PLAYBOOK ATLASPlaybook Atlas

Education

AI in education: explore 36 applications.

Support teaching, feedback and student services with evidence educators can review.

Explore lesson preparation, tutoring, assessment review and student-support workflows for educators and education administrators.

Plan your first AI move See the 36 applications

Where the 36 applications sit

Explore the domains
  1. 17Student Support
  2. 9Instruction Delivery
  3. 5Assessment & Evaluation
  4. 3Research
  5. 2Curriculum Development

01Why AI now

Why education is moving on AI, in three figures.

Each figure links to its source.

  1. AI in education: over $20B by 2027

    Global Market Insights forecast that the global AI-in-education market would exceed $20 billion by 2027.

    Source: PR Newswire

  2. Global teacher shortfall: 44M by 2030

    UNESCO projects that 44 million additional primary and secondary teachers will be needed worldwide to achieve universal education by 2030.

    Source: UNESCO

  3. 74% of U.S. schools struggled to fill vacancies

    Seventy-four percent of U.S. public schools reported difficulty filling at least one teaching vacancy with a fully certified teacher before the 2024–25 school year.

    Source: Institute of Education Sciences / National Center for Education Statistics

How to evaluate a first project

Define the learner group, learning objective and educator’s decision. Evaluate feedback against a reviewed rubric and include accessibility, missing context and incorrect answers. Test learning or intervention outcomes separately from predictive accuracy, engagement or the number of materials generated.

02Capability map

Where education puts AI to work, domain by domain.

Pick a domain to see its applications. Each one opens a full report.

Inside Student Support

Applications in Student Support

What the use cases ask AI to do

Share of the education use cases, by the kind of work the AI does.

  1. Reasoning

    Analyzes, predicts and decides: scoring, forecasting, classifying.

    100%

Under 1% each: perception, generation, optimization and agentic.

03Workflow ideas

Ways AI could change the work, in education.

Atlas groups these applications by the changes they propose to a workflow. These preliminary classifications help you explore ideas; each application still needs a review of its sources and operating requirements.

Workflow categories in this catalog

Share of the 31 applications assigned a category.

How to evaluate an application

  1. Start with a bounded task

    Identify the inputs, the person responsible, and the decision or output the workflow must produce.

  2. Check the support for each method

    Look for source-backed details about the specific implementation, including its limits and human review.

  3. Measure the pilot

    Record the current baseline, agree on acceptance criteria, and test on representative cases before expanding.

Explore applications

04What gets built

The approaches education uses most, and when to skip them.

Knowing when not to use one matters as much as knowing when to.

  1. 01 · 11 applications

    Workflow Automation

    Use AI inside orchestrated workflows to automate multi-step business processes.

    Use it when

    • When you have repetitive, rule-heavy workflows that still require human judgment or document understanding.
    • When the process spans multiple systems (ERP, CRM, ticketing, email) and manual coordination is slow or error-prone.
    • When large volumes of semi-structured or unstructured data (emails, PDFs, forms) must be processed consistently.

    Skip it when

    • When the process is highly creative, exploratory, or non-repeatable, with no stable sequence of steps.
    • When decisions are extremely high-stakes (e.g., life-critical medical decisions, major legal judgments) and regulations require full human control.
    • When you lack reliable access to the necessary systems or data sources to complete the workflow end-to-end.
    Workflow Automation, in depth

  2. 02 · 6 applications

    RAG-Standard

    LLM + retrieval layer that injects relevant documents into the prompt at query time.

    Use it when

    • You need an LLM to answer questions based on proprietary or private documents that are not part of its pretraining data.
    • Your knowledge changes frequently (e.g., policies, product docs, pricing) and you want updates to be reflected without re-training the model.
    • You have a large corpus of unstructured or semi-structured text (wikis, PDFs, tickets, emails, manuals) that users struggle to search effectively.

    Skip it when

    • Your task is primarily structured data querying or analytics (e.g., reporting over a relational database) where SQL or BI tools are more accurate and efficient.
    • You have very small, static knowledge (e.g., a short FAQ) that can fit directly into a prompt or be encoded as rules without a retrieval layer.
    • You require strict, deterministic behavior with no tolerance for probabilistic errors (e.g., certain financial calculations, safety-critical controls).
    RAG-Standard, in depth

  3. 03 · 5 applications

    Generative AI

    Models that learn data distributions to generate new text, images, code, and media.

