With the use of technology in education, the integration of digital literacy and technological skills with pedagogy has become one of the important competencies that teachers need to master. Thus, the study investigated the digital literacy, technological and pedagogical competencies of primary school teachers in Kazakhstan on a relational basis. The study was conducted with 223 primary school teachers working in various schools in Almaty. 'Digital Literacy Scale', 'Pedagogical Competence Scale' and 'Technological Competence Scale' were used to collect the data. T and F tests were used to compare school teachers' digital literacy, pedagogical and technological competencies based on gender and professional seniority. 'Multiple Regression Technique' was used to analyze the relationships between the variables of school teachers' digital literacy, pedagogical and technological competencies. The findings revealed that the pedagogical competencies of primary school teachers were high, while their digital literacy and technological competencies were at a moderate level. Pedagogical and technological efficacy and digital literacy of primary school teachers differed significantly based on gender and professional seniority. Male primary school teachers had high levels of technological competence and digital literacy, whereas female primary school teachers had high levels of pedagogical competence. Multiple regression analysis indicated that teachers' digital literacy significantly predicted their pedagogical and technological competencies.

This study investigates the performance of GPT-4, an advanced AI model developed by OpenAI, on the force concept inventory (FCI) to evaluate its accuracy, reasoning patterns, and the occurrence of false positives and false negatives. GPT-4 was tasked with answering the FCI questions across multiple sessions. Key findings include GPT-4’s proficiency in several FCI items, particularly those related to Newton’s third law, achieving perfect scores on many items. However, it struggled significantly with questions involving the interpretation of figures and spatial reasoning, resulting in a higher occurrence of false negatives where the reasoning was correct, but the answers were incorrect. Additionally, GPT-4 displayed several conceptual errors, such as misunderstanding the effect of friction and retaining the outdated impetus theory of motion. The study’s findings emphasize the importance of refining AI-driven tools to make them more effective in educational settings. Addressing both AI limitations and common misconceptions in physics can lead to improved educational outcomes.

This study investigates university students’ conceptual understanding and misconceptions about the kinetic molecular theory of gases (KMTG) in Kazakhstan. A sample of 223 students from two universities was assessed using the KMTG concept inventory. The sample consisted of 54.3% females and 45.7% males, with participants primarily in their first year of study (64.1%) and aged 17-18 (79.8%). The findings reveal significant misconceptions of molecular motion, gas pressure, entropy, and temperature changes in thermodynamic systems. For instance, 81.2% of students demonstrated incorrect conceptions about entropy, while 80.3% misunderstood temperature behavior during adiabatic processes. Statistically significant differences were observed in performance based on university affiliation and grade level, with Abai Kazakh National Pedagogical University students and first-year students outperforming second-year students. However, no significant gender-based differences were identified. This research points out the persistence of misconceptions in molecular and thermodynamic physics, indicating the need for localized and interactive instructional strategies. These findings contribute to the growing body of literature on physics education in Central Asia, providing recommendations for curriculum development and teacher training in the region.

The quality of courses plays a significant role in shaping effective future educators, particularly in mathematics education, where innovative assessment and teaching methods are crucial. This study investigates the experiences and satisfaction of future mathematics teachers enrolled in a course on criteria-based assessment (CBA) techniques, delivered using a project-based learning (PjBL) approach. A total of 98 participants from a university in Kazakhstan provided survey data that were analyzed using cluster analyses. The k-means algorithm identified three distinct clusters of participants: “less engaged or satisfied,” “moderately satisfied,” and “highly engaged and satisfied.” The optimal number of clusters was determined using the elbow method, and 3D scatter plots provided a visual representation of the data patterns. Additional analyses examined gender, prior experience with assessment techniques, and prior experience with in PjBL, highlighting significant variations in satisfaction and engagement levels across these factors. Results showed that students with intermediate knowledge of assessment techniques and prior PjBL experience were more likely to belong to the highly satisfied cluster. Gender distribution indicated a slight tendency for female students to benefit more from the CBA course. Key challenges included complex tasks such as reliability calculations, which impacted the less engaged group more significantly. Findings highlight the importance of aligning course design with learner needs, offering structured support, and integrating foundational training in CBA and PjBL methodologies. This study provides actionable recommendations for enhancing teacher education programs and improving the implementation of innovative teaching and assessment strategies.

The growing integration of artificial intelligence (AI) in education presents new opportunities and challenges for instructional design (ID). This study aims to develop and validate an AI-Incorporated Analyze, Design, Develop, Implement, and Evaluate (ADDIE) Instructional Model that embeds AI functionalities into the five phases—Analyze, Design, Develop, Implement, and Evaluate. The model is designed to enhance instructional planning through learner diagnostics, content generation, adaptive assessments, and automated feedback. To examine the perceived necessity and applicability of AI integration in each ADDIE phase, a survey-based research design was employed. A total of 167 mathematics educators, including 143 pre-service and 24 in-service teachers, participated by responding to Likert-scale items and open-ended questions. Results indicate strong support for AI integration, especially in the Design, Develop, and Evaluate phases. Pre-service teachers prioritized support in the Implement phase, while in-service teachers emphasized the value of AI in the Evaluate phase. Despite this broad enthusiasm, participants raised concerns about data privacy, ethical implications, and the potential loss of human creativity. These findings underscore the need for balanced, ethical, and pedagogically grounded AI integration in education. The proposed model offers a structured, adaptable framework for instructional designers and educators aiming to leverage AI for learner-centered and technology-enhanced teaching.

This study investigates the impact of constructivist teaching methods on achievement and misconceptions in mechanics, with a focus on gender differences. The research involved 88 first-year physics students enrolled in the physics teacher training program. Using a quasi-experimental design, students were divided into experimental (constructivist teaching) and control (traditional teaching) groups, with each group completing a pre- and post-test to assess changes in understanding and misconceptions around key mechanics concepts, particularly the concept of force. Results reveal that the constructivist intervention significantly improved achievement scores, especially for female students, thus reducing the gender gap in physics achievement. However, persistent misconceptions were observed in specific, abstract topics, such as motion in frictionless environments and rocket propulsion, which were less effectively addressed by the constructivist approach alone. These findings contribute to the literature on gender-sensitive instructional strategies and points out that constructivist approaches can be further optimized to support conceptual understanding in complex scientific domains.