primary-education
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- PaperLanguage Teaching Research25 Jul 2026
Classroom Relationship Profiles: Links with English-as-a-Foreign-Language Students’ Academic and Social Goals and Self-Regulated Learning Strategy Use
Qingyao Dan, Barry Bai, Xuan Zang
Four distinct classroom relationship profiles (Supportive, Teacher-oriented, Moderate, Isolated) were identified among EFL primary students using a person-centered approach. Students in Supportive and Teacher-oriented profiles showed higher English proficiency and more frequent use of achievement goals and self-regulated learning strategies compared to those in Moderate and Isolated profiles.
Original abstract
Classroom relationships represent a complex and dynamic system, in which students’ relationships with teachers and peers are intertwined with each other. A person-centered approach was used to investigate how multiple characteristics of a good-quality relationship (i.e., validation, caring, and instrumental guidance) with teachers and peers combined to form English-as-a-foreign-language primary school students’ classroom relationships profiles. Four distinct profiles of classroom relationships were identified: Supportive, Teacher-oriented, Moderate, and Isolated. Whether students’ gender and English proficiency level significntly predicted their likelihood of belonging to each profile was tested. The profiles were analyzed to see if there was a link to academic and social achievement goals and self-regulated learning in English language learning. The results indicated that the students in the Supportive and Teacher-oriented profiles were more likely to have a higher English proficiency who adopted achievement goals and used self-regulated learning strategies more frequently than those in the Moderate and Isolated profiles in English learning.
- PaperEdArXiv (OSF Preprints)17 Jul 2026
The Effectiveness of an Intensive AI Professional Development Program on Primary School Teachers' AI-PCK: The Pivotal Role of Assessment Rubric Design
Hossein Talebzadeh
An intensive AI professional development program significantly enhanced primary school teachers' AI-based Pedagogical Content Knowledge, with the largest gains observed in assessment rubric design. This suggests that professional development priorities should shift from content creation tools to mastering authentic, process-oriented assessment rubrics in the era of Generative AI.
Original abstract
This sub-study evaluates the effectiveness of an intensive Generative Artificial Intelligence (AI) professional development program in enhancing primary school teachers' AI-based Pedagogical Content Knowledge (AI-PCK) and identifies the specific component that yielded the highest level of improvement. Using a quasi-experimental pretest-posttest design, the study examined a sample of 142 in-service primary school teachers. Data were collected via a researcher-developed questionnaire covering 5 distinct components of AI-PCK and analyzed using paired t-tests and Cohen’s d effect size. The findings confirmed a highly significant overall positive impact of the professional development program on the teachers' total AI-PCK scores (p<0.001), accompanied by an exceptionally large effect size (d=3.71). Learning gain analysis revealed that the fifth component—familiarity with and application of assessment rubrics—experienced the most substantial improvement, achieving the highest learning gain (2.65) and the largest individual effect size (d=4.82). This result implies that in the era of Generative AI, the critical professional development priority for primary school teachers has shifted from content creation tools toward mastering the design of authentic, process-oriented assessment rubrics. This research offers a foundational framework for educational policymakers addressing the challenge of AI authenticity in primary education.
- NewsEdSurge24 Jun 2026
Vibe Coding Sparked a Love of Reading in My Classroom
A primary school teacher used vibe coding—AI-assisted software development—to build a personalized reading recommendation app that increased student engagement with the school library catalog. The tool connects children to books likely to excite them, addressing the problem of curated books sitting untouched on shelves.
Original abstract
<p>I have had big ideas before. Ideas that felt urgent and important at 11 p.m. and somehow evaporated by morning. So, when the idea for creating an AI-powered reading recommendation system to generate student excitement about our school’s library catalog came to me, I asked my partner what she thought about me giving up evenings and weekends for a year or two. “Just go for it,” she said.</p><p>That conversation was in May 2025. By November, my <a href="http://www.libraryaid.net"><u>vibe-coded app</u></a> was live in my classroom.</p><h2>Why I Started Vibe Coding</h2><p>I’m a U.K.-trained primary school teacher with 11 years of experience in international schools across the Middle East and Southeast Asia. Over the years, I have seen librarians carefully curate books only to have them sit untouched on library shelves. This is because there was no systematic way to connect each child to the book most likely to excite them.</p><p>Existing solutions were expensive, rigid, and built around proprietary book lists that didn’t match our collection. The more I looked at what was available, the more I realized the problem wasn’t that the technology didn’t exist — it was that nobody had built it for teachers like me, working in schools like mine.</p><p>So, I decided to build one myself, using an AI technique that I had read about with increasing interest: vibe coding.</p><h2><strong>Learning to Build an App</strong></h2><p>Vibe coding is a practice where people use AI tools to generate software code by describing what they want in plain language to the tool, with little to no traditional programming knowledge required. So, I started vibe coding and telling a large language model what I was trying to build. </p><p>Progress was painfully slow — a day forward, three days back. Over the summer months I nearly quit several times. The early architecture decisions haunted me: I was working on a 12-year-old Mac I hadn’t upgraded, and just getting the right development environment inst
- PaperComputers and Education: Artificial Intelligence16 Jun 2026
Fostering machine learning literacy in senior primary education: Evaluating a structured pedagogical course design
Siu Cheung Kong, Qiaoyi Wang
A structured six-to-8 hour machine learning course for senior primary students (average age 11.36) significantly improved their understanding of ML concepts, including supervised learning and reinforcement learning, with a Wilcoxon effect size of 0.55. The pedagogy combined guided worksheets, hands-on activities, and iterative refinement within robot software, enhancing conceptual learning and engagement. Students even developed initial reflections on distinguishing between AI and human learning.
Original abstract
Current K–12 artificial intelligence (AI) literacy education emphasizes tool usage over fundamental concepts, yet AI literacy requires grasping how and why AI works – critical for an AI-driven society. This highlights the need for machine learning (ML) education for young learners. We designed and evaluated a six-to 8-h ML course for 752 senior primary students (average age 11.36) across seven Hong Kong primary schools. Pre- and post-test results showed significant improvement in ML understanding, with a Wilcoxon effect size of 0.55. Students comprehended supervised learning and reinforcement learning, including algorithms such as k-nearest neighbours and artificial neural networks, via training robots in competitive circuit tasks and real-time algorithm visualization. Thematic analysis of student interviews revealed that our structured pedagogical approach — blending guided worksheets, hands-on activities, and iterative refinement of data processing, parameter adjustment, and model training within the robots' software — enhanced students’ conceptual learning and engagement. Surprisingly, they developed initial reflections on distinguishing between AI and human learning. These findings suggest the feasibility and promise of teaching fundamental ML concepts to senior primary students through a structured course design. The study contributes to future research and practice in fostering ML literacy among young learners, providing actionable insights for educators to support and allocate resources.