Main finding
Cyber educational app
Overview
Based on
Information security and privacy awareness in the age of AI
The article examines how university administrators and faculty members perceive students' information security and privacy awareness as AI-supported digital technologies expand across higher education.
Frequency distribution
Most mentioned awareness gaps
Each bar shows how many of the 15 interviewed administrators and faculty members mentioned that finding. The labels are the article's qualitative codes, grouped by theme.
Key article insights
What the study says should be improved
These four points summarize the recurring issues and recommendations reported by university administrators and faculty members in the article.
Validated framework
Six themes used by the assessment
These dimensions come from the article's interview protocol and are used as the structure for the scenario-based diagnostic.
Application logic
From article findings to student behavior
Students possess a basic level of information-security and privacy awareness, but this awareness is not consistently reflected in everyday digital behavior.
The article recommends practical awareness activities, case-based learning, and training that translates knowledge into behavior.
The final recommendations point to continuous cybersecurity awareness programmes, visible reporting mechanisms, and stronger institutional policies.
Phish or Legit?
Work through a university inbox. Inspect sender details, links, attachments, urgency, and reply-to fields before choosing a response.
These emails are training scenarios based on the article's themes, not literal emails from the article.
Use the quick actions above or choose from the full response set.
Wi-Fi Detective
Choose a campus network, then decide which activities are safe on that connection. Watch for open networks and evil-twin names that look almost official.
This is a training scenario based on the article's wireless network security and digital privacy themes.
Select a network
A credible name is not enough. Check security, ownership, and whether the network could be an evil twin.
Select every activity you would allow on the chosen network.
Signal strength is not trust
Official secure network; still verify the exact name before signing in.
Use for low-risk browsing; avoid personal data and account-heavy actions.
Convenient names can still mean limited protection.
No password and unknown ownership make it unsuitable for accounts or documents.
A credible name can imitate the official network without belonging to the university.
Unknown network + sensitive activity = stop
Exact network name, security type, and whether the owner is trusted.
Keep banking, faculty login, and personal documents away from open or suspicious networks.
VPN is an extra protective layer, not proof that an unknown network is safe.
USB Triage Desk
Review a removable-media case, identify the trust signals, then choose the first safe protocol. The scenario focuses on secure USB usage and protection of institutional data.
This is a training scenario based on the article's Basic Information Security Practices theme and its code on insufficient USB security awareness.
Colleague's USB stick
Choose the first action before any file is opened on a trusted device.
From object to safe action
Separate known, unknown, found, promotional, and institution-provided devices before opening anything.
Scan first, use an isolated device when trust is low, or hand unknown media to the responsible campus unit.
Do not expose accounts, personal documents, or university data to removable media with unclear trust.
Unknown source + trusted device = do not connect
Rule based on the article's Basic Information Security Practices theme: secure USB usage should protect institutional data before convenience.
Cyber behavior assessment
Answer short scenarios. The score focuses on whether awareness becomes daily secure behavior, which is the core gap identified in the article.
What to train first
Validated PDF sources
The app uses only validated structures from the article extraction and the general conclusion from the abstract.