Context Engineering for Educators

Master the Art of AI Context Design for Educational Excellence

Section 1 of 9

← → navigate sections  ·  Tab moves within a section

Welcome to Context Engineering

In 2025, the landscape of AI interaction has fundamentally shifted. While everyone was focused on crafting perfect prompts, the real breakthrough emerged in context engineering - the art and science of orchestrating the entire information environment that AI models operate within.

Why Context Engineering Matters for Educators

As educators, you're not just users of AI - you're architects of learning experiences. Context engineering empowers you to create reliable, scalable, and pedagogically sound AI applications that enhance rather than replace human teaching.

What You'll Learn

1

Conceptual Foundation

Understand the fundamental shift from prompt engineering to context engineering

2

Core Principles

Master the four pillars: Writing, Selecting, Compressing, and Isolating context

3

Educational Applications

Apply context engineering to real classroom scenarios and curriculum design

4

Practical Implementation

Build hands-on experience with interactive exercises and frameworks

Important: Context engineering isn't just a technical skill - it's a pedagogical strategy that transforms how AI can support learning at scale while maintaining educational integrity and personalization.

♿ Accessibility Is Part of the Context

Whenever AI generates HTML, web pages, or digital course materials, your context must include explicit WCAG 2.1 (Web Content Accessibility Guidelines) requirements. Without them, AI-generated output routinely fails Section 508 of the Rehabilitation Act and the ADA — producing low-contrast colors, missing alt text, and inaccessible interactive elements. This workshop will show you exactly where and how to include these requirements in every context you build.

Context Engineering vs. Prompt Engineering

Understanding the distinction between these approaches is crucial for modern educators leveraging AI in their practice.

🎯 Prompt Engineering

  • ✍️ Crafting individual prompts
  • 🔄 One-shot interactions
  • 🎨 Focus on clever wording
  • 📝 Static instructions
  • 🔍 Trial and error approach
  • ⚡ Quick demos and prototypes

🏗️ Context Engineering

  • 🔧 Building dynamic systems
  • 🔄 Multi-turn conversations
  • 📊 Managing entire context windows
  • ⚙️ Runtime information assembly
  • 🎯 Production-ready solutions
  • 🏫 Scalable educational applications

The Context Engineering Mindset

"Think of an LLM like a CPU, and its context window as RAM. Your job as a context engineer is like an operating system - load that working memory with just the right code and data for the task." - Andrej Karpathy

Why This Matters in Education

Consider This Scenario:

Prompt Engineering Approach: "You are a helpful math tutor. Help this student with algebra."

Context Engineering Approach: A system that dynamically loads the student's learning history, current curriculum standards, identified misconceptions, preferred learning style, recent assessment results, and relevant examples - all formatted optimally for the AI to provide personalized, pedagogically sound assistance.

🎓 Educational Impact

Context engineering enables AI to understand not just what a student is asking, but their learning journey, capabilities, and educational context.

📈 Scalability

While prompt engineering creates one-off solutions, context engineering builds systems that work reliably across hundreds or thousands of students.

🔒 Reliability

Context engineering reduces hallucinations and inappropriate responses by providing comprehensive, accurate information to the AI.

♿ Accessibility Compliance

Context engineering is the mechanism for ensuring AI-generated HTML meets WCAG 2.1 Level AA. A prompt says "build a page." A context specifies the exact contrast ratios, ARIA roles, semantic structure, and alt text requirements that make that page legally and educationally accessible.

The Four Pillars of Context Engineering

Master these foundational practices to build production-grade AI applications for education.

✍️

Writing Context

Capturing and storing relevant information for future use, like a student taking notes during problem-solving.

🎯

Selecting Context

Choosing the most relevant information from vast knowledge bases based on the current educational task.

🗜️

Compressing Context

Condensing information into efficient formats while preserving educational value and meaning.

🔒

Isolating Context

Preventing context contamination and maintaining focus on the current learning objective.

Detailed Exploration

1. Writing Context in Education

Purpose: Ensure AI systems remember and build upon previous educational interactions.

Example: An AI tutoring system that tracks a student's progress through calculus, noting which concepts they've mastered, which they struggle with, and what teaching methods work best for them.

Implementation: Store student responses, learning preferences, successful pedagogical approaches, and misconception patterns.

2. Selecting Context in Education

Purpose: Retrieve the most relevant educational information for the current learning task.

