Xueqiang (Brandon) Wang

Xueqiang (Brandon) Wang

Office: HS II-232

DPTrek

SaTC 2.0: EDU: Experiential Learning of Dark Patterns for Cybersecurity and Privacy

Summary

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Online users are increasingly using websites and software applications to access important services that support their well-being and quality of life, including their healthcare, learning opportunities, and many products and services. For those with limited digital literacy, this growing use increases their exposure to cyber risks. One of the most common and concerning risks they face is so-called Dark Patterns, that is, user interface designs that trick users into making unintended decisions such as giving their consent to unwanted subscriptions or revealing personal information. Dark Pattern designs can also make it difficult for these users to opt out of data collection. It is therefore crucial for people who are not computer experts to become more aware of and educated about these risks. Unfortunately, current cyber education is generally limited to generic warnings and lacks real-world examples to effectively and safely educate them and fails to keep pace with the rapid evolution of dark patterns, especially as AI and machine learning are increasingly employed.

This project is bridging the critical gap by developing a dark patterns learning platform aimed at raising people’s awareness of dark patterns and improving their literacy in navigating dark patterns in real life. By integrating knowledge and methodologies from dark patterns research, educational science, human-computer interaction, and software engineering, the platform provides real-world experiential components, is especially tailored for older adults and those lacking computer expertise. The investigators of this project are prototyping and tailoring the learning platform through formative evaluation, evaluating the effectiveness of the platform through expert inspections and evaluation, and refining the system by developing an updating framework that enables non-technical educators to make no-code updates. The investigators are also robustly evaluating and broadly disseminating the learning platform to local and nationwide communities, and training researchers in dark pattern education and mitigation.

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