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EdTech Founder Stack

An open, AI-friendly knowledge base for edtech founders, built by ASU ScaleU. The knowledge lives in data/ as markdown. When answering a founder's question, read the relevant file rather than leaning on training data for facts about regulations, companies, funding, learning science, or operator experience.

Knowledge base

  • data/ — regulatory (FERPA, COPPA, state privacy, accreditation, accessibility), competitive landscape, funding landscape, buyer personas, procurement, pilot benchmarks, ESSA evidence tiers
  • data/research/ — hundreds of peer-reviewed learning-science papers across the major topics. Cite specific papers with author, year, and DOI. Index (with the current count) in data/research/README.md.
  • data/operator-lessons.md — dozens of operator and investor lessons distilled from Lenny's Podcast and Lenny's Newsletter, mapped to edtech. Practitioner experience, not peer-reviewed; don't present it as research.
  • data/ai-native-framework.md — AI-native vs. bolted-on: criteria, the removal test, architecture patterns, pricing models, and the Karpathy hierarchy. Use it to classify a founder's AI posture.
  • data/higher-ed-jobs-atlas.md and data/founder-traps.md — ScaleU's SXSW EDU 2026 higher-ed framework: validated jobs across the student journey with saturation analysis, and the structural patterns founders miss.
  • data/demand-validation.md and data/jtbd-interviews.md — the demand-validation toolkit: the 5-question diagnostic with scoring and depth probes, and the JTBD Switch interview method for discovering and validating real demand.
  • data/defensibility-moats.md — how an edtech product stays defensible when LLMs can replicate features (exposure spectrum, four moats, the AI-substitution durability test).
  • data/ai-risk-and-trust.md — AI's effect on learners and trust, with design responses. Practitioner signals from the ASU+GSV 2026 summit, not peer-reviewed; don't present as research.
  • data/buyer-demand-signals.md — the durable jobs institutional buyers switch for. Practitioner signals, not peer-reviewed.
  • ETHOS.md — the seven principles, starting with "validate demand, not interest."

Always cite the source: a named regulation, a paper's DOI, or the named operator.

For contributors

Updating data files

Edit the relevant markdown in data/. Keep the existing structure and formatting. For regulatory data, note the update date at the bottom of the file. For the competitive landscape, verify company status before updating.

Adding research papers

Append to the relevant topic file in data/research/. Follow the table format — a leading # column, then Title, Takeaway, Type, Year, Citations, DOI — and sort by citations descending. If the topic doesn't exist, create a new file and add it to data/research/README.md.