As an educator and leader in K-12 computer science, I have always sought tools that enable meaningful innovation while keeping teachers and students at the center of every decision. Over the past month, I have leaned into AI, not as a replacement for expertise but as a collaborator that challenges, amplifies, and refines my ideas.
The process of working with AI is not passive. It begins with thoughtful inputs: clear goals, strategic prompts, and a vision for the desired outcome. What follows is a back-and-forth dialogue where I critique, shape, and push for better results. My expertise is not just useful in this process; it is essential.
Here are some of the ways AI has supported my recent projects. The inputs I provided, and the decisions I made along the way, were critical to achieving meaningful outcomes.
The Input: I needed to create a session to help educators integrate AI into STEM education. The goal was to balance the efficiency AI offers with the need for hands-on conceptual learning. I provided specific objectives: create an outline that contrasts AI-driven tools with traditional teaching methods, emphasizes hands-on coding activities, and includes educator takeaways.
The Process: ChatGPT generated an outline comparing AI website generators with manual HTML coding. While helpful, it lacked depth on the pedagogical implications. I refined the prompts, asking for questions educators might have about balancing AI with foundational learning.
The Output: After refining several drafts, I finalized a session plan that tackles the nuances of AI’s role in classrooms. The plan empowers educators to think critically about where and how to use these tools. My expertise ensured the session would resonate with real-world classroom challenges.
The Input: I wanted to articulate why hands-on learning is critical, even in the age of AI, and connect Ellipsis’ curriculum to a larger body of educational research. I shared specific examples from our curriculum, like hand movements and simple dances used to teach algorithms, and asked ChatGPT to tie these activities to research supporting hands-on learning.
The Process: ChatGPT synthesized educational research, highlighting how tactile and kinesthetic activities deepen comprehension. While the theory was sound, I refined the input to better align research findings with our examples. The goal was to emphasize how these activities help students internalize abstract concepts like algorithms, sequences, and loops.
The Output: The final argument framed hands-on learning as essential to conceptual mastery in computer science. The narrative connected Ellipsis’ curriculum to established research, making a compelling case for why these methods are foundational.
The Input: I set out to develop a logic model for a K-12 digital literacy and computer science initiative. The goal was to align its vision, activities, and intended outcomes. I provided ChatGPT with information about the initiative’s goals, including improving academic achievement and career readiness, and asked for a framework that tied activities to measurable outcomes.
The Process: ChatGPT drafted a logic model that organized inputs, activities, outputs, and outcomes into a structured table. While helpful, it needed stronger connections between activities and outcomes. For example, I wanted to link teacher training to improved student engagement. I refined the prompts, asking for clearer pathways and actionable short-term and long-term outcomes.
The Output: The final logic model clearly mapped activities to measurable goals, such as:
The process ensured the framework was both practical and aligned with the initiative’s vision.
The Input: Preparing for an upcoming pilot of our updated curriculum, I wanted teacher materials that clearly explained its new features. I shared details about thematic units and differentiated assessments and asked ChatGPT to organize these into a concise overview.
The Process: ChatGPT’s initial draft summarized the features but needed a stronger focus on their benefits. For example, I wanted more emphasis on how thematic units support learning progressions and how frequent assessments provide actionable insights. I refined the prompts to highlight the practical value of these updates.
The Output: The final overview distilled the updates into clear, teacher-friendly messaging, such as:
The materials equipped teachers to approach the pilot with confidence, ensuring the curriculum’s strengths were communicated effectively.
What excites me most about working with AI is not just how it boosts productivity. It invites me to think critically and creatively. At every step, my inputs, critiques, and expertise shaped the results.
I have found that using AI invites me to:
As I wrote in my blog, “Navigating AI in the Classroom: A Journey of Discovery,” AI is not a replacement for the human element. It is an amplifier of human potential. This partnership allows me to focus on what matters most: empowering teachers and inspiring students.
At Ellipsis Education, we believe technology should enhance, not replace, the transformative role of educators. As we continue to create tools and resources that inspire classrooms, AI will remain a valuable partner in refining and amplifying the work that drives real impact.
If you are curious about how AI can elevate your work, or if you are passionate about advancing K-12 education, let’s connect. Together, we can shape the future of teaching, learning, and innovation.
