As generative AI becomes increasingly integrated into children's creative lives, designing responsible and meaningful tools for Child-AI co-creation is a growing concern in HCI and education.This paper presents a scoping review of 20 studies on Child-AI co-creation, analyzing the types of creative activities involved, the age groups studied, and the roles AI systems play.Based on this synthesis, we identify six key design considerations: protecting child data privacy, minimizing bias and hallucinations, fostering appropriate reliance on AI, balancing support and creative freedom, encouraging peer and family collaboration, and making AI's creative process understandable.This work contributes an initial framework to guide the design of child-centered and developmentally appropriate AI co-creation systems, and highlights directions for future research.

AI recommendations influence our daily decisions. The convenience of navigating personalized content goes hand-in-hand with the notorious filter bubble effect, which may decrease people’s exposure to diverse options and opinions. Children are especially vulnerable to this due to their limited AI literacy and critical thinking skills. In this study, we propose a novel Augmented Reality (AR) application BeeTrap. It aims to not only raise children’s awareness of filter bubbles but also empower them to mitigate this ethical issue through sense-making of AI recommendation systems’ inner workings. By having children experience and break filter bubbles in a flower recommendation system, BeeTrap utilizes embodied metaphors (e.g., NEAR-FAR, ITERATION) and analogies (bee pollination) to bridge abstract AI concepts with sensory-motor experiences in familiar STEM contexts. To evaluate our design’s effectiveness and accessibility for a broad range of children, we introduced BeeTrap in a four-day summer camp for middle-school students from underrepresented backgrounds in STEM. Results from pre- and post-tests and interviews show that BeeTrap developed students’ technical understanding of AI recommendations, empowered them to break filter bubbles, and helped them foster new personal and societal perspectives around AI technologies.

The viral launch of new generative AI (GAI) systems, such as ChatGPT and Text-to-Image (TTL) generators, sparked questions about how they can be effectively incorporated into writing education. However, it is still unclear how teachers, parents, and students perceive and suspect GAI systems in elementary school settings. We conducted a workshop with twelve families (parent-child dyads) with children ages 8-12 and interviewed sixteen teachers in order to understand each stakeholder’s perspectives and opinions on GAI systems for learning and teaching writing. We found that the GAI systems could be beneficial in generating adaptable teaching materials for teachers, enhancing ideation, and providing students with personalized, timely feedback. However, there are concerns over authorship, students’ agency in learning, and uncertainty concerning bias and misinformation. In this article, we discuss design strategies to mitigate these constraints by implementing an adults-oversight system, balancing AI-role allocation, and facilitating customization to enhance students’ agency over writing projects.

Learning Experience Designers (LXDs) play an increasingly consequential role in the creation of courses and training materials that meet the needs of diverse learner populations and the growing class scope. Emerging design requests for scalable and effective courseware introduce new challenges in Learning Experience (LX) design practice while providing an opportunity for researchers to understand LX workflows and design new tools to improve them. This paper presents an interview study with 21 LXDs from 18 different organizations with the goal of understanding LXDs' collaborative relationships with subject matter experts (SMEs), data needs, and contextual challenges. We further perform a survey study to validate the challenges and probe into LXDs' attitudes toward a suite of data-driven solutions. We find that LXDs demonstrate a strong desire to collect data to inform their design - including target learners' prior knowledge and relevant design precedents. LXDs want support in better collaborating with SMEs, acquiring and processing diverse learner data, identifying relevant research studies to communicate their design decisions, understanding domain-specific material, and creating quality materials (especially questions). We discuss LXDs' concerns regarding automated solutions such as the lack of contextual understanding, over-reliance on automation, and data privacy before elaborating on the implications for future work.

AI curricula are being developed and tested in classrooms, but wider adoption is premised by teacher professional development and buy-in. When engaging in professional development, curricula are treated as set in stone, static and educators are prepared to offer the curriculum as written instead of empowered to be leaders in efforts to spread and sustain AI education. This limits the degree to which teachers tailor new curricula to student needs and interests, ultimately distancing students from new and potentially relevant content. This paper describes an AI Educator Make-a-Thon, a two-day gathering of 34 educators from across the United States that centered co-design of AI literacy materials as the culminating experience of a year-long professional development program called Everyday AI (EdAI) in which educators studied and practiced implementing an innovative curriculum for Developing AI Literacy (DAILy) in their classrooms. Inspired by the energizing and empowering experiences of Hack-a-Thons, the Make-a-Thon was designed to increase the depth and longevity of the educators' investment in AI education by positively impacting their sense of belonging to the AI community, AI content knowledge, and their self confidence as AI curriculum designers. In this paper we describe the Make-a-Thon design, findings, and recommendations for future educator-centered Make-a-Thons.

COVID ushered in the widespread use of videoconferencing and it's here to stay. In virtual communication, we can alter everything from our appearance, voice and backgrounds. Most of these changes are fun gimmicks, but what if we could leverage these filtering technologies to a life-changing assistive technology? We propose the idea of developing assistive video filters for people with Parkinson's disease (PwP) that will remove involuntary tremors and smooth the stuttering in their voice. We surveyed 177 PwP and 107 people from the general public, and we personally interviewed 52 PwP as well as 3 health care professionals. We find overwhelming statistical evidence that these filters would fulfill a demonstrated communication need for PwP and that the general public also approves of a video filter that could assist with communication for PwP. To test the feasibility of our concept, we developed a filter prototype to remove physical tremors and tested it on two PwP. Although this paper focuses on PwP as a use case, we hope this work encourages others to ethically develop filtering technologies to help individuals with other movement disorders, eye-contact impairment and stuttering in computer-mediated conversations.

This paper describes an AI Book Club as an innovative 20-hour professional development (PD) model designed to prepare teachers with AI content knowledge and an understanding of the ethical issues posed by bias in AI that are foundational to developing AI-literate citizens. The design of the intervention was motivated by a desire to manage the cognitive load of AI learning by spreading the PD program over several weeks and a desire to form and maintain a community of teachers interested in AI education during the COVID-19 pandemic. Each week participants spent an hour independently reading selections from an AI book, reviewing AI activities, and viewing videos of other educators teaching the activities, then met online for 1 hour to discuss the materials and brainstorm how they might adapt the materials for their classrooms. The participants in the AI Book Club were 37 middle school educators from 3 US school districts and 5 youth-serving organizations. The teachers are from STEM disciplines as well as Social Studies and Art. Eighty-nine percent were from underrepresented groups in STEM and CS. In this paper we describe the design of the AI Book Club, its implementation, and preliminary findings on teachers' impressions of the AI Book Club as a form of PD, thoughts about teaching AI in classrooms, and interest in continuing the book club model in the upcoming year. We conclude with recommendations for others interested in implementing a book club PD format for AI learning.