Artificial intelligence (AI) is quickly finding broad application in every sector of society. This rapid expansion of AI has increased the need to cultivate an AI-literate workforce, and it calls for introducing AI education into K-12 classrooms to foster students’ awareness and interest in AI. With rich narratives and opportunities for situated problem solving, story-driven game-based learning offers a promising approach for creating engaging and effective K-12 AI learning experiences. In this paper, we present our ongoing work to iteratively design, develop, and evaluate a story-driven game-based learning environment focused on AI education for upper elementary students (ages 8 to 11). The game features a science inquiry problem centering on an endangered species and incorporates a Use-Modify-Create scaffolding framework to promote student learning. We present findings from an analysis of data collected from 16 students playing the game's quest focused on AI planning. Results suggest that the scaffolding framework provided students with the knowledge they needed to advance through the quest and that overall, students experienced positive learning outcomes.

Recent years have seen growing awareness of the need to advance AI literacy for K-12 students to empower them in understanding, evaluating, and using AI. Automated problem solving is a fundamental aspect of AI, enabling machines to mimic human problemsolving abilities. Fostering awareness and interest in AI capabilities such as automated problem solving should begin early, including in the elementary grades. Although AI planning can be a complex topic, leveraging the benefits of game-based learning offers a promising approach to engage young children in learning about this important AI concept. In this work, we explore the interactions and outcomes of upper elementary students (ages 8 to 11) playing a quest on AI planning embedded within a game-based learning environment. Results indicate that students experienced positive learning gains from pre-test to post-test, while analyzing trace data from the game provides insights into challenges students faced as they attempted the in-game missions.

This study explores the structure of a summer camp curriculum and the development of middle school students' understanding of Artificial Intelligence (AI). Pre-posttests and surveys measuring AI knowledge and attitudes toward AI were analyzed by paired t-tests. Thematic analysis was used to analyze over 30 hours of video footage and student artifacts to examine the integration of curriculum and the development of students' understanding. The curriculum encompassed machine learning (ML), natural language process (NLP), and computer vision (CV), focusing on improving students' understanding of AI through the creation of AI-based artifacts, and mini-projects addressing daily issues faced in homes and schools. The findings revealed that students' overall AI knowledge and attitudes toward AI significantly improved after the intervention. Additionally, students recognized the importance of data quality and quantity in training AI. These findings highlight the positive impact of summer camp curriculum with hands-on experiences, beneficial for fostering middle school students' understanding of AI.

The growing ubiquity of artificial intelligence (AI) is reshaping much of daily life. This in turn is raising awareness of the need to introduce AI education throughout the K-12 curriculum so that students can better understand and utilize AI. A particularly promising approach for engaging young learners in AI education is game-based learning. In this work, we present our efforts to embed a unit on AI planning within an immersive game-based learning environment for upper elementary students (ages 8 to 11) that utilizes a scaffolding progression based on the Use-Modify-Create framework. Further, we present how the scaffolding progression is being refined based on findings from piloting the game with students.

There is growing attention to the need for computer science (CS) education. Stakeholders have emphasized the importance of teaching CS to all students and exposing CS to students at the early elementary level. However, there are relatively few investigations into approaches for integrating CS education into elementary classroom instruction. In this work, we explored elementary CS teachers' CS integration perceptions and practices as the first step of a CS-integrated curriculum co-design program.

As artificial intelligence (AI) technology becomes increasingly pervasive, it is critical that students recognize AI and how it can be used. There is little research exploring learning capabilities of elementary students and the pedagogical supports necessary to facilitate students' learning. PrimaryAI was created as a 3rd-5th grade AI curriculum that utilizes problem-based and immersive learning within an authentic life science context through four units that cover machine learning, computer vision, AI planning, and AI ethics. The curriculum was implemented by two upper elementary teachers during Spring 2022. Based on pre-test/post-test results, students were able to conceptualize AI concepts related to machine learning and computer vision. Results showed no significant differences based on gender. Teachers indicated the curriculum engaged students and provided teachers with sufficient scaffolding to teach the content in their classrooms. Recommendations for future implementations include greater alignment between the AI and life science concepts, alterations to the immersive problem-based learning environment, and enhanced connections to local animal populations.

