As artificial intelligence (AI) becomes increasingly integrated into daily life, there is a critical need for developing AI literacy across all educational levels. However, current AI education remains largely confined to college-level computer science classrooms with limited access for K-12 learners. We present the AI Scholars Program, a novel approach that addresses the AI education gap by preparing college computing students to serve as AI education ambassadors in their communities and empowering K-12 teachers to adopt AI education practices in their classrooms. This experience report presents the curriculum and its outcomes after one round of refinement. The program offers structured AI learning through bi-weekly webinars, resources, and collaborative opportunities to form teams and conduct community outreach projects. Our program invited 63 scholars from 30 institutions across the U.S., including 51 college students and 12 K-12 teachers. Their outreach impacted over 230 K-12 learners. We examine program outcomes for participants and projects through pre/post surveys measuring computing attitudes and self-efficacy for teaching AI, scholar interviews, and outreach project reports. We share lessons learned and challenges for designing similar programs, highlighting the importance of involving educators for effective community-engaged AI education. The program creates a sustainable pipeline for college students to develop technical skills and leadership while addressing K-12 AI education shortages. We contribute insights for scaling AI literacy and broadening participation in computing.

AI-based chatbots have the potential to accelerate learning and teaching, but may also have counterproductive consequences without thoughtful design and scaffolding. To better understand teachers’ perspectives on large language model (LLM) based chatbots, we conducted a study with 11 teams of middle-school teachers using chatbots for a science and computational thinking activity within a block-based programming environment. Based on a qualitative analysis of audio transcripts and chatbot interactions, we propose three profiles: explorer, frustrated, and mixed that reflect diverse scaffolding needs. In their discussions, we found that teachers perceived chatbot benefits such as building prompting skills and self confidence alongside risks including potential declines in learning and critical thinking. Key design recommendations include scaffolding the introduction to chatbots, facilitating teacher control of chatbot features, and suggesting when and how chatbots should be used. Our contribution informs the design of chatbots to support teachers and learners in middle school coding activities.

Children increasingly interact with generative AI systems that can produce hallucinated content, potentially reinforcing misconceptions and undermining critical thinking skills. We investigate how children detect and respond to hallucinations while building and testing LLM-powered chatbots in a development environment. We integrated hallucination-awareness scaffolds such as confidence indicators, fact-checking, repeated questioning, and model comparison. Through a study with 48 middle school learners aged 10-14, participants showed significant pre-to-post gains in AI knowledge, hallucination awareness, and confidence in building trustworthy chatbots. They developed multi-layered strategies, including probing inconsistencies and cross-checking with external sources. Key challenges included over-reliance on visible cues, fragmented use of scaffolds, and a tension between creativity and reliability. These findings highlight design implications for children’s AI literacy for responsible AI development: supporting proactive, iterative engagement in the development cycle, integrating scaffolds into coherent workflows, and balancing creativity with accuracy.

Tutoring systems improve learning through tailored interventions, such as worked examples, but often suffer from the aptitude-treatment interaction effect where low prior knowledge learners benefit more. We applied the ICAP learning theory to design two new types of worked examples, Buggy (students fix bugs), and Guided (students complete missing rules), requiring varying levels of cognitive engagement, and investigated their impact on learning in a controlled experiment with 155 undergraduate students in a logic problem solving tutor. Students in the Buggy and Guided examples groups performed significantly better on the posttest than those receiving passive worked examples. Buggy problems helped high prior knowledge learners whereas Guided problems helped low prior knowledge learners. Behavior analysis showed that Buggy produced more exploration-revision cycles, while Guided led to more help-seeking and fewer errors. This research contributes to the design of interventions in logic problem solving for varied levels of learner knowledge and a novel application of behavior analysis to compare learner interactions with the tutor.

Block-Based Programming (BBP) platforms, such as Snap!, have become increasingly prominent in $\mathrm{K}-12$ computer science education due to their ability to simplify programming concepts and foster computational thinking from an early age. While these platforms engage students through visual and gamified interfaces, teachers often face challenges in using them effectively and finding all the necessary features for classroom management. To address these challenges, we introduce SnapClass, a classroom management system integrated within the Snap! programming environment. SnapClass was iteratively developed drawing on established research about the pedagogical and logistical challenges teachers encounter in computing classrooms. Specifically, SnapClass allows educators to create and customize block-based coding assignments based on student skill levels, implement rubric-based auto-grading, and access student code history and recovery features. It also supports monitoring student engagement and idle time, and includes a help dashboard with a “raise hand” feature to assist students in real time. This paper describes the design and key features of SnapClass those are developed and those are under progress.

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.

As artificial intelligence permeates our lives through various tools and services, there is an increasing need to consider how to teach young learners about AI in a relevant and engaging way. One way to do so is to leverage familiar and pervasive technologies such as conversational AIs. By learning about conversational AIs, learners are introduced to AI concepts such as computers’ perception of natural language, the need for training datasets, and the design of AI-human interactions. In this experience report, we describe a summer camp curriculum designed for middle school learners composed of general AI lessons, unplugged activities, conversational AI lessons, and project activities in which the campers develop their own conversational agents. The results show that this summer camp experience fostered significant increases in learners’ ability beliefs, willingness to share their learning experience, and intent to persist in AI learning. We conclude with a discussion of how conversational AI can be used as an entry point to K-12 AI education.

