Informatics in Education

Computer science students are required to master data structures and algorithms, yet learning these topics often presents significant cognitive challenges. This study investigates whether a bundled package of adaptive personalization is associated with learning outcomes, perceived workload, and usability, using an existing educational visualization tool called VZOU (Visualization Zone of Understanding). The enhanced adaptive version of VZOU incorporates personalized learning paths structured around a visible learning progression map, enabling learners to monitor their progress, identify gaps, and take ownership of their learning path. Basic gamification features, Bloom-aligned progressive sequencing, and topic-specific formative quizzes are designed to support self-regulated learning through immediate feedback and motivational reinforcement. In a between-subjects study (N = 55), we compared control, non-adaptive, and adaptive groups. The adaptive group scored significantly higher on the post-test than the non-adaptive and control groups (mean 89.3 vs. 73.3 and 71.0 out of 100, respectively), and showed numerically but not statistically higher usability scores (SUS). Perceived mental workload showed a mixed pattern: the overall six-subscale NASA-TLX composite did not differ significantly by condition, though Mental Demand specifically was significantly higher in the adaptive group than in the control group, and a workload composite excluding self-rated success also differed significantly across conditions. The bundled adaptive VZOU package, combining a diagnostic-driven graph, gated progression, formative quizzes, and gamification, was associated with higher post-test performance, while raising certain aspects of perceived workload. As these components were not manipulated independently, the estimate reflects the package as a whole, not any single element.

K-12 computer science (CS) education has gained momentum globally, leading to diverse curricula. Evaluating the extent to which such curricula achieve their goals is essential yet highly complex. This paper presents a framework for evaluating K-12 CS curricula, adopting a processive perspective in which a curriculum is viewed as an evolving entity, whose seed is the policymakers’ vision and whose fruits are its outcomes. The framework explores this evolution through the prism of educational goals, both cognitive and affective, building on established models from curriculum theory. We developed the framework while evaluating a national 4th-grade CS curriculum, which we employ to demonstrate how the framework leads to research questions and methodological decisions, and how it enables the identification of gaps that may divert a curriculum from its goals. The framework offers a structured yet flexible approach for investigating specific curricula, facilitates cross-national comparisons, and supports evidence-based improvements in CS education.

The development of students’ computational thinking (CT) skills is a crucial aspect in today’s world. We used 12 Bebras-based items mapped to combinations of four CT components (abstraction, algorithmic thinking, pattern recognition, decomposition) among 848 Estonian lower-secondary students. Overall, in this sample, girls outperformed boys, and ninth graders were overrepresented among High Achievers. However, these grade-level and gender differences should be interpreted cautiously because the ninth graders’ subgroup was comparatively small and gender-imbalanced. The k-means analysis identified distinct patterns of performance across CT tasks. Because each task involved multiple CT components, the resulting clusters were interpreted as learner profiles rather than as indicators of specific CT subskills. Cluster membership was significantly associated with two single-item self-report indicators of IT-related intentions: High Achievers reported the greatest likelihood of IT studies/careers. Findings suggest CT competence (especially mastery across interdependent subskills) co-occurs with, but does not causally predict, higher self-reported intentions to pursue IT studies and careers.

Artificial Intelligence (AI) systems are now widely applied across different domains. As AI systems increasingly shape professional and academic domains, there is a growing need to pre-pare future professionals to work with these technologies responsibly and effectively. Accordingly, an increasing number of studies advocate AI education for undergraduates across disciplines. However, there is limited qualitative research on these students’ motivations for learning about AI and on what they are interested in learning, despite the importance of such understanding for designing motivating, engaging, and effective learning experiences. Therefore, the objective of this research is to answer the following two research questions: (1) What motivates undergraduates across disciplines to learn about AI? (2) What are undergraduates across disciplines interested in learning about AI? To answer the research questions, we conducted a survey with 128 students who voluntarily enrolled in an elective introductory AI course over three semesters. The findings indicate that these students are motivated to learn about AI not only for career-related utility but also due to its perceived societal relevance and ethical implications. Regarding learning interests, students most frequently expressed interest in learning how AI systems work, how these systems impact society positively and negatively, and how to use them effectively and responsibly. In the discussion section, we provide guidance on how these results can inform the design or adaptation of AI curricula, course invitations, and learning experiences to foster sustained engagement and meaningful learning among these students.