Students can begin to lose interest in CS as early as 2nd grade, indicating the importance of engaging students in CS as early as possible. This study examined the integration of computational thinking (CT) into literacy activities in early childhood education (K-2). We describe the co-design process of developing computational thinking literacy integrated curriculum for K-2, and preliminary results of K-2 student engagement in CT and literacy activities

There is growing awareness of the central role that artificial intelligence (AI) plays now and in children's futures. This has led to increasing interest in engaging K-12 students in AI education to promote their understanding of AI concepts and practices. Leveraging principles from problem-based pedagogies and game-based learning, our approach integrates AI education into a set of unplugged activities and a game-based learning environment. In this work, we describe outcomes from our efforts to co design problem-based AI curriculum with elementary school teachers.

AI is beginning to transform every aspect of society. With the dramatic increases in AI, K-12 students need to be prepared to understand AI. To succeed as the workers, creators, and innovators of the future, students must be introduced to core concepts of AI as early as elementary school. However, building a curriculum that introduces AI content to K-12 students present significant challenges, such as connecting to prior knowledge, and developing curricula that are meaningful for students and possible for teachers to teach. To lay the groundwork for elementary AI education, we conducted a qualitative study into the design of AI curricular approaches with elementary teachers and students. Interviews with elementary teachers and students suggests four design principles for creating an effective elementary AI curriculum to promote uptake by teachers. This example will present the co-designed curriculum with teachers (PRIMARYAI) and describe how these four elements were incorporated into real-world problem-based learning scenarios.

Across the globe, Computer Science Education has grown tremendously over the past decade to teach primary and secondary students computing ideas and tools. From integrating computational thinking in disciplines to teaching computer science as a stand alone subject, models for teacher preparation range from one and done professional learning workshops to full certificate and licensure programs. The group will focus on providing a landscape of how CS teachers are prepared academically in various countries and make evidence-based recommendations for how teachers should be educated to develop knowledge and skill to teach computer sci- ence. The working group will also discuss how to develop these knowledge systems while promoting instruction that is equitable and centers students in the classroom. In addition, the working group will focus on new directions in computing education (such as, artificial intelligence and machine learning) and their implica- tions for teacher preparation. We will bring together a group of international computer science education scholars who have been engaged in teacher preparation. In addition to what knowledge teachers need to teach CS, we will also focus on how the field is preparing teachers to think critically about AI/ML and the role of computer science in the design of technology tools to achieve goals while mitigating potential societal harms.

Artificial intelligence has emerged as a technology that is profoundly reshaping society and enabling rapid improvements in science, engineering, and mathematics, as well as information technology itself. This has generated increased demand for fostering an AI-literate populace as well as a growing recognition of the importance of promoting K-12 students’ awareness and interest in AI. Although efforts are be-ginning to incorporate AI learning within K-12 education, there is little research exploring how to introduce students to AI and how to support teachers to integrate AI learning experiences in their classrooms. This is especially true at the elementary school level. A particularly promising approach for providing effective and engaging AI learning experiences for elementary students is game-based learning. In this paper, we explore how to introduce AI-infused collaborative inquiry learning into upper elementary school (student ages 8 to 11) using game-based learning. To ground the work in the realities of elementary school classrooms, we present insights from interviews with elementary school teachers to under-stand how best to support them in integrating AI into their classrooms. We then present the design of PrimaryAI, a game-based learning environment that supports rich problem-based learning activities within upper elementary classrooms centered on AI applied toward solving life-science problems. Finally, we discuss some of the challenges we face in bringing AI-infused collaborative inquiry learning to upper elementary students.