Mathematical discussions have become a popular educational strategy to promote math literacy. While some studies have associated math literacy with linguistic factors such as verbal ability and phonological skills, no studies have examined the relationship between linguistic synchrony and math literacy. In this study, we modeled linguistic synchrony and students’ math literacy from 20,776 online mathematical discussion threads between students and facilitators. We conducted Cross-Recurrence Quantification Analysis (CRQA) to calculate linguistic synchrony within each thread. The statistical testing result comparing CRQA indices between high and low math literacy groups shows that students with high math literacy have a significantly higher Recurrence Rate (RR), Number of Recurrence Lines (NRLINE), and the average Length of lines (L), but lower Determinism (DET) and normalized Entropy (rENTR). This result implies that students with high math literacy are more likely to share common words with facilitators, but they would paraphrase them. On the other hand, students with low math literacy tend to repeat the exact same phrases from the facilitators. The findings provide a better understanding of mathematical discussions and can potentially guide teachers in promoting effective mathematical discussions.

The ubiquity of AI-based conversational apps such as Siri, Alexa and Google Assistant means more young users are interacting with these apps. The increasing popularity of these conversational applications brings a potential opportunity to attract learners to AI, CS and STEM fields. CS Education researchers need to explore how to leverage this opportunity, in particular to serve learners who are underrepresented in CS and STEM. This experience report describes the design and iterative refinement of a series of two-week summer camps in which 62 predominantly Black students participated in hands-on AI-based learning experiences to design and develop their own conversational AI apps. We discuss the organization of this summer camp experience, including strategies for recruiting from and building trust within the target community, designing professional development for camp facilitators, structuring the camp activities, and encouraging projects that are personally and socially relevant. We share challenges and lessons learned from this AI summer camp in the hopes that they will inform other researchers and practitioners who are interested in designing and deploying similar experiences.

More young people are interacting with smart conversational agents such as Alexa and Google Assistant. These platforms are extensible, providing, in principle, a compelling opportunity for young users to create and tinker with their own conversational agents. However, to date the interfaces for conversational app development are adult-focused. This paper presents the early design process for AMBY (AI Made by You), which we are building to empower young learners to create their own conversational agents. We first conducted a contextual inquiry with 14 middle school students (aged 11-13) in an AI summer camp, followed by two other usability studies. The system design has been refined after each study. Key features of AMBY include a visual dialogue management panel, testing panel with a diverse avatar, and a voice input modality. AMBY is designed to serve as a pedagogically-robust resource for K-12 AI education and as an engaging and creative way for middle schoolers to explore AI.

Block-based programming environments are widely used in computer science education. However, these environments pose significant challenges for student modeling. Given a series of problem-solving actions taken by students in block-based programming environments, student models need to accurately infer problem-solving students’ programming abilities in real time to enable adaptive feedback and hints that are tailored to students’ abilities. While student models for block-based programming offer the potential to support student-adaptivity, creating student models for these environments is challenging because students can develop a broad range of solutions to a given programming activity. To address these challenges, we introduce a progression trajectory-based student modeling framework for modeling novice student block-based programming across multiple learning activities. Student trajectories utilize a time series representation that employs code analysis to incrementally compare student programs to expert solutions as students undertake block-based programming activities. This paper reports on a study in which progression trajectories were collected from more than 100 undergraduate students engaging in a series of block-based programming activities in an introductory computer science course. Using progression trajectory-based student modeling, we identified three distinct trajectory classes: Early Quitting, High Persistence, and Efficient Completion. Analysis of these trajectories revealed that they exhibit significantly different characteristics with respect to students’ actions and can be used to accurately predict students’ programming behaviors on future programming activities compared to competing baseline models. The findings suggest that progression trajectory-based student models can accurately model students’ block-based programming problem solving and hold potential for informing adaptive support in block-based programming environments.

Research has shown study habits and skills to be correlated with academic success, calling for a deeper comprehension of these behaviors and processes to design effective interventions for struggling students. Chatbots have recently been used as a persuasive technology to help support behavioral change, making them an intriguing design space for students' study habits and skills. This paper investigated the feasibility of using chatbots for promoting behavioral change of college students majoring in Computer Science (CS). We conducted semi-structured interviews with CS peer-tutors and surveyed university freshmen to understand students' study habits and identify technical intervention opportunities. Inspired by the findings, we designed StudyBuddy, a chatbot prototype deployed in Slack that periodically sends tips, provides assessments of students' study habits via surveys, helps the students break down assignments, recommends academic resources, and sends reminders. We evaluated the usability of the prototype in-depth with 8 students (both first-year and senior students) and 5 course instructors followed by a large scale evaluative survey (n=117) using video of the prototype. Our research identified important design challenges such as building trust and preserving privacy, limiting interaction costs, and supporting both immediate and long-term sustainable support. Likewise, we proposed design recommendations that demonstrate context awareness, personalize the experience based on user preferences, and adapt over time as students mature and grow.