Mapping Hungarian secondary school students’ digital and AI literacy with a focus on language learning
Bibliographic record
Abstract
As digital technologies and artificial intelligence (AI) increasingly shape education, students must acquire the competencies necessary to navigate these tools critically and ethically. Although digital literacy has been extensively explored in higher education contexts, research on secondary school students remains limited. This study addresses this gap by mapping Hungarian secondary school students’ digital and AI literacy using the Digital Intelligence Framework (DQ Institute, 2019) and the Quebec Digital Competency Framework (Conseil supérieur de l’éducation, 2019). An online questionnaire (N = 130) assessed six competency dimensions, including ethical AI use, critical evaluation, and communication. Quantitative data analysis was conducted using SPSS, through descriptive statistics, ANOVA, correlation, and regression. Findings reveal significant differences in AI ethics and responsibility by school type and in communication-related skills based on self-assessed English proficiency, with lifelong learning predicting AI confidence. The findings highlight the need for context-sensitive, ethical, and skill-integrated AI literacy education at the secondary level. By aligning with internationally recognized frameworks, the study informs policy and practice, promoting equitable, future-ready skill development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".