For our AI Strategy Lab group project, our team developed “School IRL”, a concept designed to turn a student’s everyday surroundings into interactive learning moments. The core premise is straightforward: by pointing a standard smartphone camera at common physical objects—such as a bridge, a supermarket shelf, or a tree along the street—the application identifies the scene with computer vision and connects it directly to curriculum-aligned micro-lessons. Within our four-person team, my primary individual contributions centered on two foundational deliverables: Economics (cost structures, scalability, and pricing logic) and the Future-Market Test (assessing strategic resilience in an agent-dominated ecosystem).
My initial responsibility was building out the venture’s economic model. During our early ideation sessions, the team naturally fell into a conventional software mindset, assuming that once the application was built, serving additional students would incur virtually zero incremental expense. However, when I sat down to calculate the back-end unit economics of multimodal visual recognition and real-time lesson generation, reality hit hard. Modern vision-language models and generative outputs carry non-zero marginal inference costs. I ran a concrete baseline calculation: assuming an active student completes three neighborhood exploration quests a week and scans three to five items per quest, a single active user easily triggers dozens of multimodal API calls each month. If we offered unlimited free access to drive growth, our cloud compute bill would expand exponentially with user activity, destroying our unit economics from day one.
This cost realization marked our first critical pivot. It disproved our initial inclination toward a purely freemium consumer model and led me to restructure our monetization logic. I proposed that we shift to a B2B2C institutional model, packaging School IRL as a supplemental curriculum tool sold to schools and school districts through per-student annual SaaS licensing. For direct consumers, we introduced a capped monthly allowance of free scans, after which users purchase token packages. On the technical side, I also recommended caching common local objects—such as neighborhood landmarks, common trees, or standardized supermarket packaging—so the application serves pre-validated curriculum lessons instead of triggering expensive inference calls for identical items. This shifted our financial foundation from a vague traffic-first strategy to an operationally defensible business model.
My second major responsibility was executing the future-market test, which pushed us to evaluate how School IRL would survive if students, teachers, and competitors all had autonomous AI agents. Carrying out this simulation revealed a major vulnerability that nearly compromised our core value proposition. If every student soon has a capable multimodal AI agent running locally on their device, our planned learning loop—capturing an object, receiving questions, answering, and logging progress—becomes trivial to bypass. A student could point their camera, and their personal agent could intercept the prompt and generate correct answers in seconds, completely removing the student from the cognitive loop. If the software could not guarantee real student engagement, schools would have zero incentive to pay for it.
This failure analysis forced us to redesign our task format. We realized that trying to outsmart digital agents with text-based multiple-choice quizzes was pointless; our defensibility had to come from embodied, physical interaction in the real world. We restructured the lesson tasks to require situated observations that cannot be scraped from pure pixels. For instance, instead of asking an abstract economics question about supply and demand when looking at a supermarket shelf, the prompt requires the student to compare two specific packages of milk on the same shelf, calculate the price-per-liter difference on the spot, and enter that observed metric. When inspecting a bridge for physics, the task prompts the student to walk to the side support and observe the structural angle. By tying completion to physical observation, we raised the barrier against remote automated cheating and preserved the product’s instructional integrity.
Regarding our overall project scope, the team reached an early consensus: focus strictly on education, but avoid overcomplicated technologies. During initial brainstorms, we briefly discussed using immersive AR glasses or wearable sensors, but quickly dismissed them. In real educational environments, resource distribution is highly unequal; requiring specialized hardware would immediately price out underfunded public schools. Furthermore, advanced hardware creates steep training requirements for teachers, adding friction that stalls classroom adoption before it even begins. By deliberately choosing the smartphone already in a student’s pocket and focusing on common neighborhood objects, we ensured that any student within a short walk could access learning materials. This taught me that strategic competence is not about picking the most complex technology, but about achieving technology-tasks fit within real operational constraints.
From a collaboration standpoint, this project had an especially strong impact on me as an exchange student. In my home education background, learning is traditionally centered around lecture-based instruction, where teachers lecture from the front and students passively absorb notes. Coming from that setting, I had never considered how dynamic and contextualized learning could be simply by turning everyday street objects into interactive lessons through a smartphone. Working with team members from different backgrounds expanded my perspective on experiential and student-centered learning. This project was more than an academic exercise for credits; when I return home, I look forward to discussing these ideas with friends who teach, exploring how contextualized AI tools might reduce their lesson prep while making learning more engaging for students.
Overall, the AI Strategy Lab reshaped how I think about technology and management. I learned that developing a business strategy is never about presenting a flashy prototype; it requires calculating the underlying marginal compute costs, respecting operational constraints, and designing defenses against future technological shifts. This experience helped me step into the role of a strategic manager—one who uses economic principles and grounded judgment to turn technological capabilities into sustainable, real-world value.
