This study explores the application of large language models (LLMs) to create computational thinking tasks for the Bebras International Challenge through a single-case study approach. Using exemplar-based prompting with seven authentic Bebras tasks from the 2024 cycle as contextual input, a task was developed that was subsequently accepted for inclusion in the 2025 international Bebras challenge. Comparison with the exemplar tasks confirmed that the generated content drew from multiple sources rather than replicating any single task, combining grid-based constraint satisfaction, rule-based filtering, and logical deduction into a novel navigation puzzle with engaging narrative context. International expert reviewers evaluated the task using established Bebras quality criteria, confirming successful alignment with core pedagogical requirements including age-appropriateness, clarity, and cultural neutrality. However, two significant gaps emerged in the broader authoring workflow: accessibility compliance in the researcher-authored visual components and technical inaccuracies in the LLM-generated informatics framing. Following collaborative revision by international editors that addressed these concerns while preserving the LLM’s creative contributions, the task achieved acceptance for international use. The findings reveal a collaborative pipeline comprising contextual preparation, LLM-guided generation, human technical implementation, expert community review, and collaborative revision. Results from this case suggest that LLMs can efficiently generate educationally sound creative foundations while requiring integrated human expertise to meet specialised standards and ensure inclusive design, with the task’s acceptance providing encouraging evidence for the viability of this collaborative approach.
This paper presents research findings on primary school students’ awareness of the ethical aspects of using artificial intelligence (AI) tools for homework. The study used a self-constructed online questionnaire administered to 301 primary school students from grades five to eight attending two primary schools in Northwestern Croatia. The results show that 57.8% of students already use AI tools to complete their homework. Students in higher grades use it more often than those in lower grades. A significant Spearman correlation exists between respondents’ daily time spent on social media and the use of AI tools (r = 0.34, p < .001). Only 36.5% of students in the sample believe it is unacceptable to copy AI-generated answers when completing their homework. The results underscore the need to integrate AI education into the primary school informatics curriculum in the Republic of Croatia, including introducing content that addresses the ethical aspects of using AI tools.