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.

Polygraph tests have been used for many years as a means of detecting deception, but their accuracy has been the subject of much debate. In recent years, researchers have explored the use of neural networks in polygraph scoring to improve the accuracy of deception detection. The purpose of this scoping review is to offer a comprehensive overview of the existing research on the subject of neural network applications in scoring polygraph tests. A total of 57 relevant papers were identified and analyzed for this review. The papers were examined for their research focus, methodology, results, and conclusions. The scoping review found that neural networks have shown promise in improving the accuracy of polygraph tests, with some studies reporting significant improvements over traditional methods. However, further research is needed to validate these findings and to determine the most effective ways of integrating neural networks into polygraph testing. The scoping review concludes with a discussion of the current state of the field and suggestions for future research directions.

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.

Years of studies have consistently demonstrated that people’s capacity to detect deceit is no better than chance. For law enforcement officers, accurate deception detection is critical. The traditional polygraph examination is now the sole standardized and reliable method for detecting deceit. There are several standardized scoring protocols (Lafayette Polygraph System 11.8.6) to Control Question Technique (CQT) Polygraph examinations: PolyScore, OSS-2, OSS-3 and manually scoring. Due to the ongoing controversy over which scoring system performs better in terms of avoiding false positive and false negative errors, this study introduces a Multilayer Perceptron Neural Network (MLP) prediction approach to Polygraph deception scoring utilizing manually scored examination data. A MLP was trained to predict high and low deception scores in 400 offender data, based on the most predictive psychophysiological indicators found in the scientific literature: amplitude of electrodermal reaction (ARED), amplitude of blood pressure in brachial pulse (ATAB), change of base line level in chest breathing (MNBRT) and difference of altitude between breathing cycles (DIFA). The model predicted the deception level of the 400 offenders with a correct classification rate (CCR) of 80%, result consistent with the prediction accuracy reported in the recent literature. The MLP neural network modeling results showed that based on the four psychophysiological indicators ARED, ATAB, MNBRT and DIFA there is an 80% correct classification rate of high and low deception scores received by insincere subjects. The key outcome of this study suggests that MLP represents a robust approach to identify deception in manually scored polygraph examinations.