Children often interact with search engines within a classroom context to complete assignments or discover new information. To successfully identify relevant resources among those presented on a search engine results page (SERP), users must first be able to comprehend the text included in SERP snippets. While this task may be straightforward for an adult user, children may encounter obstacles in terms of readability and comprehension when attempting to navigate a SERP. Previous research has demonstrated the positive impact of including visual cues on a SERP as relevance signals to guide children toward appropriate resources. In this work, we explore the effect of supplying visual cues related to readability and text difficulty on children's (ages 6-12) navigation of a SERP. Using quantitative data collected from user-interface interactions and qualitative data gathered from participant interviews, we analyze the impact of these visual cues on children's selection of results on a SERP when carrying out information discovery tasks.

Current approaches in automatic readability assessment have found success with the use of large language models and transformer architectures. These techniques lead to accuracy improvement, but they do not offer the interpretability that is uniquely required by the audience most often employing readability assessment tools: teachers and educators. Recent work that employs more traditional machine learning methods has highlighted the linguistic importance of considering semantic and syntactic characteristics of text in readability assessment by utilizing handcrafted feature sets. Research in Education suggests that, in addition to semantics and syntax, phonetic and orthographic instruction are necessary for children to progress through the stages of reading and spelling development; children must first learn to decode the letters and symbols on a page to recognize words and phonemes and their connection to speech sounds. Here, we incorporate this word-level phonemic decoding process into readability assessment by crafting a phonetically-based feature set for grade-level classification for English. Our resulting feature set shows comparable performance to much larger, semantically- and syntactically-based feature sets, supporting the linguistic value of orthographic and phonetic considerations in readability assessment.

In this work, we discuss the findings emerging from co-design sessions between children ages 6 to 11 and adults, which were conducted to advance knowledge on how to best support children using well-known search tools for online information discovery. Specifically, we argue that by leveraging scaffolding, gamification techniques, and design choices via an application, it is possible to enhance children's habits related to query formulation. Outcomes from this preliminary exploration reveal that gameplay incentives (e.g. levels, points, and other incentives like customization) are needed and effective in motivating further interaction with the application, which in turn leads to further utilization of the scaffolding needed to positively impact query formulation.

As an initial step towards enabling the adaptation of (popular, and widely used) web search environments so that they can better serve children and ease their path towards information discovery, we introduce Recognizing Young Searchers (RYSe). RYSe leverages lexical, syntactical, spelling/punctuation, and vocabulary features that align with the Concrete Operational stage of development (originally identified by Jean Piaget) in an attempt to identify users that are in this stage. The concrete operational stage is commonly associated with children ages 7-11. Findings emerging from our initial empirical exploration using single queries formulated by children and sample queries from adults showcase the feasibility of relying on different cognitive traits inferred from the short text of a single query to distinguish those that are formulated by younger searchers.

In this vision paper, we spotlight children as often underserved users in the digital ecosystem. With online search as a use case, we discuss the need for a multi-perspective approach to designing interactive interfaces and technologies that can enable information access systems to better respond to children's requirements while respecting the cultural and social norms impacting their upbringing.

Children's ability to spell effectively is a major barrier to using search engines successfully. While search engines make use of spellcheckers to provide spelling corrections to their users, they are designed for more traditional users (i.e., adults) and have proven inadequate for children. The specific target of children for this research are those with early literacy skills (whose are typically ages 6–12). The aim of this work is twofold: first, to address the types of spelling errors children make by researching, developing, and evaluating algorithms to generate and rank candidate English spelling suggestions for children; and second, to improve children's user experience when using our proposed spellchecker by involving them in the design process through participatory design and evaluating the impact of interactive elements on children's spellchecking behaviors. The outcomes of our studies and assessments result in a phonetic-based spelling correction model (KidSpell) that can more accurately correction children's spelling errors than existing state-of-the-art models. Further, we learned that visual and audio cues have a positive impact on children's ability to find their intended word from a list of spelling suggestions.

Readability is a core component of information retrieval (IR) tools as the complexity of a resource directly affects its relevance: a resource is only of use if the user can comprehend it. Even so, the link between readability and IR is often overlooked. As a step towards advancing knowledge on the influence of readability on IR, we focus on Web search for children. We explore how traditional formulas–which are simple, efficient, and portable–fare when applied to estimating the readability of Web resources for children written in English. We then present a formula well-suited for readability estimation of child-friendly Web resources. Lastly, we empirically show that readability can sway children’s information access. Outcomes from this work reveal that: (i) for Web resources targeting children, a simple formula suffices as long as it considers contemporary terminology and audience requirements, and (ii) instead of turning to Flesch-Kincaid–a popular formula–the use of the “right” formula can shape Web search tools to best serve children. The work we present herein builds on three pillars: Audience, Application, and Expertise. It serves as a blueprint to place readability estimation methods that best apply to and inform IR applications serving varied audiences.

