With the current rise of Large Language Models (LLMs), it also raises concerns that sycophantic responses may influence how users form opinions and trust in such models. This paper investigates how LLM sycophancy affects trust, perceived trustworthiness, and opinion formation among simulated users representing young adults. A 2 x 2 mixed experimental design was conducted in which simulated users between the ages of 18 and 25 interacted with either a neutral or sycophantic model across two topics: autonomous vehicles and AI in society. Users completed pre- and post-interaction questions for each topic. In addition, their open-ended reflection responses were qualitatively analyzed. Both neutral and sycophantic conditions were configured on the model Llama 3.1 8B. The results suggest that the sycophantic model increases perceived trustworthiness, while the effects on trust and opinion formation were insignificant. These results indicate that sycophantic behavior may make models appear more trustworthy even when it does not strongly influence users' opinions or trust. Results from manipulation checks show that there was only a significant difference in perceived validation between both conditions, suggesting that the perceived trustworthiness may have been influenced more by validation than by broader sycophantic behavior. Since the study uses simulated users, the results should be interpreted as exploratory rather than direct evidence of human behavior. The paper contributes an experimental setup for studying LLM sycophancy on simulated users and highlights the need for further validation with real human participants. ... Read more

In recent years, there has been a gradual increase in the use of generative artificial intelligence (AI) among young adults. At the same time, they tend to process textual information while under conditions of divided attention. As a result, young adults may encounter AI-generated misinformation when their cognitive resources are occupied, potentially affecting their ability to evaluate information critically. Previous research has linked external cognitive load (CL) to task performance, but less is known about its impact on the evaluation of AI-generated misinformation. To address this gap, this study used a simulated experiment in which AI personas representing young adults evaluated the veracity of AI-generated true and false statements under no-load, low-load, and high-load conditions, measuring accuracy, confidence, and sharing intention. High CL reduced personas' accuracy and confidence in evaluating veracity, whereas low CL did not differ significantly from the no-load condition. No statistically significant effect of CL was found for sharing intention. As the study is simulation-based, the results should not be interpreted as direct evidence of the behaviour of real young adults. ... Read more

The use of LLMs (Large Language Models) as "thinking partners", conversational partners actively partaking in user's reasoning, is on the rise. As young adults become the demographic that engages with LLMs the most, concerns over whether different AI "thinking partner" styles can help or hinder responsible opinion formation become more prevalent. This study investigates how three "thinking partner" styles, Steelman, Socratic, and Neutral, affect opinion change, epistemic trust, and epistemic autonomy in simulated young adult participants. A between subjects study was conducted, using simulated personas as participants. Each persona engages with a "thinking partner" condition for a five exchange session on the topic of individual versus systemic responsibility for climate action. Opinion change differed significantly across conditions, with the Steelman producing a shift away from individual climate action, while the Socratic and Neutral produced comparable positive shifts towards agreement. No significant change was noted for epistemic trust and autonomy, both of which were rated consistently high regardless of the condition. These findings suggest that an adversarial AI may provoke resistance rather than persuasion, while trust and sense of autonomy is preserved across interaction styles. This study serves as a preliminary methodological pilot, future work should replicate the experiment with human participants. ... Read more

The increasing use of generative AI has had a significant impact on how people experience, interact, and interpret media. The widespread adoption of generative AI has raised concerns regarding the spread of AI-generated misinformation and its influence on the perceived trustworthiness of information. This study investigated how AI-personas representing young adults evaluated AI-generated and human-generated statements. A mixed factorial experimental design was used with three independent variables: statement truthfulness, statement source, and source label visibility. 124 AI-personas completed surveys where they were asked to evaluate short statements based on their truthfulness, confidence and trustworthiness. Mixed ANOVA was conducted to examine the effects of content source, truthfulness and labeling.

The results showed that AI-generated misinformation was not identified less accurately than human-generated misinformation. Source labeling did not significantly affect confidence in truthfulness judgments. Trustworthiness ratings were significantly influenced by both statement condition and label visibility. When source labels were hidden, AI-generated statements received higher trustworthiness ratings than human-generated statements. However, when the source labels were revealed, the trustworthiness ratings for AI-generated content were reduced, while human-made statements received higher trustworthiness scores. These findings suggest that knowledge of content origin influences the perceived trustworthiness. ... Read more

AI-generated content has become very hard to distinguish and it has evolved into a challenge for users to judge whether the media was created by a human or by a machine. This study examines whether AI and media literacy interventions can improve the ability of AI-agent personas, prompted as young adults, to detect AI-generated texts and images. Twenty AI-agent personas completed pre- and post-intervention detection tasks across both modalities. Overall detection accuracy increased from 85.75% before the intervention to 94.25% after the intervention, with a larger improvement for image stimuli compared to text stimuli. Text detection accuracy was already high before the intervention, while image detection still showed room for improvement. The findings suggest that AI and media literacy guidance can produce measurable changes by using specific cues, but they should not be treated as direct evidence of how real young adults would respond. This study contributes an exploratory test of using AI-agent personas to evaluate intervention designs before human-participant research. ... Read more