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Experience firsthand how playful, hands-on design challenges can serve as engaging entry points into core computer science concepts. Using a playful, team-based monster-replicating challenge as the anchor activity, we explore the foundational CS concept of input → process → output. Then we’ll explore how we might use open-source AI platforms to adapt the lesson to support differentiated instruction for diverse learners, revisiting the anchor activity from multiple learner perspectives. Then we’ll switch gears and dive into student-facing AI, using it as a tool for building personal communication and creative thinking skills rather than a substitute for their own ideas. Leave with practical strategies, ready-to-use activities, and a clear framework for connecting computer science, hands-on STEM, and inclusive pedagogy across the K–8 classroom.
In this highly interactive session, educators will experience how a simple, tech-free building challenge can serve as a powerful entry point into the basics of computer science, differentiated instructional design, and responsible AI integration, all without a single line of code. Participants will engage in a fast-paced, team-based monster-replicating challenge that brings the foundational computer science concepts of input → process → output to life. Working in teams of three, each participant takes on a rotating role, builder, describer, and reconstructor, experiencing firsthand how clarity, sequencing, and precision directly shape outcomes. The debrief surfaces natural connections to algorithms, debugging, and the kind of communication skills that underpin both strong writing and strong computational thinking.
The session is intentionally designed as a low-floor, high-engagement experience accessible to all educators, regardless of CS background. No prior knowledge of computer science is assumed, and no technology is required for the core activity. At the same time, it offers high ceilings and wide walls, the same monster build that introduces algorithms to a kindergartener can spark rich conversation about instructional equity, learner variability, and the design of inclusive learning environments for seasoned educators.
A central focus of the second part of this session is modeling how AI can help teachers rapidly differentiate instruction without starting from scratch. Participants revisit the monster challenge through AI-generated variations of the same activity, each designed to support different learner needs. This allows educators to experience firsthand how small shifts in language, structure, and scaffolding can significantly impact access and outcomes. The facilitator then pulls back the curtain, sharing the AI prompts used to generate these adaptations and walking the group through the prompting process live. Key strategies are discussed, including how to write prompts grounded in specific learning goals, how to describe a learner profile with enough clarity to generate useful responses, and how feedback loops between teacher, student, and AI can refine both instruction and output over time.
The final segment of the workshop shifts the lens from teacher-facing to student-facing AI use. Participants explore how to model AI as a tool that strengthens student thinking, communication, and creativity rather than replacing it. Through a playful, game-based experience, educators see how structured prompting and iterative feedback can help students clarify their ideas, strengthen their writing, and deepen engagement. This also offers a natural ELA extension through storytelling and descriptive writing.
Throughout the session, participants will experience strategies for differentiation, collaboration, and student-centered facilitation that honor diverse learners and multiple entry points. Participants will leave with a ready-to-use unplugged CS activity adaptable across grades K–5, a live-modeled AI prompting framework for generating differentiated lesson materials, and a student-facing AI approach they can bring back to their classrooms right away. Most importantly, they will leave with increased confidence that CS and AI integration does not require a technology background, it requires curiosity, strong instructional design, and a willingness to build a monster.