Example: When a student asks about photosynthesis, the system selects their biology level, previous chemistry knowledge, learning style preferences, and relevant curriculum standards.

Implementation: Use semantic search, knowledge graphs, and educational taxonomies to retrieve contextually relevant information.

3. Compressing Context in Education

Purpose: Fit essential educational information within AI context limits while maintaining pedagogical effectiveness.

Example: Summarizing a student's learning journey: "Student shows strong visual learning preference, has mastered basic algebra but struggles with word problems, responds well to real-world applications."

Implementation: Create hierarchical summaries, use educational abstractions, and prioritize pedagogically relevant information.

4. Isolating Context in Education

Purpose: Prevent information from different students, subjects, or learning objectives from interfering with each other.

Example: Ensuring that strategies used for teaching advanced students don't inappropriately influence AI responses to struggling learners.

Implementation: Use clear context boundaries, session management, and educational role-based access controls.

♿ Applying the Four Pillars to Accessibility

The same four pillars apply directly to producing WCAG 2.1 AA compliant output:

  • Writing: Store your institution's approved accessible color palette (web-safe hex values with verified contrast ratios) so the AI reuses them consistently.
  • Selecting: Pull the relevant WCAG 2.1 Success Criteria for the content type being generated — different criteria apply to video (SC 1.2.2), color (SC 1.4.3), and keyboard navigation (SC 2.1.1).
  • Compressing: Summarize your accessibility requirements into a reusable block rather than re-explaining them from scratch every session.
  • Isolating: Keep accessibility requirements in their own named section of your context so the AI treats them as non-negotiable constraints, not stylistic suggestions.

Educational Applications of Context Engineering

Transforming Educational Scenarios

🎯 Personalized Learning Assistants

AI tutors that understand each student's learning history, preferences, and current needs to provide tailored instruction.

📝 Intelligent Assessment Systems

Automated grading that considers context like assignment rubrics, learning objectives, and individual student progress.

🔍 Curriculum Development Tools

AI that helps design courses by considering institutional standards, student demographics, and pedagogical best practices.

💬 Student Support Chatbots

AI assistants that provide accurate information by accessing relevant institutional policies, academic calendars, and student records.

Case Study: The Adaptive Literature Discussion Bot

Challenge: Create an AI that can facilitate meaningful literature discussions for students at different academic levels.

Context Engineering Solution:

  • Writing: Track each student's analytical depth, discussion style, and literary knowledge base
  • Selecting: Pull relevant text passages, critical perspectives, and appropriate discussion questions based on student level
  • Compressing: Summarize key themes and previous discussions while maintaining nuance
  • Isolating: Ensure advanced students' insights don't spoil discoveries for beginning readers

Subject-Specific Applications

🧮 Mathematics

Context: Student's mathematical foundation, problem-solving strategies, common error patterns

Application: Adaptive problem generation with scaffolded hints

🔬 Science

Context: Lab experience, conceptual understanding, safety protocols, equipment availability

Application: Virtual lab guidance with safety-aware instructions

📚 Language Arts

Context: Reading level, cultural background, writing goals, literary exposure

Application: Culturally responsive writing feedback and reading recommendations

🎨 Creative Arts

Context: Artistic background, medium preferences, creative goals, inspiration sources

Application: Personalized project suggestions and technique tutorials

Ethical Considerations in Educational Context Engineering

  • Privacy: Ensure student data is protected and used only for educational benefit
  • Bias: Regularly audit context selection to prevent reinforcement of educational inequities
  • Transparency: Students and educators should understand how AI systems use contextual information
  • Agency: Maintain human oversight and student autonomy in learning decisions
  • Accessibility: All AI-generated digital materials must comply with WCAG 2.1 Level AA and Section 508. Include explicit color contrast requirements (minimum 4.5:1 for normal text), ARIA landmark and role specifications, alt text requirements, and keyboard navigation rules in every context that may produce HTML or web content. This is a legal obligation under the ADA, not an optional enhancement.

Before & After: Transforming Prompts into Context Engineering

See how standard educational prompts can be transformed into powerful context-engineered solutions that deliver reliable, personalized, and pedagogically sound results.

Example 1: Mathematics Tutoring Assistant

❌ Standard Prompt Approach

"You are a helpful math tutor. Help students solve algebra problems. Be encouraging and explain step by step."

