Fred G. Martin

Conversational agents, such as chatbots, have increasingly found their way into many dimensions of our lives, including entertainment and education. In this exploratory study, we designed Ask Me Anything (AMA), a child-friendly, topic-specific chatbot, and investigated children’s attitudes and trust toward AI-driven conversational agents. Unlike general-purpose assistants, AMA was constrained to three child-relevant topics—astronomy, sneakers and shoes, and dinosaurs—to prompt targeted questioning from students and drive engagement. We tested AMA with 63 students, ages 6 to 14, including first-graders and middle school students (grades 6 to 8), in a public school in the Northeastern United States. Students worked in small groups, interacted with our tool for three to ten minutes, and completed a post-survey. We identified three key themes that emerged from student conversational interactions with AMA: expressing wonder, surprise, and curiosity; building trust and developing confidence; and building relationships and anthropomorphizing. Also, we observed a broad attitude of openness and comfort. Students trusted the chatbot responses in general, indicating a high level of trust in and reliance on AI as a source of information. They described AMA as “knowledgeable,” “smart,” and that they could “trust it.” To confirm their perception of reliability, some students tested the chatbot with questions to which they knew the answers. This behavior illustrated a fundamental aspect of children’s cognitive development: the process of actively evaluating the credibility of sources. This study contributes to the growing literature on child–AI interaction by revealing gaps in children’s critical engagement and highlighting the need for design that scaffolds AI literacy. It reveals the need for age-sensitive, trust-aware design and points to a broader gap in digital safety awareness among children interacting with AI.

This paper explores the evolution of artificial intelligence (AI) and machine learning (ML) educational tools and curricula for children, describing a spectrum from playful exploration to structured pedagogy. We address three research questions: (RQ1) What themes are revealed in the literature on AI/ML for children? (RQ2) In what ways do the development of AI/ML tools and curricula co-evolve? (RQ3) How does the historical context of the field frame current work on AI/ML for children? We observe a co-evolution of tools and curricula, where researchers build new tools and curriculum developers adopt tools created by others. As tools prove successful, they become widely available and are adopted by a broad range of users. Additionally, we note instances where tool creators take on the role of curriculum developer. Recognizing the need for a clear overview of AI/ML educational resources, we introduce major themes of tools, tool-forward, and curriculum-forward works. These themes are designed to help educators, researchers, and curriculum designers in discerning the fundamental nature and potential applications of resources. By distinguishing tools that are standalone from those integrated into curricula, our framework supports strategic decisions in resource adoption and development. Drawing on the field’s history, we outline a continuum of tools and platforms, from constructionist elements to interactive play environments.

As artificial intelligence (AI) technology rapidly advances and integrates further into everyday life, building AI literacy in children is now more important than ever. AI literacy includes understanding key AI concepts such as search algorithms, which are foundational and have wide-ranging real-world applications. To support curricula that foster AI and computational literacy in K-12 students, we developed a novel, easy-to-use software tool to introduce this foundational topic by teaching two search algorithms to middle school children. We present IntoTheRabbitHole, an interactive web application where users help a rabbit find its carrot to learn about Depth First Search (DFS) and Breadth First Search (BFS). In this work, our research questions were: (RQ1) Can we develop an engaging and enjoyable web application to teach search algorithms?; (RQ2) Could we find evidence of student learning of Depth First Search and Breadth First Search from using our application?; and (RQ3) How do students compare Depth First Search and Breadth First Search after interacting with IntoTheRabbitHole? We collected both quantitative and qualitative data on student use of IntoTheRabbitHole at an after-school AI program. We found that IntoTheRabbitHole was successful in fostering an understanding of both DFS and BFS. Furthermore, we obtained a statistically significant result showing that interaction with IntoTheRabbitHole led to better performance on a DFS traversal pre- and post-survey item, indicating that many students learned DFS through interaction with our tool.

This paper presents the Teacher Education for Data Science (TEDS), a study conducted with a cohort of teachers who developed methods to incorporate data fluency approaches into teaching and learning across multiple middle school subjects. The objective of this study is to investigate how teachers integrate data science pedagogy into their instruction for middle-grade learners. In a four-session online professional development sequence, teachers learned to use a web-based collaborative data visualization platform; developed a data-intensive lesson for their existing middle school curriculum; implemented the lesson(s) with their students; and shared reflections with fellow teachers and the researchers.This paper shares the design of the professional development sequence, explores the capabilities of the data visualization platform, and examines three examples of data-intensive projects created by the teachers: a paper airplane engineering activity (science class); a "travel agency" vacation planning activity (life skills class); and a volcano research activity (science class).We describe how we supported the teachers in a responsive co-design process. The data collected included teacher-created instructional artifacts, and 30-minute post-teaching interviews, focusing on their lesson design process and student responses to data-intensive approaches.Teachers reported on student agency as they entered data into the collaborative platform and discovered real-world connections in their data. To deepen middle school learners’ engagement and fluency with data, we recommend expanding teachers’ access and familiarity with technology-supported pedagogical approaches. Integrating data science into the curriculum broadens opportunities for both teachers and students, fostering critical thinking and deeper engagement with the world around us.

In this research full paper, we examine students’ improvement in spatial visualization skills when using MYR (short for "My Reality"), a browser-based, cloud-hosted programming environment for beginning through advanced programmers to create immersive, three-dimensional virtual reality scenes.The research literature suggests that there is correlation between students’ spatial abilities and their success in programming. In this study, we conducted a three-week (six hour) virtual after-school program which introduced high school students with different programming backgrounds to coding in MYR. During the program, students learned the basics of MYR, introductory CS topics, and completed individual coding projects, creating an original MYR scene.A study examined changes in students’ spatial reasoning as a result of this intervention. The students’ performance in spatial skills was measured using the Revised Purdue Spatial Visualization Test: Visualization of Rotations (Revised PSVT:R) [1]. Students completed this instrument using a pre/post survey design. We analyzed the impact of the intervention using a paired samples T-test. We further developed a rubric for analyzing the sophistication of students’ MYR code and applied it to evaluating the programming expertise of our study participants.The program was hosted twice with two different groups of high school students. Most showed interest in MYR programming and expressed their creativity and learned skills in their original project. With the first group of students, we found increases in their spatial visualization performance after the intervention with MYR, though statistical significance was not reached. The second group of students had higher baseline prior experience in computing and spatial visualization skills; these students did not further increase in their spatial visualization skill.The analysis showed that the MYR has a potential to improve spatial skills and engage students’ interest in computing. We recommend that MYR and related computational environments be further studied and made available to students.

The ubiquity of AI in society means the time is ripe to consider what educated 21st century digital citizens should know about this subject. In May 2018, the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) formed a joint working group to develop national guidelines for teaching AI to K-12 students. Inspired by CSTA’s national standards for K-12 computing education, the AI for K-12 guidelines will define what students in each grade band should know about artificial intelligence, machine learning, and robotics. The AI for K-12 working group is also creating an online resource directory where teachers can find AI- related videos, demos, software, and activity descriptions they can incorporate into their lesson plans. This blue sky talk invites the AI research community to reflect on the big ideas in AI that every K-12 student should know, and how we should communicate with the public about advances in AI and their future impact on society. It is a call to action for more AI researchers to become AI educators, creating resources that help teachers and students understand our work.