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Angelica Lermann Henestrosa 2025-10-13 14:08:10 +02:00
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\begin{abstract}
Since the emergence of large language models (LLMs) in 2022, generative AI has rapidly expanded into mainstream applications, leading to the integration of Apple Intelligence into customer devices in 2024. This integration into personal technology marks a significant shift, bringing advanced AI capabilities into everyday devices and making them accessible to private individuals.
Since the emergence of large language models (LLMs) in 2022, generative AI has rapidly expanded into mainstream applications, leading, for example, to the integration of Apple Intelligence into customer devices in 2024.
This integration into personal technology marks a significant shift and a further reduction in barriers to use, bringing advanced AI capabilities into everyday devices and making them accessible to private individuals.
Thus, the use of generative AI--consciously or unconsciously--along with interaction through LLM-powered (voice) assistants and engagement with AI-generated content is expected to increase significantly.
However, data that link this usage to psychological variables and track it over time remain scarce.
This longitudinal study comprises the data from an American sample across six waves at two-month intervals between September 2024 and July 2025. It examines user behavior, attitudes, knowledge, and perceptions related to generative AI.
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This dataset allows for future research on psychological and behavioral dynamics of AI use over time, offering insights into user engagement and the individual factors connected to it.
Thus, this data set allows for future research on psychological and behavioral dynamics of AI use over time, offering insights into user engagement and the individual factors connected to it.
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\end{abstract}
\section{Background and Summary}
The introduction of transformer architectures in 2017 marked a major breakthrough in natural language processing (NLP), enabling significant advances in machine learning (ML) and the development of large language models (LLMs). These models, trained on vast corpora of text data, have demonstrated unprecedented capabilities in generating coherent and contextually relevant language. A milestone in public engagement with generative AI (GenAI) was the release of ChatGPT in November 2022, which made LLMs widely accessible to non-expert users.
Since then, millions of individuals have interacted with conversational agents and other GenAI tools, often on a regular basis, integrating them into everyday tasks such as writing, coding, learning, and decision-making (LIT).
This widespread proliferation of AI technologies, coupled with their increasingly diverse applications and personalized user experiences, raises the questions on how psychological factors shape and might explain differences in AI adoption and usage.
As AI systems become more adaptive and embedded in everyday life, understanding the determinants of usage intensity, behavioral patterns, and types of use becomes essential.
Moreover, the field of AI is evolving at a fast pace, and user characteristics such as attitudes and trust are subject to change over time. Therefore, longitudinal research that captures temporal fluctuations in user traits and behaviors is crucial.
% lets all reflect on which term and why we want to use, and how we define it: usage vs. use
% Taken from LEK application
This project is a joint project from the human-computer interaction group at
the Leibniz-Institut für Wissensmedien in Tübingen (IWM). There are several preregistrations from group members focusing on their
subquestions. However, the overall aim is to examine how people change their
use, perception, and attitudes towards AI-tools like ChatGPT, Siri, or Alexa.
the Leibniz-Institut für Wissensmedien in Tübingen (IWM). There are several preregistrations from group members focusing on their individual
subquestions. However, the overall aim is to examine how people's
use, perception, and attitudes towards AI-tools like ChatGPT, Siri, or Alexa change over time.
This study targets an US-American sample due to Apple announcing to release its new AI platform Apple Intelligence in autumn 2024 (in the US due to the stricter regulations in the EU) and we expect many people to be exposed to this AI on their Apple devices. Data collection started at the end of August 2024?? (six waves, roughly one year). By using a longitudinal design we were able to track changes over time and to get some hints on causality. Longitudinal studies are more likely to find changes if there is a potential change trigger (Zhao et al., 2024)