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 card‑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 decision‑making processes when conducting online searches in school contexts, shedding light on both rationally adopted and discarded online search practices in the classroom setting.

The traditional Information Retrieval (IR) evaluation framework—anchored in topical relevance and relevance‑based metrics—reflects a system-centred perspective. Yet for specific user groups, relevance alone is insufficient; benchmarking that relies exclusively on conventional metrics overlooks qualities intrinsic to the users IR approaches are meant to serve. Here, we draw attention to Children IR and examine the value of extending traditional evaluation with a human-centred perspective that accounts for how children interpret and evaluate information to more authentically capture performance and better reflect how well an approach truly meets children’s needs. Our empirical exploration using a child‑focused dataset, multiple ranking strategies, and traditional and extended frameworks reveals not only the limitations of relevance-based assessments but also the advantages of employing frameworks that are tailored to reflect the needs of child users, paving the way for more inclusive and effective evaluation frameworks.

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.