Children regularly engage with online information access systems, yet much of our understanding of how they search has not been revisited in recent years. Further, the reasons children avoid certain search practices remain largely unexamined. To address these gaps, we introduce a tangible cardĝ€'based activity designed to elicit and make visible children's reasoning during search. Building on prior literature, we examine whether previously documented practices remain relevant for the current generation of young searchers. We, then, investigate children's explanations for avoiding particular practices, revealing insights into their understanding of the information-seeking process. Overall, this work contributes to a better understanding of children's decisionĝ€'making processes when conducting online searches in school contexts, shedding light on both rationally adopted and discarded online search practices in the classroom setting.

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.

Query performance prediction (QPP) methods have primarily been tailored to mainstream users, thus relying on the traditional concept of relevance. In the case of children, however, relevance goes beyond content-based resource-query matching, which is why we gauge the performance of existing QPP methods in estimating the fit of resources retrieved in response to child-formulated queries. Outcomes from our empirical exploration of various QPP methods using a traditional and a child-focused definition of relevance on 2 datasets reveal the limitations in the adaptability of existing methods to the context of child information retrieval.

User models, which represent the needs of users an online information access system is meant to serve, can facilitate the design and evaluation of the system. However, these models typically rely on assumptions drawn from adult users, such as advanced literacy levels or the ability to evaluate retrieved content critically. Hence, they often overlook the distinct needs and search habits of children, despite the growing prevalence of information access systems among youngsters. In this work, we introduce a set of child anti-personas, user models representing classes of users for whom the system is not designed. To substantiate these mismatches, we investigate how primary school children's behaviors when interacting with different search tools diverge from the assumptions of typical user models. Thereafter, we identify recurring patterns of misalignment between children's behavior and system expectations and link these patterns to the risks identified by adult stakeholders involved in children's online searching. By representing children as anti-personas of contemporary search tools, this work contributes to the quest for more informed and less stereotypical user modeling when it comes to information access for underrepresented groups, such as children.

The concept of relevance in Information Retrieval (IR) has been extensively studied. However, most mainstream IR models have been developed with adult users in mind, assuming cognitive maturity and autonomous interaction. Younger searchers, who increasingly integrate IR systems into their information-seeking practices, differ in cognitive abilities, information needs, and limited digital knowledge, which shape how they judge relevance, often diverging from traditional definitions assumed to work for adults. This calls for a deeper understanding of how this underrepresented group judges online content. In this study, we explore how children interpret and determine relevance when searching for information online in primary school classrooms. As information-seeking in this context is often guided by teachers, we also probe their criteria for relevance. By comparing both perspectives, we uncover points of alignment and divergence. These findings contribute to revisiting the concept of relevance for the primary school context and, more broadly, to the design and evaluation of equitable, context-aware IR systems that support responsible and inclusive information seeking practices.

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.