Problems:

  • No knowledge of student's skill level
  • Can't adapt to learning style
  • No awareness of common mistakes
  • Generic responses for all students

✅ Context Engineering Approach

CONTEXT LAYERS:

Student Profile: Alex, Grade 9, struggles with negative numbers, visual learner, confident with basic operations, previous misconception: thinks (-x)² = -x²

Learning Objectives: Master quadratic equations, understand order of operations with negatives

Pedagogical Strategy: Use visual representations, provide worked examples, check for negative number misconceptions before proceeding

Session Context: Third session on quadratics, previous sessions covered factoring basics, student successfully solved x² + 5x + 6 = 0

Response Framework: Always ask "What do you think happens first?" before showing steps, use graphical representations when possible, immediately address if negative signs are handled incorrectly

Benefits:

  • Personalized to Alex's specific needs
  • Builds on previous learning
  • Proactively addresses known misconceptions
  • Uses preferred learning modality

Example 2: Essay Feedback System

❌ Standard Prompt Approach

"Grade this essay and provide feedback. Focus on grammar, structure, and content. Be constructive."

Problems:

  • No rubric or standards reference
  • Ignores assignment requirements
  • Can't adjust for student level
  • No consideration of learning goals

✅ Context Engineering Approach

ASSIGNMENT CONTEXT:
Course: English 101, Assignment: Persuasive Essay on Environmental Policy
Word Limit: 750-1000 words, Due: Week 8 of semester

RUBRIC CRITERIA:
- Thesis clarity (25%): Must present arguable position
- Evidence quality (30%): 3+ credible sources required
- Organization (25%): Intro-body-conclusion with transitions
- Grammar/Style (20%): College-level writing conventions

STUDENT CONTEXT:
Maria, ESL learner (Spanish L1), second semester, strong ideas but struggles with article usage and complex sentence structure, previous essays show good research skills but weak thesis statements

FEEDBACK PROTOCOL:
1. Start with content strengths
2. Address thesis development specifically
3. Limit grammar feedback to top 2 patterns
4. Provide specific revision suggestions
5. Include one model paragraph example

Benefits:

  • Aligned with specific assignment goals
  • Considers student's language background
  • Builds on previous feedback patterns
  • Provides actionable improvement steps

Example 3: Research Assistant for Graduate Students

❌ Standard Prompt Approach

"Help me find sources for my research paper. Suggest relevant articles and studies."

Problems:

  • No disciplinary context
  • Unclear methodology standards
  • No timeline or scope guidance
  • Generic search suggestions

✅ Context Engineering Approach

RESEARCH CONTEXT:
Discipline: Educational Psychology, Degree: PhD (3rd year)
Topic: Digital literacy interventions in K-12 settings
Research Question: "How do gamified digital literacy programs affect reading comprehension in grades 3-5?"

METHODOLOGY REQUIREMENTS:
- Systematic review approach preferred
- Need experimental/quasi-experimental studies
- Publication window: 2018-2025
- Sample size minimum: n=50

INSTITUTIONAL STANDARDS:
- APA 7th edition citation required
- University access: PsycINFO, ERIC, JSTOR
- IRB considerations for future empirical work

CURRENT PROGRESS:
- 23 articles reviewed so far
- Strong representation of gamification research
- Need more studies specifically on reading outcomes
- Gap identified: limited research on long-term retention

Benefits:

  • Targeted to specific research parameters
  • Follows disciplinary conventions
  • Builds on existing progress
  • Identifies specific gaps to address

Example 4: Language Learning Conversation Partner

❌ Standard Prompt Approach

"Practice Spanish conversation with me. Correct my mistakes and help me improve."

Problems:

  • No proficiency level specified
  • Unclear correction preferences
  • No topic or goal structure
  • Missing cultural context

✅ Context Engineering Approach

LEARNER PROFILE:
Sarah, B1 intermediate, English L1, learning Mexican Spanish
Strengths: Vocabulary, basic grammar | Weaknesses: Subjunctive, pronunciation of rr
Goal: Conversational fluency for medical volunteering

SESSION PARAMETERS:
- Topic focus: Healthcare/medical scenarios
- Error correction: Recasts preferred over direct correction
- Complexity: Introduce 2-3 new structures per session
- Cultural notes: Include Mexican healthcare etiquette

CONVERSATION PROTOCOL:
1. Start with warm-up question about previous session
2. Role-play: Patient interaction scenarios
3. Introduce new medical vocabulary in context
4. Practice subjunctive with health recommendations
5. End with reflection on progress and next session goals

PROGRESS TRACKING:
Last session: Mastered body parts vocab, struggled with "Es importante que..." constructions

Benefits:

  • Tailored to specific learning goals
  • Appropriate challenge level
  • Culturally relevant content
  • Builds systematically on progress

Example 5: Laboratory Safety Assistant

❌ Standard Prompt Approach

"Provide lab safety guidance. Tell me about proper procedures and safety equipment."

