This paper examines the co-design process for a foundational AI microcredential course targeting K-12 teachers' knowledge, agency, and effectiveness in integrating AI into their classrooms. We collaborated with six K-12 teachers and instructional coaches to ensure the course's relevance and practicality. Using conjecture mapping and memoing, we systematically captured and analyzed insights from the collaborative process. These methods helped us pinpoint essential themes and requirements for effective professional development (PD) that meets the unique challenges and opportunities of teaching about and using AI in K-12 classrooms. Themes included concerns about in-class monitoring for unethical impacts of AI integration and the desire for empowerment in evaluating and selecting AI tools that they can best leverage to meet state and national standards. Educator requirements centered on the creation of quick, easily accessible, and asynchronous learning activities. In addition, educators requested just-in-time AI integration resources and learning opportunities that can be leveraged throughout the year, rather than being limited to PD sessions. This study contributes to AI education by providing a framework for designing teacher professional development programs that are responsive to the evolving educational landscape and the specific needs of K-12 teachers.

This demo introduces a hands-on robotics module designed to cultivate AI literacy in elementary students through computational thinking and robotics. Co-designed with 20 elementary school teachers and instructional coaches, the module helps students differentiate between artificial intelligence and human intelligence, focusing on the AI4K12 Big Idea of Perception, and specifically how our senses differ from a robot's sensors. The centerpiece of the curriculum is the "alien planet" task, where students program a robot to explore an unseen planet created by a peer, using its sensors to identify objects like "aliens" (green), "water" (blue), and "unique materials" (red). This activity not only builds critical thinking and problem-solving skills but also introduces foundational AI concepts, such as how machines perceive the world differently than humans. By connecting to other subjects like art (e.g., hue values) and linking to students' real-world experiences, the module offers a truly interdisciplinary and relevant approach to early AI education.

As efforts to incorporate Computer Science (CS) and Computational Thinking (CT) into K-12 classrooms continue to expand, there is a growing need for programs that prepare teachers for the effective teaching and integration of CS and CT into their instruction. An ongoing challenge is preparing current and future teachers to develop the skills and confidence needed to teach and integrate CS and CT. Micro-credentials, designed as a short, focused course, offer opportunities for teachers to build skills and confidence through targeted study. This experience report examines a self-paced online micro-credential developed and implemented within a university-based college of education. The micro-credential was designed to equip both pre-service and in-service teachers with the skills and knowledge necessary to teach and integrate CS and CT into K-12 teaching and learning. We describe the micro-credential, including its structure, sequencing, and content. We then present an exploration of teachers' experiences in the micro-credential. Findings from surveys and CS autobiographies show increases in participants' attitudes, beliefs, and perceptions toward the conceptual and technical aspects of teaching CS, with a particular focus on designing clear and actionable plans for integration. The results from this study provide valuable insights for the development of future CS- and CT-focused micro-credentials.

This paper explores the use of large language models (LLMs) to score and explain short-answer assessments in K-12 science. While existing methods can score more structured math and computer science assessments, they often do not provide explanations for the scores. Our study focuses on employing GPT-4 for automated assessment in middle school Earth Science, combining few-shot and active learning with chain-of-thought reasoning. Using a human-in-the-loop approach, we successfully score and provide meaningful explanations for formative assessment responses. A systematic analysis of our method's pros and cons sheds light on the potential for human-in-the-loop techniques to enhance automated grading for open-ended science assessments.

This study introduces a methodology to investigate students’ collaborative behaviors as they work in pairs to build computational models of scientific processes. We expand the Self-Regulated Learning (SRL) framework—specifically, Planning, Enacting, and Reflection—proposed in the literature, applying it to examine students’ collaborative problem-solving (CPS) behaviors in a computational modeling task. We analyze these behaviors by employing a Markov Chain (MC) modeling approach that scrutinizes students’ model construction and model debugging behaviors during CPS. This involves interpreting their actions in the system collected through computer logs and analyzing their conversations using a Large Language Model (LLM) as they progress through their modeling task in segments. Our analytical framework assesses the behaviors of high- and low-performing students by evaluating their proficiency in completing the specified computational model for a kinematics problem. We employ a mixed-methods approach, combining Markov Chain analysis of student problem-solving transitions with qualitative interpretations of their conversation segments. The results highlight distinct differences in behaviors between high- and low-performing groups, suggesting potential for developing adaptive scaffolds in future work to enhance support for students in collaborative problem-solving.

