Improving Phishing Resilience with AI-Generated Training: Evidence on Prompting, Personalization, and Duration

TL;DR

This study validates that AI-generated phishing training using large language models is effective, scalable, and can be personalized with minimal effort, showing significant learning gains in controlled experiments.

Contribution

It provides empirical evidence that simple prompting strategies with LLMs are sufficient for effective phishing resilience training, reducing the need for complex personalization.

Findings

AI-generated training yields significant learning gains.

Simple prompts are as effective as complex personalization.

Longer training duration modestly improves accuracy.

Abstract

Phishing remains a persistent cybersecurity threat; however, developing scalable and effective user training is labor-intensive and challenging to maintain. Generative Artificial Intelligence offers an interesting opportunity, but empirical evidence on its instructional efficacy remains scarce. This paper provides an experimental validation of Large Language Models (LLMs) as autonomous engines for generating phishing resilience training. Across two controlled studies (N=480), we demonstrate that AI-generated content yields significant pre-post learning gains regardless of the specific prompting strategy employed. Study 1 (N=80) compares four prompting techniques, finding that even a straightforward "direct-profile" strategy--simply embedding user traits into the prompt--produces effective training material. Study 2 (N=400) investigates the scalability of this approach by testing personalization and training duration. Results show that complex psychometric personalization offers no measurable advantage over well-designed generic content, while longer training duration provides a modest boost in accuracy. These findings suggest that organizations can leverage LLMs to generate high-quality, effective training at scale without the need for complex user profiling, relying instead on the inherent capabilities of the model.