Problem
Research questions and friction points this paper is trying to address.
AI literacy
data literacy
informal learning
secondary education
socio-technical themes
KI-Adventskalender: An Informal Learning Intervention for Data & AI Literacy
🤖 AI Summary
This study addresses the widespread gap among secondary students in understanding key concepts that influence AI system outputs, such as data quality, evaluation metrics, and modeling assumptions. To bridge this gap, the authors designed and deployed a free web-based extracurricular learning platform that innovatively integrates data and AI literacy education into an advent calendar format themed around December holidays. The platform delivers 24 progressively structured micro-challenges to foster sustained engagement in informal learning, supported by interactive tasks, behavioral tracking, and revision analytics. Data from 2025 indicate that over 75% of students who completed the first 12 challenges went on to finish all 24, with higher revision rates positively correlated with completion success, suggesting the design effectively enhances students’ data literacy, sociotechnical awareness, and deep engagement.
📝 Abstract
Secondary school students increasingly encounter AI systems whose outputs depend on data quality, evaluation choices and modeling assumptions. To provide accessible entry points to these interconnected concepts, we developed KI-Adventskalender, a free web-based extracurricular initiative with 24 didactically curated, short, guided micro-challenges released daily in December, targeting data-centric competencies and socio-technical themes that shape how data are interpreted in practice. Drawing on two annual iterations, we report aggregate platform traces characterizing participation and task-level engagement. Participation increased substantially in 2025, but early attrition persists. Progression stabilized after midpoint: among users reaching Day 12 in 2025, more than 75% completed the calendar. Competence cluster performance shifted across years; higher revision rates co-occurred with strong pass rates, suggesting sustained engagement. We use these observations to motivate a next-step measurement agenda: tighter task instrumentation, embedded micro-assessments and mixed-method evaluation designs that can distinguish persistence from conceptual uptake, knowledge progression and durable learning outcomes.
Innovation
Methods, ideas, or system contributions that make the work stand out.
informal learning
AI literacy
micro-challenges
data-centric competencies
mixed-method evaluation
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