Agents based on Large Language Models (LLM) have introduced a new way of information seeking that could simplify the search process to suit children’s cognitive skills, as these agents often respond to natural language inquiries with easy-to-read and plausible answers. Still, with emotions playing a crucial role in children’s information seeking and consumption behaviours, it is important to consider whether these agents suit children’s emotional intelligence. With that in mind, in this work, we examine the emotional undertones of LLM agent responses for children’s inquiries. Considering the known impact of prompt engineering on an agent’s response, we investigate whether explicitly informing an agent that the user is a child influences the emotions conveyed in its response. Outcomes from this empirical study reveal the limitations of LLM agents to fit children’s emotional intelligence, with agents tending to over-amplify any underlying emotion in a child’s inquiry. With our findings, we advance knowledge in the role of emotions in children’s online search and offer insights that could be used to improve children’s online information access.

The rise of digital platforms for accessing online content-from popular search engines to social media sites- has contributed to the (un)intentional propagation of misleading information. This phenomenon, known as Information Disorder, affects individuals and society. Extensive research has been conducted to study and address Information Disorder as it pertains to the general population. Yet, little is known about how children, who have specific needs and behaviours when interacting with digital content, deal with misleading information and how the algorithms, that underlay the information access tools they use, mitigate or exacerbate the issue. Through a systematic literature review, we present research efforts that address or discuss the impact of Information Disorder on children and their overall information-seeking experience. We analyse the literature from various perspectives, including children's behaviour across platforms and the solutions developed to mitigate misleading information. Inspired by the knowledge distilled and gaps identified in our review, we discuss research directions that tackle both technological and human-centred challenges children face when dealing with misleading information, seeking to establish a foundation to mitigate the effects of Information Disorder among children.

In an existing study, the InsideOut Framework is used to produce and explore the emotional profiles of search engines (SE) in response to queries formulated by children aged 9 to 11 in the classroom context, revealing the emotional diversity of SE responses. Since then, there have been significant technological advances in emotion detection and information access. In this work, we conduct a comprehensive reproducibility study where we probe today's emotional profile of SE using both a lexicon-based and a language-model based approach tailored to the Italian language, thus addressing an acknowledged limitation of the original study. Additionally, considering the prevalence of agents based on Large Language Models (LLM) as information access systems among children, we extend the analysis to capture the emotional undertones of LLM responses and juxtapose them to those of SE. Our findings emphasize the importance of leveraging the appropriate emotion detection technique to produce and explore emotional profiles and lead us to reflect on the interplay of emotions on children's search-as-learning experience.