Artificial intelligence and the redefinition of the DIKW pyramid: Challenges of the postdigital age

Abstract

In an era where artificial intelligence generates texts, recommends decisions, shapes markets, and actively participates in the construction of social reality, the classical DIKW hierarchy (Data-Information-Knowledge-Wisdom) faces significant challenges. Never in human history has more data and information been produced, yet at the same time it has never been more difficult to distinguish knowledge from the illusion of knowledge, truth from persuasiveness, and wisdom from statistical prediction. This paper examines the relationship between information and knowledge in the context of generative artificial intelligence, with a particular focus on the phenomenon of the "simulation of knowledge" in a postdigital society. The research aims to investigate whether trust in artificial intelligence is related to the ability to distinguish accurate from inaccurate information, as well as whether discrepancies exist between perceived and actual knowledge. The survey for this paper was conducted using a questionnaire on a sample of 97 respondents, combining elements of validated instruments such as the Misinformation Susceptibility Test (MIST) and self-assessment measures. The results indicate that respondents demonstrate a relatively good ability to recognize accurate and inaccurate information; however, no statistically significant relationship was found between self-assessed competence and actual performance. This finding suggests the presence of a potential "illusion of knowledge," where individuals overestimate their ability to evaluate information. These findings highlight the importance of critical information literacy in the context of artificial intelligence and suggest that generative AI may contribute to the blurring of boundaries between information and knowledge in postdigital society.