In early mathematics education, the beliefs of the teacher are essential for facilitating the integration of technology into teaching mathematics. This study explores the influence of physical and digital interactive learning environments on the development of early childhood teachers’ beliefs about integrating technology into early mathematics classrooms. To understand the development of teachers’ beliefs, a half-year, content-based professional development program that incorporates interactive digital and physical environments was designed for this research on early childhood mathematics teachers (N = 58). We administered a questionnaire to measure teachers’ beliefs regarding employing an interactive technological environment in mathematics classrooms before and after engaging in an intervention program. In addition, a reflective writing strategy was implemented with the participants (N = 10) to understand the development of their beliefs about technology via an evaluation of their levels of reflection. In general, the research findings indicate that an interactive learning environment supports an improvement in teachers’ beliefs about technology. Furthermore, positive qualitative findings were drawn from the reflective writing essays of early childhood teachers. The qualitative findings indicate that an interactive environment enables teachers to be conscious of selecting effective math-specific technologies that facilitate children’s exploration and discovery of mathematics concepts.

American scientific publishing group

American scientific publishing group

Mathematics education is being reshaped by artificial intelligence tools, which tailor instruction to individual learners through adaptive guidance and instant feedback, and in doing so can deepen conceptual understanding and build problem-solving capacity. Whether students accept such tools and keep using them, however, rests on a combination of cognitive, behavioral, and psychological conditions. The present study investigated how first-year female undergraduate students (N = 125) at a university in Saudi Arabia accept AI tools, and how psychologically resilient they are, within a mathematics learning environment supported by AI. A further aim was to determine how psychological resilience relates to both technology acceptance and learning engagement in that environment. A model was therefore constructed integrating the Technology Acceptance Model (TAM), learning engagement, and psychological resilience. Data was gathered through a quantitative cross-sectional survey at the end of the semester and analyzed using descriptive statistics and Structural Equation Modeling (SEM). Participants reported high technology acceptance and psychological resilience. Model fit was satisfactory, and the proposed hypotheses were supported. Psychological resilience was positively associated with both technology acceptance and learning engagement, and was indirectly associated with behavioral intention through the model’s mediating constructs, of which perceived usefulness and perceived ease of use are the first links. By adding psychological resilience and learning engagement to TAM, these findings may broaden the model and help clarify how these constructs relate to AI acceptance in AI-supported mathematics learning.

American scientific publishing group

The incorporation of expert opinions and handling of data uncertainty are addressed by Al-Alkhazaleh through the introduction of a soft expert set. This extension of the soft set framework aims to enhance the analysis and decision-making processes by incorporating expert knowledge. On the other hand, the utilization of single neutrosophic sets (SVNSs) and fuzzy sets (FSs) has been introduced as models to effectively handle uncertain data. In this work, the authors propose a model that combines the essential characteristics of fuzzy sets (FSs) and single neutrosophic sets (SVNSs) within expert systems. Consequently, this model aims to offer decision-makers increased flexibility when interpreting uncertain information, empowering them in the decision-making process. From a scientific point of view, the process of evaluating this high-performance SVNFSES disappears. Therefore, in this paper, we initiated a new approach known as single-valued neutrosophic fuzzy soft expert sets (SVNFSESs) as a new development in a fuzzy soft computing environment. We investigate some fundamental operations on SVNFSESS along with their basic properties. Also, we investigate AND and OR operations between two SVNFSESS as well as several numerical examples to clarify the above fundamental operations. Finally, we have given an aggregation operator (AO) for SVNFSESs to construct a new algorithm to demonstrate the method’s effectiveness in handling some real-life applications.