Search for: All records

As artificial intelligence (AI) becomes more prominent in children’s lives, an increasing number of researchers and practitioners have underscored the importance of integrating AI as learning content in K-12. Despite the recent efforts in developing AI curricula and guiding frameworks in AI education, the educational opportunities often do not provide equally engaging and inclusive learning experiences for all learners. To promote equality and equity in society and increase competitiveness in the AI workforce, it is essential to broaden participation in AI education. However, a framework that guides teachers and learning designers in designing inclusive learning opportunities tailored for AI education is lacking. Universal Design for Learning (UDL) provides guidelines for making learning more inclusive across disciplines. Based on the principles of UDL, this paper proposes a framework to guide the design of inclusive AI learning. We conducted a systematic literature review to identify AI learning design-related frameworks and synthesized them into our proposed framework, which includes the core component of AI learning content (i.e., five big ideas), anchored by the three UDL principles (the “why,” “what,” and “how” of learning), and six praxes with pedagogical examples of AI instruction. Alongside this, we present an illustrative example of the application of our proposed framework in the context of a middle school AI summer camp. We hope this paper will guide researchers and practitioners in designing more inclusive AI learning experiences. 

As conversational AI apps such as Siri and Alexa become ubiquitous among children, the CS education community has begun leveraging this popularity as a potential opportunity to attract young learners to AI, CS, and STEM learning. However, teaching conversational AI to K-12 learners remains challenging and unexplored due in part to the abstract and complex nature of some conversational AI concepts, such as intents and training phrases. One promising approach to teaching complex topics in engaging ways is through unplugged activities, which have been shown to be highly effective in fostering CS conceptual understanding without using computers. Research efforts are underway toward developing unplugged activities for teaching AI, but few thus far have focused on conversational AI. This experience report describes the design and iterative refinement of a series of novel unplugged activities for a conversational AI summer camp for middle school learners. We discuss learner responses and lessons learned through our implementation of these unplugged activities. Our hope is that these insights support CS education researchers in making conversational AI learning more engaging and accessible to all learners. 

Summer camps have become popular for introducing K-12 learners to computer science (CS) and artificial intelligence (AI) in informal learning environments. Facilitators play crucial roles in guiding and engaging learners in these contexts, but there is limited research on their roles in informal AI learning. This paper examines facilitators’ dialogues with campers in a middle school AI summer camp, identifying eight major facilitator roles. The roles differed depending on group dynamics and project phase. The paper provides empirical grounding to define facilitators’ roles in AI learning and guide the design of professional development for camp facilitators. 

Conversational AIs such as Alexa and ChatGPT are increasingly ubiquitous in young people’s lives, but these young users are often not afforded the opportunity to learn about the inner workings of these technologies. One of the most powerful ways to foster this learning is to empower youth to create AI that is personally and socially meaningful to them. We have built a novel development environment, AMBY–‘‘AI Made By You’’–for youth to create conversational agents. AMBY was iteratively designed with and for youth aged 12–13 through contextual inquiry and usability studies. AMBY is designed to foster AI learning with features that enable users to generate training datasets and visualize conversational flow. We report on results from a two-week summer camp deployment, and contribute design implications for conversational AI authoring tools that empower AI learning for youth.