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 goal of this study was to widen the scope of the social component of family sustainability. The fundamental goal of this non-experimental, quantitative study was to look at the links between parenting correlates, personality characteristics, marital satisfaction, and well-being in couples, as well as to construct explanatory prediction models for relational and marital satisfaction. The study focused on the effects of personality traits, parental self-efficacy, and attachment to marital and relational satisfaction. The test instruments applied were: the Experiences in Close Relationships-Revised, Marital Adjustment Test, Couple Satisfaction Index, Relationship Satisfaction Scale, Family Distress Index, Generalized Self-Efficacy Scale (adapted to the role of a parent), Mowen’s Personality Scale, and demographic data. A total of 238 Romanians answered the online survey, recruited based on a convenience sampling method. Participants in this research were individuals who were both married and had at least one child. The main findings show that agreeableness, openness, conscientiousness, anxious and avoidant attachment, and marital adjustment predict the satisfaction in the relationship, and openness partially mediates the association between marital adjustment and relationship satisfaction. Parental self-efficacy appears to influence the association between relationship satisfaction and marital satisfaction. An increased parental self-efficacy score predicts an increased relationship satisfaction and marital satisfaction. The higher the parental self-efficacy, the higher the satisfaction in the relationship, which leads to an increase in satisfaction in the couple. These results show that marital adjustment and adaptation are related to relational satisfaction, and these relationship are partially mediated by an individual’s openness. Despite some limitations, the current study significantly contributes to couples therapies and interventions in terms of physical and mental health, and the study provides insight into the experiences and perspectives of married individuals with children in Romania.

Drawing on digital agency theory, expectancy–value frameworks, and self-regulated learning perspectives, this study proposes and tests a moderated mediation model explaining students’ intentions to use AI. Using data from 673 university students, we examined whether sense of positive agency (SOPA) predicts intention to use AI indirectly through perceived value and perceived benefits of AI, and whether these pathways are conditionally shaped by sense of negative agency (SONA). Conditional process analysis (PROCESS Model 59) showed that SOPA had no direct effect on intention to use AI (b = 0.013, p = 0.882). Instead, its influence was fully indirect and conditional. SOPA predicted perceived value and perceived benefits of AI only at moderate to high levels of SONA, with significant SOPA × SONA interactions for both mediators (p = 0.040). Perceived value strongly predicted intention to use AI (b = 0.385, p < 0.001), and this relationship was amplified at higher levels of negative agency (b = 0.138, p = 0.002). In contrast, the effect of perceived benefits on intention weakened as SONA increased (b = −0.125, p = 0.005), becoming non-significant at higher levels of negative agency (Johnson–Neyman point ≈ 2.99). The final model explained 50.4% of the variance in intention to use AI. Overall, the findings indicate a conditional appraisal mechanism: as negative agency increases, perceived value becomes a stronger predictor of intention, whereas the motivational contribution of perceived benefits weakens and becomes non-significant beyond the Johnson–Neyman threshold. These results support an agency-aware account of AI adoption focused on how cognitive appraisals relate to intention under different perceived agency orientations, without implying ethical reasoning or moral deliberation processes not measured in this study.

Artificial intelligence (AI) is increasingly embedded in students’ learning practices, yet little is known about how AI engagement evolves from an external technological aid into an agentic component of self-regulated learning. This study applies psychological network analysis to examine the structural relations among students’ knowledge of AI, perceived value and perceived cost of AI, intention to use AI, and three core self-regulated learning processes—forethought, performance control, and self-reflection—across different levels of AI use frequency. The study was conducted on a sample of 673 university students and early-career graduates. Networks were estimated using EBICglasso for the full sample and separately for low-, moderate-, and high-frequency AI users. Across all models, a stable two-system organization emerged, consisting of an AI appraisal subsystem (knowledge, value, cost, intention) and a self-regulation subsystem (forethought, performance control, self-reflection). However, the connectivity between these subsystems differed systematically by usage frequency. Among low-frequency users, perceived cost was more prominently positioned within the appraisal subsystem, suggesting that cost-related concerns may be more salient in lower-frequency use contexts. In contrast, in the moderate- and high-frequency groups, performance control appeared more centrally positioned at the interface between appraisal and self-regulation, suggesting stronger alignment between AI-related appraisals and performance-level regulatory processes in these groups. Students’ knowledge of AI displayed context-dependent structural roles across networks, consistent with a variable relational position across use-frequency groups. Overall, the findings suggest that AI appraisal and self-regulated learning form partially distinct but interconnected subsystems, and that their configuration may vary across AI use-frequency groups. Because subgroup comparisons were descriptive and formal stability analyses were not conducted, these findings should be interpreted as exploratory. The results do not support causal or developmental inference and require replication using bootstrapped stability analyses and formal network comparison procedures.