    Use it when

    • When you need to generate natural language, code, or media that would be expensive or slow for humans to produce manually.
    • When tasks involve ambiguity, open-ended questions, or creative exploration rather than strict right-or-wrong answers.
    • When you can ground the model on reliable internal or external data sources to improve factuality.

    Skip it when

    • When the task requires strict determinism, formal guarantees, or regulatory-grade traceability (e.g., certain financial calculations, safety-critical control systems) without a robust verification layer.
    • When you lack access to high-quality, authoritative data or subject-matter expertise to validate outputs in a high-risk domain.
    • When a simple rules engine, search, or template-based system can solve the problem more reliably and cheaply.
    Generative AI, in depth

05The applications

36 education applications, each with a full report.

The widest first, by the number of use cases each covers. Every report shows what changed, how it works, who has done it and what can go wrong.

  1. Student Success Risk Prediction ModelAI that identifies at-risk students before they fail or drop out. These systems analyze academic and behavioral data to forecast struggles, explain root causes, and recommend interventions—adapting to each learner. The result: higher retention, closed achievement gaps, and personalized support at scale.React → Pred23 use cases
  2. AI-Assisted Education Evaluation ReviewAI-supported workflows for structured review, validation, and monitoring of education programs, learning tools, and training evidence, using standardized rubrics, supporting-document checks, human oversight, and performance tracking to improve consistency, compliance, and release confidence.Expert → AI10 use cases
  3. Computational Drug Discovery Lab Workflow InstructionSupports hands-on computational science labs for drug discovery education with reusable notebooks and reproducible pipelines for HIV protease bioactivity modeling, molecular dynamics trajectory analysis, protein-ligand docking, virtual screening, molecular modeling, and data curation.Expert → Platform8 use cases
  4. Rubric-linked Feedback for Teacher ReviewA student reaches the right conclusion but skips the explanation your rubric requires. Prepare feedback that shows exactly what the work supports, what is missing, and what to try next. Teachers can review draft comments, check similar-answer groups, resolve disagreements and answer regrade requests while retaining the scoring decision.Generative-Content7 use cases
  5. Student Risk Early Alert MonitorMonitors student progress signals such as participation, alerts, surveys, and support indicators to identify at-risk students early and help advisors and faculty coordinate timely interventions.Expert → AI4 use cases
  6. Academic Progress Early Alert and Completion Risk MonitoringMonitors midterm grades, LMS engagement, and required-course bottlenecks to identify academically at-risk students early, predict on-time completion risk, and support coordinated advisor and student support interventions.Expert → AI3 use cases
All 36 applications, A to Z

Work with us

The catalog shows what is possible. A sprint decides what you should fund.

Reading every report still leaves the hardest question open: which move is worth funding first, here, given your constraints. That is the sprint: two weeks, fixed scope, and a candid answer either way.

Book a 30-minute callHow the sprint works

Two weeks Fixed scope A candid fit-or-no-fit answer

  1. 01

    Three ranked opportunities

    A decision-ready shortlist scored against value, feasibility, evidence, risk, and time to proof.

  2. 02

    One pilot-ready workflow

    A bounded operating design with actors, handoffs, data needs, controls, and success measures.

  3. 03

    A defensible 90-day plan

    Build-versus-buy guidance, ROI assumptions, risk treatment, sequencing, and executive decisions.

06Rules & standards

Rules and standards to check, with their scope and sources.

Selected US and EU references for these workflows. Applicability depends on jurisdiction, organization, data and intended use. The planning notes are Atlas editorial guidance; they are not official implementation-time estimates or an exhaustive list of obligations.

  1. FERPA · education records and service providers

    Law or regulation

    United States · FERPA-covered educational institutions

    Using the school-official exception for an outside service requires conditions including institutional control over record use and restrictions on redisclosure. A vendor contract alone does not establish every condition.

    US Department of Education: school officials under FERPA

    Source checked

    Project planning note

    Document the disclosure basis, vendor purpose, institutional control and permitted reuse of student records.

  2. COPPA · children’s online privacy

    Law or regulation

    United States · covered online services involving children under 13

    COPPA covers child-directed commercial services and certain operators with actual knowledge that they collect children’s personal information. Check the amended Rule, parental-consent requirements and applicable exceptions for the actual service.

    FTC: Complying with COPPA

    Source checked

    Project planning note

    Check the amended Rule for the age group, service and consent arrangement before a student pilot.

Your next step

Find your first move in education.

Ask the workspace which move fits your company. Add your size, systems and constraints, and it works from these 36 applications to a plan your team can act on.

Plan your first AI move

Rather talk it through? Book a 30-minute call