While the urgency of AI literacy has been broadly acknowledged, little empirical research has examined how AI education supports the internalization of AI concepts. Whereas much AI education research reports short-term learning gains, this study investigates how students use AI concepts to mediate collaborative reflection and ethical reasoning after participation in a three-year co-design based cumulative AI education program. Drawing on sociocultural and cultural-historical activity theory, we qualitatively analyzed group interviews with 40 K–12 students in grades 6 and 9. The results show that students fluently used AI concepts as mediating tools. Instead of commonsense explanations and folk theories, students relied on scientific explanations and used AI concepts to explain classifier behavior, social media mechanisms, user engagement, and filter bubbles. These concepts and theoretical orientation to everyday data-driven systems extended to ethical reasoning and critical analysis of practices, structures, and problems in everyday life both at personal and societal levels. The study highlights the potential of cumulative AI education to foster transformative learning and agency, extending AI literacy beyond functional understanding toward volition and abilities to question existing practices, envision alternatives, and take informed action.

Generative artificial intelligence (GenAI) is increasingly used in school informatics, yet technically correct assistance can still interfere with the reasoning through which learners develop algorithmic competence. This conceptual paper focuses on upper-secondary algorithmisation, defined here as the staged design and representation of algorithms from problem interpretation and decomposition through pseudocode or flowcharts to debugging and verification. It proposes pedagogical noise as an integrative, taskspecific diagnostic lens for identifying misalignment between an AI contribution and the competence-forming work that should remain learner-owned at a particular stage. A structured and traceable conceptual synthesis compares this lens with cognitive offloading, productive struggle, scaffolding failure, automation bias, performance-learning dissociation, feedback overload, reduced epistemic agency, overreliance, hallucination, and academic misuse. The synthesis develops seven proposed forms of pedagogical noise, each tied to a primary diagnostic dimension: completeness, timing, transparency, learner judgement, causal debugging, volume/actionability, and ownership/accountability. Worked cases show how technically correct GenAI support may function as scaffolding or substitution depending on learner state, timing, output granularity, transparency, and agency. The contribution is not a new theory of learning or a claim that GenAI is inherently harmful; it is a bounded diagnostic framework and a set of design propositions for preserving learnerowned reasoning in upper-secondary algorithmisation.

Persistent software quality problems continue to occur despite advances in software engineering methods, tools, and standards. This paper proposes a conceptual explanation suggesting that such problems may partly reflect how software engineers are educated to reason about qualityrelevant decisions under uncertainty. The study introduces decision-oriented product thinking as an engineering competence concerned with trade-offs, prioritization, value-sensitive reasoning, and long-term quality consequences. Drawing on software engineering education research, industry skills-gap studies, and state-of-practice investigations in requirements engineering, the paper develops a conceptual model linking educational framing, decision visibility, product-thinking practices, and quality-oriented reasoning. The analysis identifies an opportunity to integrate existing forms of decision-oriented learning more systematically across software engineering education. Although contemporary curricula and educational practices already address elements of decision reasoning, quality-relevant choices, assumptions, alternatives, and trade-offs are not necessarily made consistently visible as objects of instruction, reflection, and assessment. The paper proposes decision visibility as an integrative educational mechanism for connecting these existing elements with product-thinking practices and quality-oriented reasoning. The contribution of the study is conceptual and explanatory. In addition to proposing a conceptual model, the paper introduces educational design principles and classroom-oriented applications intended to make quality-relevant decisions more visible during software engineering education.