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K-12 Computer Science (CS) education has seen remarkable growth recently, driven by the increasing focus on CS and Computational Thinking (CT) integration. Despite the abundance of Professional development (PD) programs designed to prepare future CS teachers with the required knowledge and skills, there is a lack of research on how teachers' perceptions and attitudes of CS and CT evolve before and after participating in these programs. To address this gap, our exploratory study aims to study the dynamics of pre-and in-service teachers' experiences, attitudes, and perceptions towards CS and CT through their participation in a K-12 CS education micro-credential program. In this study, we employed topic modeling to identify topics that emerged from teachers' written pre- and post-CS autobiographies, conducted statistical analysis to explore how these topics evolve over time and applied regression analysis to investigate the factors influencing these dynamics. We observed a shift in teachers' initial feelings of fear, intimidation, and stress towards confidence, fun, and feeling competent in basic CS, reflecting a positive transformation. Regression analysis revealed that features, such as experienced teacher status and CT conceptual understanding, correlate with participants' evolving views. These observed relationships highlight the micro-credential's role in not only enhancing technical competency but also fostering an adaptive, integrative pedagogical mindset, providing new insights for course design. 

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. 

Intelligent systems to support collaborative learning rely on real-time behavioral data, including language, audio, and video. However, noisy data, such as word errors in speech recognition, audio static or background noise, and facial mistracking in video, often limit the utility of multimodal data. It is an open question of how we can build reliable multimodal models in the face of substantial data noise. In this paper, we investigate the impact of data noise on the recognition of confusion and conflict moments during collaborative programming sessions by 25 dyads of elementary school learners. We measure language errors with word error rate (WER), audio noise with speech-to-noise ratio (SNR), and video errors with frame-by-frame facial tracking accuracy. The results showed that the model’s accuracy for detecting confusion and conflict in the language modality decreased drastically from 0.84 to 0.73 when the WER exceeded 20%. Similarly, in the audio modality, the model’s accuracy decreased sharply from 0.79 to 0.61 when the SNR dropped below 5 dB. Conversely, the model’s accuracy remained relatively constant in the video modality at a comparable level (> 0.70) so long as at least one learner’s face was successfully tracked. Moreover, we trained several multimodal models and found that integrating multimodal data could effectively offset the negative effect of noise in unimodal data, ultimately leading to improved accuracy in recognizing confusion and conflict. These findings have practical implications for the future deployment of intelligent systems that support collaborative learning in actual classroom settings. 

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. 

Through a mixed-methods approach that utilized teacher surveys and a focus group with computer science (CS) instructional coaches, this study examined elementary teachers’ confidence in meeting the needs of students with disabilities, the extent to which the teachers could use the Universal Design for Learning (UDL) framework in CS education, and the strategies that their CS instructional coaches used with them to help meet the needs of all learners, including those with disabilities. Findings from a Wilcoxon signed-rank test and a general linear regression of the teacher surveys revealed that teachers’ confidence in teaching CS and in meeting the needs of students with disabilities increased over the 5 month coaching study, but their understanding of UDL remained low throughout the study. A qualitative thematic analysis of open-response survey questions revealed that the teachers could identify instructional strategies that support the inclusion of students with disabilities in CS instruction. These strategies aligned with high leverage practices (HLPs) and included modeling, the use of explicit instruction, and opportunities for repeated instruction. When asked to identify UDL approaches, however, they had more difficulty. The focus group with coaches revealed that the coaches’ primary aim related broadly to equity and specifically to access to and the quality of CS instruction. However, although they introduced UDL-based strategies, they struggled to systematically incorporate UDL into coaching activities and did not explicitly label these strategies as part of the UDL framework on a consistent basis. This finding explains, to a large extent, the teachers’ limited understanding of UDL in the context of CS education.