In this paper, we present BiGBERT, a deep learning model that simultaneously examines URLs and snippets from web resources to determine their alignment with children’s educational standards. Preliminary results inferred from ablation studies and comparison with baselines and state-of-the-art counterparts, reveal that leveraging domain knowledge to learn domain-aligned contextual nuances from limited input data leads to improved identification of educational web resources.

In this paper, we take a step towards understanding how to design search engine results pages (SERP) that encourage children's engagement as they seek for online resources. For this, we conducted a participatory design session to enable us to elicit children's preferences and determine what children (ages 6-12) find lacking in more traditional SERP. We learned that children want more dynamic means of navigating results and additional ways to interact with results via icons. We use these findings to inform the design of a new SERP interface, which we denoted CHIRP. To gauge the type of engagement that a SERP incorporating interactive elements-CHIRP-can foster among children, we conducted a user study at a public school. Analysis of children's interactions with CHIRP, in addition to responses to a post-task survey, reveals that adding additional interaction points results in a SERP interface that children prefer, but one that does not necessarily change engagement levels through clicks or time spent on SERP.

Given the more widespread nature of natural language interfaces, it is increasingly important to understand who are accessing those interfaces, and how those interfaces are being used. In this paper, we explore spellchecking in the context of web search with children as the target audience. In particular, via a systematic literature review of work that illustrate challenges and limitations, we show that, while widely used, popular search tools are ill-designed for children. We then use spellcheckers as a case study to highlight the need for an interdisciplinary approach that brings together natural language processing, education, human-computer interaction to address a known information retrieval problem: query misspelling. We conclude that it is imperative that those for whom the interfaces are designed have a voice in the design process.

Spellchecking functionality embedded in existing search tools can assist children by offering a list of spelling alternatives when a spelling error is detected. Unfortunately, children tend to generally select the first alternative when presented with a list of options, as opposed to the one that matches their intent. In this paper, we describe a study we conducted with 191 children ages 6-12 in order to offer empirical evidence of: (1) their selection habits when identifying spelling suggestions that match the word they meant to type, and (2) the degree of influence multimodal cues, i.e., synthesized speech and images, have in prompting children to select the correct spelling suggestion. The results from our study reveal that multimodal cues, primarily synthesized speech, have a positive impact on the children's ability to identify their intended word from a list of spelling suggestions.

For help with their spelling errors, children often turn to spellcheckers integrated in software applications like word processors and search engines. However, existing spellcheckers are usually tuned to the needs of traditional users (i.e., adults) and generally prove unsatisfactory for children. Motivated by this issue, we introduce KidSpell, an English spellchecker oriented to the spelling needs of children. KidSpell applies (i) an encoding strategy for mapping both misspelled words and spelling suggestions to their phonetic keys and (ii) a selection process that prioritizes candidate spelling suggestions that closely align with the misspelled word based on their respective keys. To assess the effectiveness of KidSpell, we compare the model's performance against several popular, mainstream spellcheckers in a number of offline experiments using existing and novel datasets. The results of these experiments show that KidSpell outperforms existing spellcheckers, as it accurately prioritizes relevant spelling corrections when handling misspellings generated by children in both essay writing and online search tasks. As a byproduct of our study, we create two new datasets comprised of spelling errors generated by children from hand-written essays and web search inquiries, which we make available to the research community.

Misspellings in queries used to initiate online searches is an everyday occurrence. When this happens, users either rely on the search engine's ability to understand their query or they turn to spellcheckers. Spellcheckers are usually based on popular dictionaries or past query logs, leading to spelling suggestions that often better resonate with adult users because that data is more readily available. Based on an educational perspective, previous research reports, and initial analyses of sample search logs, we hypothesize that existing spellcheckers are not suitable for young users who frequently encounter spelling challenges when searching for information online. We present early results of our ongoing research focused on identifying the needs and expectations children have regarding spellcheckers.

Children use popular web search tools, which are generally designed for adult users. Because children have different developmental needs than adults, these tools may not always adequately support their search for information. Moreover, even though search tools offer support to help in query formulation, these too are aimed at adults and may hinder children rather than help them. This calls for the examination of existing technologies in this area, to better understand what remains to be done when it comes to facilitating query-formulation tasks for young users. In this paper, we investigate interaction elements of query formulation-including query suggestion algorithms-for children. The primary goals of our research efforts are to: (i) examine existing plug-ins and interfaces that explicitly aid children's query formulation, (ii) investigate children's interactions with suggestions offered by a general-purpose query suggestion strategy vs. a counterpart designed with children in mind, and (iii) identify, via participatory design sessions, their preferences when it comes to tools / strategies that can help children find information and guide them through the query formulation process. Our analysis shows that existing tools do not meet children's needs and expectations, the outcomes of our work can guide researchers and developers as they implement query formulation strategies for children.