With the continual development of artificial intelligence and smart computing in recent years, quantitative approaches have become increasingly popular as an efficient modeling tool as they do not necessitate complicated mathematical models. Many nations have taken steps, such as transitioning to online schooling, to decrease the harm caused by coronaviruses. Inspired by the demand for technology in early education, the present research uses a radial basis function (RBF) neural network (NN) modeling technique to predict preschool instructors’ technology usage in classes based on recognized determinant characteristics of technology acceptance. In this regard, this study utilized the RBFNN approach to predict preschool teachers’ technology acceptance behavior, based on the theory of planned behavior, which states that behavioral achievement, in our case the actual technology use in class, depends on motivation, intention and ability, and behavioral control. Thus, this research design is based on an adapted version of the technology acceptance model (TAM) with eight dimensions: D1. Perceived usefulness, D2. Perceived ease of use, D3. Perceived enjoyment, D4. Intention to use, D5. Actual use, D6. Compatibility, D7. Attitude, and D8. Self-efficacy. According to the TAM, actual usage is significantly predicted by the other seven dimensions used in this research. Instead of using the classical multiple linear regression statistical processing of data, we opted for a NN based on the RBF approach to predict the actual usage behavior. This study included 182 preschool teachers who were randomly chosen from a project-based national preschool teacher training program and who responded to our online questionnaire. After designing the RBF function with the actual usage as an output variable and the other seven dimensions as input variables, in the model summary, we obtained in the training sample a sum of squares error of 37.5 and a percent of incorrect predictions of 43.3%. In the testing sample, we obtained a sum of squares error of 14.88 and a percent of incorrect predictions of 37%. Thus, we can conclude that 63% of the classified data are correctly assigned to the models’ dependent variable, i.e., actual technology use, which is a significant rate of correct predictions in the testing sample. This high significant percentage of correct classification represents an important result, mainly because this is the first study to apply RBFNN’s prediction on psychological data, opening up a new interdisciplinary field of research.

The interaction between teachers and students is critical to the learning process. Student success and learner satisfaction have consistently improved in educational situations where instructors and students connect frequently and meaningfully. The Community of Inquiry (CoI) framework, as well as the sense of belonging concept, have received a significant amount of attention from researchers investigating online learning since its debut. The current study focuses on the CoI framework in general, and in particular on studies on teaching, social, and cognitive presences in connection to students’ feeling of belonging and grit enhancement. This research investigated the opinion of 310 students at the Aurel Vlaicu University of Arad regarding their satisfaction with their teachers’ presences, their academic sense of belonging, and their grit. Our methodology followed an innovative approach. First, we employed a network analysis on all subscales’ mean scores, and then we performed a sequential mediation analysis based on both the network analysis results and the conclusions from the literature review. We tested whether students’ perceived classroom comfort and perceived faculty support sequentially mediated the relationship between teacher’s cognitive presence and students’ grit. According to the scientific literature, teacher’s cognitive presence consists of four fundamental categories: triggering events, exploration, integration, and resolution, which specifically the validation of knowledge by cooperation and reflection in a community of inquiry. We further tested if sense of belonging might mediate the relationship between teachers’ cognitive presence and students’ grit. The results show that students’ perceived classroom comfort and perceived faculty support partially and significantly sequentially mediate the relationship between teachers’ cognitive presence triggering events and students’ grit. The results are then further used to suggest possible recommendations for designing holistic learning environments in Romanian higher education institutions.

This study investigates how compatibility and perceived enjoyment affect the link between intention to use and actual technology use in Romanian preschool education, building on earlier studies. Methods: 300 participants were invited to participate in this research from 15 Romanian counties. 182 preschool teachers’ questionnaires were utilized for data analysis after the return and screening of responses. A valid and accurate scale evaluating preschool teachers’ behavior towards technology adoption was included in the questionnaire, along with self-reported demographic data, professional identification, and other information. Data was analyzed using SPSS V.16. Results: (1) Intention to use, compatibility, perceived enjoyment, and actual use were positively associated. (2) The effect of compatibility and perceived enjoyment on the link between intention to use and actual technology use was carried out in the following way: Intention to use → Compatibility with technology → Perceived enjoyment → Actual use. We hypothesize that intention to use affects compatibility, compatibility affects perceived enjoyment, and, lastly, perceived enjoyment affects actual use. For a more robust validation of results, we have also modelled this relationship with the Radial Basis Function (RBF) neural network. Conclusion: Compatibility and perceived enjoyment partially mediate the relationship between intention to use and actual technology use in class by Romanian preschool teachers. According to the theory of planned behavior, this study brought to light the intricacy of the relationship between preschool teachers’ intention to utilize technology in the classroom and their actual usage of it. Limitations and implications are discussed.

