending synchronous online classes, while their EEG data was captured. The work seeks to comprehend synchronization and intermittent desynchronization through state-of-the-art neuroimaging and K-nearest Neighbors (KNN) algorithm. By analyzing data obtained from EEG, it is feasible to evaluate the level of brainwave synchronization between teachers and students using correlation coefficients. For educators, the study’s findings provide valuable insights that can be used to improve instructional techniques and guide them through the ever-changing digital ecosystem. Understanding ideal synchronization times and the factors contributing to desynchronization makes it possible to design therapies that enhance cognitive resonance . This multidisciplinary work crosses the gap between education and neuroscience, and contributes to clarifying the significance of brain-to-brain synchronization in remote learning and highlighting the role played by ML in resolving cognitive alignment issues.