This study investigates the implementation of artificial intelligence (AI) tools in Vietnamese primary education, focusing on educators' experiences and socioeconomic factors affecting adoption. Through semi-structured interviews with 16 teachers and survey data from 160 educators across four provinces, we examined implementation patterns and challenges. The survey revealed that teachers prioritize AI tools for teaching activities (36%) and assessment (34%) over lesson preparation (30%). However, despite high interest in AI integration (82%), only 28% of educators reported adequate training. Our findings highlight that successful implementation faces three key challenges: limited teacher technological expertise, traditional academic priorities of parents, and time constraints for technology adoption. The study provides insights for education policymakers and leaders in developing teacher training programs and addressing the gap between infrastructure availability and effective AI implementation in primary education.

Building a math learning application on mobile devices following the STEAM educational model is a solution to help students see and grasp the role of mathematics in all aspects of their lives, have the mathematical skills to execute ideas correctly, be able to apply mathematical theories and skills to everyday life; create interest, curiosity, eagerness for learning in students. In addition, the application helps parents stay closer to their children and helps parents conveniently accompany their children in learning. This article summarizes the overview studies on applying the STEAM educational model in building math learning applications on mobile devices for grade 1 students.

This study introduces PERS (Personalized Exercise Recommender System), integrating Student-Generated Questions (SGQ) with a recommender system for programming courses. Implemented in a 10-week introductory programming course with 395 undergraduates (200 using PERS, 195 as control), PERS uses collaborative filtering to suggest personalized exercises. This study addresses the impact of PERS on student engagement and learning outcomes, the effectiveness of its recommender system, and students' perceptions of the system. Results show significant improvements in exercise completion rates, assignment scores, and final exam performance for PERS users compared to the control group. The system demonstrated high recommendation relevance and user satisfaction, suggesting its potential to enhance personalized learning in programming education.

Computer programming courses often face the challenge of high dropout rates, prompting educators to seek effective solutions to improve student engagement and retention. Student Question-Generation (SQG) have been explored as potential remedy to this issue, enabling advancements in computer science education. However, the majority of existing SQG support systems lack personalized features to enhance student learning. In response to this gap, the study introduces a personal recommender system of SQG exercises tailored for programming courses. The system leverages individual student preferences and SQG exercise complexities to personalize learning experiences within SQG activities. This paper presents the design of the system and its user interface and delves into motivations and technical bases underlying its development.