Collective impact is an approach for solving complex social problems at scale. The challenge of broadening participation in computing (BPC) is one such problem. The complexity of BPC is compounded by the decentralized nature of public education, where decisions are made primarily at the state level and subject to interpretation at the district level. As such, diversifying computer science (CS) pathways across the nation requires a systemic approach such as collective impact to engage all of the stakeholders who influence CS education and whose decisions can either facilitate or hinder BPC efforts. This experience report discusses how the collective impact framework has been used to advance the work of the Expanding Computing Education Pathways (ECEP) Alliance, an NSF funded BPC Alliance focused on states and state policy as the unit of change. We discuss how the five essential features of collective impact (common agenda, shared measurement, mutually reinforcing activities, continuous communication, and backbone support) coalesce to facilitate ECEP's theory of change. The report highlights specific policy changes that ECEP states have addressed to promote BPC, the flipped accountability that results from a non-hierarchical leadership model, and the challenges of measuring systemic changes as an intermediary to BPC.

In order to fully prepare high-quality teachers to deliver on the promise of CS for All, teacher preparation programs must ensure teacher candidates have mastered both content and the pedagogy to teach that content well. University CS departments are well-positioned to teach the content, but "methods courses" are an important source of pedagogical and pedagogical content knowledge for future computer science teachers. This panel will describe a variety of approaches: Colleges of Education or CS departments; in-person, hybrid, or online course delivery; and standalone "CS Methods" courses or CS integrated in broader methods courses. Each panelist will describe their course. The moderator will elicit common features between the courses as well as the strengths and weaknesses between different approaches.

In recent years, state members of the Expanding Computing Education Pathways (ECEP) Alliance have made efforts to increase access to and broaden participation in computing at the K-12 levels. Each ECEP state's K-12 computer science (CS) education journey has been documented during their ECEP membership resulting in over 25,000 digital documents. Over the course of the project it was necessary to track key events, identify trends across states, and maintain consumable records of state progress. A systematic way to collect and track the data is critical to conduct historical and cross-state analyses. In an effort to quantify and categorize, the researchers engaged in a review process of all ECEP reports, artifacts, and other relevant data to develop a system. Relevant and important components were identified in each type of document and assigned codes using ECEP's Five Stage Model ("a five-step process toward state-level CS education reform"), the Capacity, Access, Participation, and Experience (CAPE) framework (to measure equity in CS education implementation), and specific policies initiatives (alignment with various policy initiatives - Code.org's "Nine Policy Ideas to Make CS Fundamental to K?12 Education"). Indiana was identified as a state to conduct an initial, in-depth case study using this process. Indiana's case will be used as a model to further develop the stories of other ECEP Alliance member states. Through the development of a data dashboard, we hope to organize all of this information to make it more easily accessible for review and further analysis. The ECEP data dashboard development is currently in progress.

Despite the growing demand for computer science (CS) education for K-12 students, one of the most significant barriers to schools offering CS courses is the lack of certified teachers [1]. According to a report by Code.org [2], only a few states have initial CS certification teacher pathways resulting in differences among the types of qualification pathways. The lack of consistent and clear information is confusing for teachers, school districts, and advocacy groups. One of the challenges of creating a clear understanding of computer science teacher certification in the United States is that certificates can have many meanings, and definitions differ from state to state [3]. This study gathers nationwide data of all the pathways to teach computer science to create definitions for the multitude of CS credential pathways and demonstrates the spectrum of credential pathways.

Countries around the globe have acknowledged the pervasiveness of artificial intelligence (AI) in our lives and the importance of educating our students on how AI technologies work. For example, the Chinese education ministry has integrated AI into the mandatory high school curriculum, including a pilot textbook to teach students about the fundamental AI technologies like deep learning and recognition. However, there are fewer examples of AI education at the primary level, and these are typically focused on decision-making, machine learning, and programming. We have recently started to develop our own elementary AI curriculum. To develop the curriculum, we first needed to explore what students already knew about AI. Although there are a few small studies on how primary students conceptualize AI, we conducted a study to examine how 10 nine- and ten-year-old students conceptualized artificial intelligence by focusing on two main questions: (1) How do elementary students conceptualize artificial intelligence? (2) What are elementary students' experiences with artificial intelligence? Students? definitions of AI tended to focus on programming and robotics. Students described examples of AI that included robotic vacuums, Siri/Alexa, YouTube, and search engines. They also showcased some misconceptions around AI designs and implementations.

