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.

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.

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 (AI) becomes more prominent in children's lives, an increasing number of researchers and practitioners 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, the framework that guides teachers and learning designers into inclusive learning design 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 articles and synthesized them into our proposed framework. Our new framework 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 education. 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.

Task-oriented dialogue systems are expected to handle a constantly expanding set of intents and domains even after they have been deployed to support more and more functionalities. To live up to this expectation, it becomes critical to mitigate the catastrophic forgetting problem (CF) that occurs in continual learning (CL) settings for a task such as intent recognition. While existing dialogue systems research has explored replay-based and regularization-based methods to this end, the effect of domain ordering on the CL performance of intent recognition models remains unexplored. If understood well, domain ordering has the potential to be an orthogonal technique that can be leveraged alongside existing techniques such as experience replay. Our work fills this gap by comparing the impact of three domain-ordering strategies (min-sum path, max-sum path, random) on the CL performance of a generative intent recognition model. Our findings reveal that the min-sum path strategy outperforms the others in reducing catastrophic forgetting when training on the 220M T5-Base model. However, this advantage diminishes with the larger 770M T5-Large model. These results underscores the potential of domain ordering as a complementary strategy for mitigating catastrophic forgetting in continually learning intent recognition models, particularly in resource-constrained scenarios.

Previous literature has associated math literacy with linguistic factors such as verbal ability and phonological skills. However, few studies have investigated linguistic synchrony, shown in mathematical discussions. This study modelled math literacy and examined the relationship of math literacy with linguistic synchrony between students and facilitators. We retrieved data from 20,776 online mathematical discussion threads at a secondary school level. First, we assessed students' math literacy based on their discussions and classified them into high- and low-math literacy groups. Then, we conducted Cross-Recurrence Quantification Analysis (CRQA) to calculate linguistic synchrony within each thread. The result implies that students with high math literacy are more likely to share common words (eg, mathematical terms) with facilitators. At the same time, they would paraphrase the facilitators' words rather than blindly mimic them as the exact sentences or phrases. On the other hand, students with low math literacy tend to use overlapping words with facilitators less frequently and are more likely to repeat the exact same phrases from the facilitators. The findings provide an empirical data analysis and insights into mathematical discussions and linguistic synchrony. In addition, this paper implies the directions to improve online mathematical discussions and foster math literacy.Practitioner notes What is already known about this topic Mathematical discussions are known to be an effective way to promote math literacy. Math literacy and linguistic skills have a strong link. Linguistic synchrony is related to better collaboration and common knowledge building. What this paper adds Reveals the relationship between math literacy and linguistic synchrony and deepens the understanding of digital communication in online learning environments. Provides empirical analysis of natural language data in group discussions using CRQA. Conceptualizes linguistic synchrony with three sub-concepts: linguistic concurrence, predictability, and complexity. Implications for practice and/or policy Educators and practitioners could utilize the automatic formative assessment of math literacy based on the student's language use in mathematical discussions. Educational technology researchers and designers could include CRQA indices and recurrence plots in the dashboard design to provide information to support teachers and learners. Teachers would be able to provide real-time interventions to promote effective mathematical communication and foster math literacy throughout mathematical discussions.

Linguistic alignment, the tendency of speakers to share common linguistic features during conversations, has emerged as a key area of research in computer-supported collaborative learning. While previous studies have shown that linguistic alignment can have a significant impact on collaborative outcomes, there is limited research exploring its role in K-12 learning contexts. This study investigates syntactic and lexical linguistic alignment in a collaborative computer science learning corpus from 24 pairs (48 individuals) of middle school students (aged 11-13). The results show stronger effects of self-alignment than partner-alignment on both syntactic and lexical levels, with students often diverging from their partners on task-relevant words. Furthermore, student self-alignment on the syntactic level is negatively correlated with partner satisfaction ratings, while self-alignment on lexical level is positively correlated with their partner's satisfaction.

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.

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.

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.

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.

Natural Language Processing (NLP) plays a significant role in our daily lives and has become an essential part of Artificial Intelligence (AI) education in K-12. As children grow up with NLP-powered applications, it is crucial to introduce NLP concepts to them, fostering their understanding of language processing, language generation, and ethical implications of AI and NLP. This paper presents a comprehensive review of digital learning environments for teaching NLP in K-12. Specifically, it explores existing digital learning tools, discusses how they support specific NLP tasks and procedures, and investigates their explainability and evaluation results in educational contexts. By examining the strengths and limitations of these tools, this literature review sheds light on the current state of NLP learning tools in K-12 education. It aims to guide future research efforts to refine existing tools, develop new ones, and explore more effective and inclusive strategies for integrating NLP into K-12 educational contexts.

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.

Power dynamics in human-human communication can impact rapport-building and learning gains, but little is known about how power impacts human-agent communication. In this paper, we examine dominance behavior in utterances between middle-school students and a teachable robot as they work through math problems, as coded by Rogers and Farace's Relational Communication Control Coding Scheme (RCCCS). We hypothesize that relatively dominant students will show increased learning gains, as will students with greater dominance agreement with the robot. We also hypothesize that gender could be an indicator of difference in dominance behavior. We present a preliminary analysis of dominance characteristics in some of the transactions between robot and student. Ultimately, we hope to determine if manipulating the dominance behavior of a learning robot could support learning.

Modeling a learner’s frustration in adaptive environments can inform scaffolding. While much work has explored momentary frustration, there is limited research investigating the dynamics of frustration over time and its relationship with problem-solving behaviors. In this paper, we clustered 86 undergraduate students into four frustration trajectories as they worked with an adaptive learning environment for introductory computer science. The results indicate that students who initially report high levels of frustration but then reported lower levels later in their problem solving were more likely to have sought help. These findings provide insight into how frustration trajectory models can guide adaptivity during extended problem-solving episodes.

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.

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.

This study examined undergraduate STEM students’ experiences using an online introductory computer programming learning environment equipped with an automated hint generation system. Following a convergent parallel mixed methods design, this study utilized both quantitative and qualitative data from student experiential data. Analysis by level of prior knowledge demonstrated that elements of the learning environment did not cater to their learning needs and cognitive architecture. Cognitive Load Theory was used to contextualize system elements against both higher and lower prior experience learners, ultimately pointing to a need to design better scaffolds and hints to the needs of novice CS learners.