ClassSignal

ClassSignal is a classroom feedback tool built for teachers to collect anonymous student signals (understand, question, lost) during live lessons. The tool allows students to respond via QR or short code without account creation, and provides real-time aggregation of responses. A key feature is the use of GPT-5.6 to analyze these signals and generate structured confusion maps that suggest teaching interventions. The author states this was built for the OpenAI 2026 hackathon.

The product appears to be a prototype or proof-of-concept with no evidence of revenue, customers or adoption beyond the author's own demonstration. It is not evidenced whether ClassSignal has been used in real classrooms or tested with actual teachers and students. The description states it was built by one person (jersonmejia452-hue Mejía) and includes no mention of funding, partnerships, or commercial traction.

The single most important open question: Has ClassSignal been tested in real classrooms, and if so, what were the outcomes?

The description states that ClassSignal is a classroom feedback tool. It allows teachers to create a course, open a live class, and share a short code or QR.

Students participate anonymously from their phones without creating an account. They select one of three signals: "I understand", "I have a question", or "I'm lost". Students may also describe what is confusing them in writing.

Teachers receive these signals in real time, can compare multiple pulses during the class, moderate written questions, and share selected doubts on an anonymous student wall.

The system uses GPT-5.6 to transform each pulse into a structured confusion map, identifying concepts causing difficulty and proposing teaching interventions or publication drafts. The teacher always reviews the AI output before using or publishing it.

The author states that ClassSignal was inspired by the problem of students staying silent when confused due to fear of judgment or embarrassment. The positioning is that it makes asking for help feel safe and helps teachers notice confusion before moving on.

The claim evolution shows a progression from identifying a classroom problem (quiet classrooms hiding confusion) to building a solution that addresses this through anonymous feedback, real-time aggregation, and AI-powered analysis.

The author claims the tool provides useful information without connecting student identity to their classroom signal. The positioning is centered on safety, visibility of confusion, and teacher empowerment through data-driven insights.

The description states that ClassSignal is designed for teachers who want to understand student comprehension during live lessons. It targets educators in educational settings where students might be hesitant to ask questions aloud.

The primary customer is described as a teacher conducting live classes. The tool is built for use in classrooms, with students participating via mobile devices.

No evidence of specific customer segments beyond "teachers" or "educators" is provided. There is no indication of whether the target includes K-12 teachers, university professors, corporate trainers, or other educational roles.

Not evidenced. The description does not contain any information about pricing, monetization strategies, or business model elements beyond the author's own account of building it for a hackathon.

The product is built with React, TypeScript, Vite, Tailwind CSS, and Zod for the frontend. Supabase provides backend services including authentication, PostgreSQL database, Realtime updates, Row Level Security, and Edge Functions.

Anonymous submissions are processed through Supabase Edge Functions using Cloudflare Turnstile for protection against abuse, rate limiting, and per-pulse pseudonymous identifiers.

The AI features use OpenAI's API with GPT-5.6 and structured outputs. Student names, emails, account information, and anonymous identifiers are never sent to the model. AI generation only happens after an explicit teacher action.

The author states that for a Cálculo Diferencial demonstration, ClassSignal includes 6 classes, 18 historical pulses, 536 anonymous responses, and 132 written doubts. This suggests some level of internal testing or demonstration but no evidence of external adoption.

The product is described as a prototype built for a hackathon (OpenAI 2026). There is no evidence of revenue, customers, or adoption beyond the author's own account.

Not evidenced. The description does not mention any existing competitors or competitive landscape in the classroom feedback or educational technology space.

The product appears to be a prototype built by one person for a hackathon with no evidence of commercial traction, revenue, or customer adoption. The author states that the greatest challenge was protecting anonymity without turning the public response endpoint into an abuse vector, suggesting potential technical and security concerns.

There is no evidence of testing in real classrooms or feedback from actual teachers and students. The product's maturity level is unclear beyond being a hackathon submission.

The description mentions that AI generation only happens after explicit teacher action, which may limit its utility if teachers don't consistently engage with the AI features.

1. Has ClassSignal been tested in real classrooms? If so, what were the outcomes and feedback?
2. What is the current state of the product beyond the hackathon prototype?
3. Are there any existing partnerships or pilot programs with educational institutions?
4. How does the system handle potential abuse or spam from anonymous submissions?
5. What are the plans for scaling beyond a single developer's capacity?
6. Has there been any market research or user validation with teachers and students?