Problems:

  • No specific lab or equipment context
  • Generic safety advice
  • No emergency protocols
  • Missing institutional requirements

✅ Context Engineering Approach

LAB ENVIRONMENT:
Organic Chemistry Lab, University of State, Room 204
Equipment: Fume hoods (8), rotary evaporators (4), heating mantles
Current experiment: Synthesis of aspirin (undergraduate level)

SAFETY PROTOCOLS:
- Required PPE: Safety goggles, lab coats, closed-toe shoes
- Chemical inventory: Acetic anhydride, sulfuric acid, salicylic acid
- Emergency procedures: Eye wash station (15 min), safety shower, fire extinguisher locations
- Waste disposal: Organic waste container, acid/base neutralization

STUDENT CONTEXT:
Second-year chemistry majors, completed safety training, previous experience with distillation
Common issues: Forgetting to check glassware, improper heating mantle use

INSTITUTIONAL REQUIREMENTS:
- All accidents reported to EHS within 24 hours
- Lab notebooks required for all procedures
- Instructor approval needed for equipment modifications

Benefits:

  • Specific to actual lab conditions
  • Addresses known student mistakes
  • Includes emergency procedures
  • Aligned with institutional policies

Key Transformation Principles

Notice how each context-engineered version includes:

🎯 Specific Context

Detailed information about the learner, environment, and goals

📚 Educational Standards

Alignment with curriculum, rubrics, and institutional requirements

🔄 Adaptive Elements

Ability to build on previous interactions and adjust to progress

🛡️ Safety & Ethics

Appropriate safeguards and pedagogically sound approaches

Example 6: Accessible Course Page Generator

❌ Standard Prompt Approach

"Create an HTML page for my online course module on climate change. Make it look nice with colors and include an infographic."

Problems:

  • No color contrast requirements — AI may pick decorative but inaccessible colors
  • Infographic has no alt text — invisible to screen readers
  • No semantic HTML guidance — likely to use <div> soup instead of landmark elements
  • No ARIA attributes — interactive elements won't work with assistive technology
  • Likely WCAG 2.1 violations out of the box — fails Section 508 compliance

✅ Context Engineering Approach

ACCESSIBILITY REQUIREMENTS (WCAG 2.1 AA / Section 508):

Semantic Structure:
Use HTML5 landmarks: <header>, <main>, <nav>, <section>, <footer>. Heading hierarchy must be h1 → h2 → h3 with no skipped levels. First focusable element must be a skip-to-main-content link.

Color & Contrast (Web-Safe Colors):
Body text: #333333 on #ffffff — contrast ratio 12.6:1 ✅
Headings: #003366 on #ffffff — contrast ratio 13.4:1 ✅
Accent/links: #005b99 on #ffffff — contrast ratio 7.2:1 ✅
Warning callouts: #7a3b00 on #fff3cd — contrast ratio 5.1:1 ✅
Never use color alone to indicate meaning — pair with icon + label.

Images & Infographics:
All <img> elements require descriptive alt text. The climate change infographic must include: alt="Infographic showing global temperature rise of 1.1°C since 1850, with bar chart of CO2 levels by decade" plus a full text summary immediately below.

ARIA & Keyboard Navigation:
All interactive elements keyboard-accessible (tab order logical). Focus indicators visible with minimum 3:1 contrast. Dynamic content uses aria-live="polite". Tab panels use role="tablist", role="tab", role="tabpanel", aria-selected, aria-controls.

Self-Audit:
Before finalizing, review each element against WCAG 2.1 AA Success Criteria. Report any violations by criterion number (e.g., SC 1.4.3 Contrast Minimum) and provide corrected code.