The general objective of this research is to investigate students' attitudes towards artificial intelligence (AI) in relation to a series of socio-demographic data such as age, gender, university year, general level of familiarity with technology, and the attitude of the social group. The purpose is to identify the characteristics of students that could serve as predictive factors for their attitude towards AI and to outline directions for strategies aimed at efficiently managing their relationship with AI products. The results, obtained through the application of the ATTARI-12 scale (Stein, et al., 2024) on a sample of 87 students from three faculties in Bucharest and processed using IBM SPSS Statistics v23, showed statistically significant correlations between the socio-demographic data (age, year of study, and social group attitude) and the attitude toward AI, and that these are also factors that predict students' attitudes towards AI by 82%. The results also highlight that the age group 25-34 has the most open attitude toward AI, and that there are significant differences in this attitude depending on the social group attitude. Although the research objective was achieved, due to the small number of subjects, this study represents a pilot study with associated limitations, such as the inability to generalise conclusions. Instead, the findings serve to guide strategies for the efficient management of AI-related interactions while adhering to ethical principles.

The general objective of this research was to investigate the attitudes of Bucharest students toward artificial intelligence (AI)—in particular, ChatGPT—in relation to their personality traits, coping strategies, and personal values to identify psychosocial approaches for students’ effective reporting toward this AI product. As there was no instrument validated and calibrated on Romanian students, the scale constructed by Acosta-Enriquez et al. in 2024 was adapted to students from Bucharest (N = 508). Following the item analysis, the adapted scale was reduced to 16 items, and, following the factor analysis (EFA–0.81 < α < 0.91), the structure with three factors (cognitive, affective, and behavioral components), explaining 53% of the variation in Bucharest students’ attitudes toward ChatGPT, was maintained considering the results of the confirmatory factor analysis—CFA (χ²(79) = 218.345, p < 0.001; CMIN/DF = 2.486; CFI = 0.911; TLI = 0.900; RMSEA = 0.058 (90% CI: 0.50–0.065). The present study showed that 85.53% of the research subjects used ChatGPT at least once, of which 24.11% have a positive/open attitude toward ChatGPT, and that there are correlations (p < 0.01; 0.23 < r² < 0.50) between students’ attitudes toward ChatGPT and several personality traits, coping strategies, and personal values. It also proves that the three components of the attitude toward ChatGPT (cognitive, affective, and behavioral) are correlated with a series of personality traits, coping strategies, and personal values of students. Although the general objective was achieved and the adapted scale has adequate psychometric qualities, the authors propose in future studies to expand the group of subjects so that the scale can be validated at the level of the Romanian population. In this research, at the end, several concrete approaches are proposed for the effective reporting of students toward this AI product, which, beyond the ethical challenges, also recognizes the benefits of technology in the evolution of education.

The impact of artificial intelligence (AI) on our lives can no longer be neglected, and thus, understanding this phenomenon and how humans interact with it is becoming more important than ever. The study emphasizes one of the psychological characteristics that helps us in accepting AI and using it to its fullest, namely the psychological resilience that each of us, and in particular emergency service workers, possesses in different proportions. The analysis of the relationship between the two dimensions (psychological resilience and AI) highlights, among other things, the importance of how organizations can use this interaction to achieve a framework for developing the psychological resources of their employees so that they are increasingly receptive to the use of AI in their work, pursuing the benefits it brings. The study is conducted on 89 emergency service workers and focuses on the positive and highly statistically significant relationship between psychological resilience and attitudes towards AI. The study also particularly emphasizes the relationship between tenacity and self-efficacy, self-confidence, ability to learn from life experiences, rapid recovery from negative events, social and family resources as dimensions of psychological resilience in relation to attitude towards IA of emergency workers.