Keeping K-12 teachers engaged during students’ learning and problem solving in technology-enhanced, integrated problem-based learning (PBL) has been shown to support deeper student involvement, and, therefore, better success learning difficult science, computing, and engineering concepts and practices. However, students’ learning processes and corresponding difficulties are not easily noticed by teachers as students learn from these environments as processes are captured through mouse clicks, drag and drop actions, and other low-level activities. As such, teachers find it difficult to set up meaningful interactions with students while also maintaining the focus on student-centered learning. Little research has examined dashboard-supported responsive teaching practices for K-12 PBL. This study examined 8 teachers as they used a co-designed teacher dashboard to assess and respond to students’ learning and strategies during an integrated, PBL STEM curriculum. Teachers completed a series of 5 “planning period simulations” leveraging the dashboard and think-aloud protocols were implemented, supported by semi-structured interview questions, to enable the teachers to verbalize their thought and evaluation processes. Content analysis and epistemic network analysis were conducted to analyze the simulations. Understanding how teachers use dashboards to support evidence-based teaching practices during technology-enhanced curricula is critical for improving teacher support and preparation.

This paper presents a study using a robotics platform for teaching computing and cybersecurity concepts to high school students. 38 students attended a week-long camp, starting with projects such as a simulation-only game and a simple autonomous driving program for the robots in order to learn and apply computational thinking (CT) and networking skills. They were then assigned a series of challenges that required developing progressively more advanced cybersecurity measures to protect their robots. This culminated in a final challenge that required implementing defensive measures such as encryption, secure key exchange and sequence numbers. We used an evidence-centered design framework to construct rubrics for grading student work. The pre- and post-test results show that the interventions helped students learn cybersecurity and CT concepts, but they had difficulties with networking concepts. These results correlate with scores from the game and the final challenge. Overall, surveys show that the competition-based robotics learning framework engaged students and supported their overall learning, but our intervention needs to be modified to help students learn networking concepts

C2STEM is a web-based learning environment founded on a novel paradigm that combines block-structured, visual programming with the concept of domain specific modeling languages (DSMLs) to promote the synergistic learning of discipline-specific and computational thinking (CT) concepts and practices. Our design-based, collaborative learning environment aims to provide students in K-12 classrooms with immersive experiences in CT through computational modeling in realistic scenarios (e.g., building models of scientific phenomena). The goal is to increase student engagement and include inclusive opportunities for developing key computational skills needed for the 21st century workforce. Research implementations that include a semester-long high school physics classroom study have demonstrated the effectiveness of our approach in supporting synergistic learning of STEM and CS/CT concepts and practices, especially when compared to a traditional classroom approach. This technology demonstration will showcase our CS+X (X = physics, marine biology, or earth science) learning environment and associated curricula. Participants can engage in our design process and learn how to develop curricular modules that cover STEM and CS/CT concepts and practices. Our work is supported by an NSF STEM+C grant and involves a multi-institutional team comprising Vanderbilt University, SRI International, Looking Glass Ventures, Stanford University, Salem State University, and ETR. More information, including example computational modeling tasks, can be found at C2STEM.org.

The paper presents RoboScape, a collaborative, networked robotics environment that makes key ideas in computer science accessible to groups of learners in informal learning spaces and K-12 classrooms. RoboScape is built on top of NetsBlox, an open-source, networked, visual programming environment based on Snap! that is specifically designed to introduce students to distributed computation and computer networking. RoboScape provides a twist on the state of the art of robotics learning platforms. First, a user's program controlling the robot runs in the browser and not on the robot. There is no need to download the program to the robot and hence, development and debugging become much easier. Second, the wireless communication between a student's program and the robot can be overheard by the programs of the other students. This makes cybersecurity an immediate need that students realize and can work to address. We have designed and delivered a cybersecurity summer camp to 24 students in grades between 7 and 12. The paper summarizes the technology behind RoboScape, the hands-on curriculum of the camp and the lessons learned.