As primary and secondary computer science offerings expand across the United States and other countries, there is a growing demand for educators who can teach CS. One way to meet the demand is to include CS content and instruction in teacher preparation programs, at the inservice (practicing teachers) and/or preservice (becoming a teacher) levels. This workshop is designed for higher education faculty from CS and Education departments as well as teachers who support CS education programs and would like to learn more about creating pathways for preparing teachers to teach CS. The workshop will provide rationales for CS teacher preparation programs (including how to develop and sustain the programs), detail what teachers need to know and be able to do to teach CS, review dimensions and examples of existing programs, and share resources to support CS teacher preparation program development. Participants will have an opportunity in the last hour to work on developing a CS program for their institution and writing an action plan of 2-3 short term goals to support program development.

Recent years have seen the rapid adoption of artificial intelligence (AI) in every facet of society. The ubiquity of AI has led to an increasing demand to integrate AI learning experiences into K-12 education. Early learning experiences incorporating AI concepts and practices are critical for students to better understand, evaluate, and utilize AI technologies. AI planning is an important class of AI technologies in which an AI-driven agent utilizes the structure of a problem to construct plans of actions to perform a task. Although a growing number of efforts have explored promoting AI education for K-12 learners, limited work has investigated effective and engaging approaches for delivering AI learning experiences to elementary students. In this paper, we propose a visual interface to enable upper elementary students (grades 3–5, ages 8–11) to formulate AI planning tasks within a game-based learning environment. We present our approach to designing the visual interface as well as how the AI planning tasks are embedded within narrative-centered gameplay structured around a Use-Modify-Create scaffolding progression. Further, we present results from a qualitative study of upper elementary students using the visual interface. We discuss how the Use-Modify-Create approach supported student learning as well as discuss the misconceptions and usability issues students encountered while using the visual interface to formulate AI planning tasks.

Recent years have seen growing recognition of the importance of enabling K-12 students to learn computer science. Meanwhile, artificial intelligence has emerged as a technology with the potential to profoundly reshape society. This has generated increasing demand for fostering an AI-literate populace. However, there is little work exploring how to introduce K-12 students to AI and how to support K-12 teachers in integrating AI into their classrooms. In this work, we explore introducing AI learning experiences into upper elementary classrooms (student ages 8 to 11). With a focus on integrating AI and life science, we present initial work on a collaborative game-based learning environment that features rich problem-based learning scenarios. This will enable students to gain experience with AI as it applies to solving real-world life-science problems.

As many U.S. states continue to work to increase and broaden participation in K-20 computing education, it is imperative to collect data and construct landscape reports to create organized efforts and strategic plans. Effective CS education interventions, and strategic plans, must be data-driven in order to ensure that all students have access to and are retained in high quality K- 20 computer science pathways (Stanton et al., 2017). The Expanding Computing Education Pathways (ECEP) Alliance, and the 23 member states, have been leaders in the development and promotion of state-level landscape reports. Several ECEP states have successfully designed, delivered, and analyzed data collection tools to landscape the current status of computer science education within their own state (e.g., Maryland, Indiana, Texas). However, there have been instances where data collected by different stakeholders have provided conflicting perspectives and viewpoints. Conflicting data, data that fails to account for intersectionality, or leaves out critical populations or context, potentially distracts time and effort from broadening participation in computing. This session will provide a platform for researchers and evaluators to discuss data relevant to BPC efforts, how to develop surveys, and how to structure and disseminate reports.