Benefits:

  • Section 508 and WCAG 2.1 AA compliant from the first draft
  • Usable by students with visual, motor, and cognitive disabilities
  • Screen reader compatible — full semantic structure
  • Specified web-safe hex colors with verified contrast ratios
  • Built-in self-audit prevents violations before delivery

Practice Transformation Exercise

Take one of your current educational prompts and transform it using context engineering principles. Consider:

  • What student information would improve responses?
  • What institutional standards should be included?
  • How can the system build on previous interactions?
  • What safety or ethical considerations apply?
  • What WCAG 2.1 AA accessibility requirements must be met for any HTML output?
  • What specific web-safe hex color values and contrast ratios should the AI use?

Interactive Context Builder

Transform your standard prompts into powerful context-engineered solutions using this step-by-step builder. Each step guides you through the essential components of effective educational context engineering.

🛠️ Build Your Context-Engineered Prompt

Step 1: Choose Your Educational Context Template

Select the template that best matches your intended use case:

📚 Tutoring Assistant

One-on-one student support and explanation

📝 Assessment & Feedback

Grading and providing constructive feedback

💬 Discussion Facilitator

Leading classroom discussions and debates

🔍 Research Assistant

Supporting academic research and writing

🎯 Practice & Drill

Skill practice and repetitive learning

⚙️ Custom Context

Build from scratch for unique needs

📋 Your Generated Context

Your complete context-engineered prompt will appear here as you progress through the steps:

Complete the steps above to see your generated context...

🎯 Context Quality Checklist

Use this checklist to evaluate your completed context:

✅ Completeness
  • □ Specific learning objectives defined
  • □ Student profile included
  • □ Teaching methodology specified
  • □ Examples and resources provided
⚖️ Balance
  • □ Detailed but not overwhelming
  • □ Flexible yet structured
  • □ Comprehensive yet focused
  • □ Safe and ethical boundaries set
♿ Accessibility (WCAG 2.1 AA)
  • □ WCAG 2.1 Level AA compliance required
  • □ Color contrast ratios specified (4.5:1 / 3:1)
  • □ Web-safe hex colors defined
  • □ ARIA roles & landmark requirements included
  • □ Alt text & multimedia captions required
  • □ AI self-audit instruction included
🔒 Safety & Ethics
  • □ Academic integrity guidelines set
  • □ FERPA/privacy considerations noted
  • □ Escalation triggers defined
  • □ Inappropriate request handling covered

⚠️ Important Reminders

  • Start Small: Test your context with a few students before full implementation
  • Iterate: Refine based on actual performance and student feedback
  • Monitor: Regularly review AI responses to ensure quality and appropriateness
  • Privacy: Always comply with institutional policies regarding student data

Hands-On Practice

Interactive Context Engineering Exercise

Practice building context for an educational AI assistant. Try different approaches and see how context engineering improves the quality and relevance of responses.

Scenario: Student Struggling with Essay Writing

Student Profile: Sarah, 10th grade, ESL learner, strong in STEM but struggles with persuasive writing, prefers structured approaches

Your analysis will appear here...

Context Engineering Checklist

Before Implementing Educational AI Systems, Ask:

Context Writing
  • □ What student information should be preserved?
  • □ How will learning progress be tracked?
  • □ What pedagogical strategies are most effective?
Context Selection
  • □ What knowledge is relevant to current task?
  • □ How do I prioritize different information types?
  • □ What educational standards apply?
Context Compression
  • □ How can I summarize without losing meaning?
  • □ What information is most critical?
  • □ How do I handle context length limits?
Context Isolation
  • □ How do I prevent cross-student contamination?
  • □ What information should be compartmentalized?
  • □ How do I manage session boundaries?
♿ WCAG 2.1 / Section 508 Compliance
  • □ Have I specified WCAG 2.1 Level AA as a hard requirement?
  • □ Are web-safe hex color values and contrast ratios (4.5:1 / 3:1) defined?
  • □ Are semantic HTML5 landmarks and ARIA roles specified?
  • □ Are alt text and caption requirements included?
  • □ Have I instructed the AI to self-audit output before delivery?

Practice Scenarios

Scenario A: Math Tutoring

Design context for an AI that helps struggling algebra students. Consider prior knowledge, common misconceptions, and effective teaching strategies.

Scenario B: Research Assistant

Create context for an AI that helps graduate students with literature reviews. Include research methodology, citation standards, and disciplinary conventions.

Scenario C: Language Learning

Build context for an AI language partner. Consider proficiency level, cultural context, learning goals, and conversation topics.

