Segment security-training content by learner age group to maximize personal relevance¶

Strategy · All strategies
Evidence · no claims cited

Description¶

Rather than a one-size-fits-all approach, this strategy tailors scam scenarios to each demographic's everyday digital life: simple game-based interactions for children, dating-app and AI-generated-media scenarios for teens and college students, and coupon, shopping-deal, and lottery scenarios for learners over 50. The article concludes that "the feedback strongly supports a shift toward a segmented model, where age-relevant scenarios... ensure that the training maximizes personal relevance and real-world applicability," including a proposed Enter Age feature at login.

Design Implications¶

Context¶

Requirements¶

• A demographic intake mechanism such as the proposed Enter Age feature at login to filter and deliver age-appropriate content
• A library of scenario content matched to each age group's actual exposure

Constraints¶

• The strategy derives from participant brainstorming feedback in the qualitative analysis, not from a tested comparison of segmented versus unsegmented training

Target Learners¶

• children ages 4-10
• teens and college students
• adults over 50

Target Learning Goals¶

• recognizing scams relevant to one's own daily digital life
• verification habits such as checking senders and URLs

Related Strategies¶

• Targeted and Specific Communication with Families
• Make dementia education relevant to participants' role and experience and involve active face-to-face participation

Examples¶

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Key Sources¶

• Bingjun Li, Christopher Buzaid, Weihao Qu. (2025). Security Education in Higher Education through AI-Powered Gamification. Journal of Cybersecurity, Digital Forensics and Jurisprudence. https://doi.org/10.65879/3070-5789.2025.01.07