Who Wants to Be Cybersecure? Expert Evaluation of a Culturally Adaptive Gamified Cybersecurity Awareness App
Bibliographic record
Abstract
Cybersecurity awareness is a pressing issue in Nigeria as the rapid growth of digital technologies has outpaced public knowledge of online safety. Most of the available cybersecurity awareness programs often fail to resonate with local users as they were developed for WEIRD (Western, Educated, Industrialized, Rich, Democratic) contexts, making them less enticing and less efficient for changing people’s behavior. Additionally, most existing solutions are inaccessible to the general public, as they are often delivered through seminars, workshops, and expert-led training, which have limited reach and engagement. This paper describes an evaluation by experts of "Who Wants to Be Cybersecure", a culturally adapted gamified mobile application designed to improve cybersecurity awareness among Nigerians. The app enhances engagement by integrating scenario-based learning, gamification (leaderboards, competition), and cultural elements such as Afrobeats music and Pidgin English. We assess the app’s usability, effectiveness, and cultural relevance through qualitative expert interviews with cybersecurity experts in industry and academia. Furthermore, experts noted that cultural elements would increase credibility and acceptance, leading to better user engagement. Nevertheless, the experts identified some areas for improvement, including more Nigerian-related cybersecurity scenarios (e.g., SIM swap fraud and online banking scams).
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".