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Awesome AI for Education Awesome

A human-curated, evidence-backed field guide to AI that improves educational practice.

AI for education, not education about AI.

English | Chinese

Human-reviewed | Evidence-labeled | Bilingual

Contents

Teaching and Lesson Design

Assessment and Feedback

  • Brisk Teaching - Helps teachers create feedback, assessments, differentiated materials, translations, and student activities inside existing browser workflows; vendor case study.
  • Gradescope - Helps instructors grade paper, online, and programming assignments with answer grouping, rubrics, and reusable feedback; public deployment.
  • Writable - Helps teachers manage writing assignments, rubrics, feedback, and revision cycles; product documentation.

Tutoring and Learner Support

Content and Curriculum

  • Diffit - Helps teachers adapt source material into differentiated readings, vocabulary support, worksheets, translations, and handouts; public deployment.

School Operations and Governance

Teacher Development and Coaching

Open Source

  • Shiori - Gives learners Google Classroom sync, study plans, quizzes, and spaced-repetition flashcards in an MIT-licensed, self-hostable study companion; open source review.

Open source is a distinct editorial layer, not a requirement for the whole guide. Code availability makes a project inspectable, but it does not prove educational impact.

Field Evidence

  • Ottawa Catholic School Board + Brisk - Describes a district-scale rollout inside Google Workspace for feedback, personalized support, and multilingual access. Vendor-reported case study.
  • Barbers Hill ISD + Brisk - Describes district adoption for targeted feedback, student activities, translation, text leveling, and differentiated support. Vendor-reported case study.
  • Ysleta Middle School + Curipod - Describes writing feedback and classroom participation with emergent bilingual learners. Vendor-reported case study.

These records show implementation settings, not independent endorsements. Vendor ownership is stated so readers can judge each source appropriately.

Watchlist

Watchlist is an active review queue, not a lower-quality directory. A resource moves only when public evidence changes.

How Curation Works

Every Main List decision is made by a human maintainer. AI may help discover candidates, gather sources, or draft copy, but it may not approve an entry.

Each selected resource must show a concrete education workflow, a clear target user, and public evidence. Evidence labels describe source ownership and strength without turning uncertain evidence into a numeric score. Vendor case studies stay visibly vendor-reported, and resources move between Main List and Watchlist as evidence changes.

The public evidence ledger records sources, limitations, and review dates. The full curation criteria define admission, movement, and removal. General AI tools, AI-literacy courses, and paper-only collections stay out unless they connect to a practical education implementation.

Contributing

Educators, researchers, builders, and school teams are welcome to challenge entries, add evidence, and suggest resources. English-only submissions are welcome; maintainers are responsible for final bilingual parity.

Read CONTRIBUTING.md, then suggest a project or suggest field evidence. Review the curation criteria and evidence ledger above before submitting.

Released under the MIT License.

Releases

Packages

Contributors