Although recent years have seen an emergence of tools for automated identification of deceptive design patterns on websites, their scope and reliability remain understudied. Institutional websites are a particularly interesting research domain. They have an extensive information structure and shape the conditions of user interaction. The purpose of the article is to empirically evaluate signs of deceptive design patterns on Polish universities’ websites and analyse how they are identified using automated analytical tools. The study covers all public universities in Poland (N = 65). The analysis involved automated tools representing different methodological underpinnings, including web browser extensions and GPT language model-based analytical procedures. The study pinpoints significant differences in the paradigms behind the results provided by the two methods.
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Automated Detection of Deceptive Design Patterns on University Websites: A Comparative Analysis of Browser-Based Tools and LLM-Based Approaches
The findings indicate that the current capabilities of automated tools offer merely fragmented detection of selected deceptive design patterns, instead of a complete systemic diagnosis of the problem.