As children increasingly consume media on devices, parents look for ways this usage can support learning and growth, especially in domains like social-emotional learning. We introduce eaSEL, a system that (a) integrates social-emotional learning (SEL) curricula into children's video consumption by generating reflection activities and (b) facilitates parent-child discussions around digital media without requiring co-consumption of videos. We present a technical evaluation of our system's ability to detect social-emotional moments within a transcript and to generate high-quality SEL-based activities for both children and parents. Through a user study with N=20 parent-child dyads, we find that after completing an eaSEL activity, children reflect more on the emotional content of videos. Furthermore, parents find that the tool promotes meaningful active engagement and could scaffold deeper conversations around content. Our work paves directions in how AI can support children's social-emotional reflection of media and family connections in the digital age.

Much of early literacy education happens at home with caretakers reading books to young children. Prior research demonstrates how having dialogue with children during co-reading can develop critical reading readiness skills, but most adult readers are unsure if and how to lead effective conversations. We present ContextQ, a tablet-based reading application to unobtrusively present auto-generated dialogic questions to caretakers to support this dialogic reading practice. An ablation study demonstrates how our method of encoding educator expertise into the question generation pipeline can produce high-quality output; and through a user study with 12 parent-child dyads (child age: 4–6), we demonstrate that this system can serve as a guide for parents in leading contextually meaningful dialogue, leading to significantly more conversational turns from both the parent and the child and deeper conversations with connections to the child’s everyday life.

Computational thinking (CT) education reaches only a fraction of young children, in part because CT learning tools often require expensive hardware or fluent literacy. Block-based programming environments address these challenges through symbolic graphical interfaces, but users often need instructor support to advance. Alternatively, voice-based tools provide direct instruction on CT concepts but can present memory and navigation challenges to users. In this work, we present Visual StoryCoder, a multimodal tablet application that combines the strengths of each of these approaches to overcome their respective weaknesses. Visual StoryCoder introduces children ages 5–8 to CT through creative storytelling, offers direct instruction via a pedagogical voice agent, and eases use through a block-like graphical interface. In a between-subjects evaluation comparing Visual StoryCoder to a leading block-based programming app for this age group (N = 24), we show that Visual StoryCoder is more understandable to independent learners, leads to higher-quality code after app familiarization, and encourages personally meaningful projects.

Typical educational robotics approaches rely on imperative programming for robot navigation. However, with the increasing presence of AI in everyday life, these approaches miss an opportunity to introduce machine learning (ML) techniques grounded in an authentic and engaging learning context. Furthermore, the needs for costly specialized equipment and ample physical space are barriers that limit access to robotics experiences for all learners. We propose ARtonomous, a relatively low-cost, virtual alternative to physical, programming-only robotics kits. With ARtonomous, students employ reinforcement learning (RL) alongside code to train and customize virtual autonomous robotic vehicles. Through a study evaluating ARtonomous, we found that middle-school students developed an understanding of RL, reported high levels of engagement, and demonstrated curiosity for learning more about ML. This research demonstrates the feasibility of an approach like ARtonomous for 1) eliminating barriers to robotics education and 2) promoting student learning and interest in RL and ML.

Computational thinking (CT) education reaches only a fraction of young children, in part because CT learning tools often require expensive hardware or fluent literacy. Informed by needfinding interviews, we developed a voice-guided smartphone application leveraging storytelling as a creative activity by which to teach CT concepts to 5- to 8-year-old children. The app includes two storytelling games where users create and listen to stories as well as four CT games where users then modify those stories to learn about sequences, loops, events, and variables. We improved upon the app design through wizard-of-oz testing (N = 28) and iterative design testing (N = 22) before conducting an evaluation study (N = 22). Children were successfully able to navigate the app, effectively learn about the target computing concepts, and, after using the app, children demonstrated above-chance performance on a near transfer CT concept recognition task.

The Giggle Gauge offers a quick and simple way for researchers to evaluate the engagement of systems designed for children. This self-report metric is based on prior work delineating the components of engagement and was designed to address the limitations of children's cognitive development (e.g., by focusing on simple language and rapid administration). Through a process of iterative design (N = 23, ages 4 -- 10) and co-design (N = 8, ages 7 -- 11), we refined the items of this metric to ensure children's comprehension. A validation study with 26 children, ages 4 -- 7, confirmed the validity and reliability of the Giggle Gauge through the assessment of three properties: known-groups validity, criterion validity, and test-retest reliability. We simultaneously developed a bifurcated response type, intended to reduce the cognitive load of traditional ordinal response, and show through participant quotes that it may decrease the cognitive load of self-report questions for children.

A key challenge in education is effectively engaging children in learning activities. We investigated how a narrative story impacts engagement and learning, as well as how feedback can provide further benefits. To do so, we created an interactive, tablet-based learning platform with a multi-step math task designed using Common Core State Standards. Subjects completed a pretest and then were assigned to a condition, either one of three variations of the system (narratives, narratives with hints, and narratives with a tutoring chatbot using wizard-of-oz techniques) or a control system that has children complete the same learning task without narratives nor feedback, before the subjects completed a post test. 72 children in U.S. grades 3--5 participated. Our results showed that embedding learning activities into narratives boosted children's engagement as evaluated by coding video responses and surveys, and the integration of a tutoring chatbot improved learning outcomes on the assessment. These results provide evidence that a narrative-based tutoring system with chatbot-mediated help may support effective learning experiences for children.