Scenario D: Lab Safety

Design context for an AI safety assistant in science labs. Include safety protocols, equipment knowledge, and emergency procedures.

Educational Context Engineering Frameworks

The CLEAR Framework for Educational Context

A systematic approach to designing educational AI contexts that are Comprehensive, Learner-centered, Ethical, Adaptive, and Reliable.

C

Comprehensive

Include all relevant educational information: learning objectives, student background, pedagogical strategies, and institutional requirements.

L

Learner-Centered

Prioritize student needs, learning styles, and educational goals in context design and information selection.

E

Ethical

Ensure privacy protection, bias mitigation, and transparent use of student data in all context engineering decisions. This includes ADA compliance: all AI-generated digital content must meet WCAG 2.1 Level AA and Section 508 requirements by design, not as an afterthought.

A

Adaptive

Design systems that can adjust context based on changing student needs, new educational research, and evolving learning objectives.

R

Reliable

Build robust systems that consistently provide accurate, pedagogically sound responses across diverse educational scenarios.

The Context Pyramid for Education

Three-Layer Educational Context Structure

🎯 Interaction Layer

Current conversation, immediate learning goals, real-time feedback

📊 Dynamic Context Layer

Student profile, learning history, adaptive content selection

🏗️ Foundational Layer

Curriculum standards, institutional policies, pedagogical frameworks

Implementation Strategies

🚀 Start Small

Begin with simple context engineering in one course or application before scaling to institutional level.

🔄 Iterate Rapidly

Test context designs with small groups of students and refine based on educational outcomes and feedback.

📏 Measure Impact

Track learning outcomes, student engagement, and system reliability to validate context engineering approaches.

🤝 Collaborate

Work with instructional designers, IT staff, and students to create comprehensive context engineering solutions.

Common Context Engineering Pitfalls in Education

  • Over-contextualization: Including too much information can overwhelm AI systems and reduce response quality
  • Static Context: Failing to update context as students learn and grow
  • Bias Amplification: Context that reinforces existing educational inequities
  • Privacy Violations: Including sensitive student information inappropriately
  • Technical Complexity: Creating systems too complex for educators to understand and maintain
  • Omitting Accessibility Requirements: Failing to include WCAG 2.1 AA and web-safe color specifications means AI-generated HTML will almost always have contrast failures, missing alt text, and inaccessible interactive elements — creating legal liability under Section 508 and the ADA

Resources for Continued Learning

Essential Reading

📚 Foundational Papers

🎓 Educational AI Research

  • AI Pedagogy Project (Harvard metaLAB)
  • Institute on AI, Pedagogy, and Curriculum (AAC&U)
  • Educational Technology Journal

♿ Accessibility Standards

🛠️ Practical Tools

🌐 Communities

  • AI in Education Discord
  • Prompt Engineering Reddit
  • Educational Technology Forums

Next Steps in Your Context Engineering Journey

1

Experiment

Start with low-stakes applications in your courses. Try building simple context-aware AI assistants for specific educational tasks.

2

Collaborate

Connect with colleagues, IT staff, and instructional designers to share knowledge and build institutional capacity.

3

Scale

As you gain experience, gradually expand to more complex applications and larger student populations.

4

Innovate

Develop novel applications of context engineering that address specific challenges in your discipline or institution.

Professional Development Opportunities

Recommended Training Paths

  • Technical Skills: Learn API integration, prompt programming, and context management systems
  • Pedagogical Knowledge: Understand how AI can support different learning theories and educational approaches
  • Ethical Framework: Develop expertise in responsible AI use in educational settings
  • Research Methods: Learn to evaluate the effectiveness of context engineering in educational outcomes
  • Accessibility Standards: Complete WCAG 2.1 training to understand what Level AA compliance requires, use the WebAIM Contrast Checker to verify color choices, and learn to read the WAVE accessibility report for any page your AI produces

Create Your Personal Learning Plan

Based on what you've learned today, identify:

  • One specific context engineering application you want to implement in your teaching
  • Three skills you need to develop to make this successful
  • Two colleagues or resources you can collaborate with
  • A timeline for testing and implementing your first context engineering project

Remember: The Future of Education is Context-Aware

Context engineering isn't just about better AI responses - it's about creating educational ecosystems that understand, adapt to, and support each learner's unique journey. As educators, you have the opportunity to shape how this technology develops and is applied in service of learning.