Evaluating AI-Based Learning Platform to Increase Cybersecurity Awareness: Case Study of Indonesian Students
Bibliographic record
Abstract
In the digital age, cybersecurity threats to educational institutions are increasing.The increasing number of academics and students falling victim to phishing attacks highlights the severity of the issue.This research investigates the impact of Artificial Intelligence (AI)-based learning platforms on cybersecurity awareness, revealing a focus on threat detection rather than education.Some constructs from Protection Motivation Theory (PMT), Self-Determination Theory (SDT), and Technology Acceptance Model (TAM) were examined to understand the factors that influence intention to use and cybersecurity awareness.This study involved 212 students at a private university in Indonesia to learn cybersecurity topics and take an assessment on an AI-based learning platform.The students then filled out questionnaires to measure their perceptions of those factors.The results showed that perceived severity (PS) and intention to use significantly impact cybersecurity awareness, with intention to use playing a key role in enhancing awareness.Perceived autonomy and usefulness also significantly influence students' intention to use.Furthermore, AI demonstrably increases students' cybersecurity awareness.These findings support AI-based cybersecurity education through personalized, interactive tools and provide valuable insights for improving awareness programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".