Intro to AI
Foundational concepts: what AI is, how machines learn from examples, and where students already meet AI in daily life.
Our framework
The DEAR Method
An iterative instructional framework we developed to support AI literacy in low-resource settings. It's grounded in culturally responsive teaching and designed to work offline — a single facilitator with printed materials can run every lesson when devices or internet aren't available.
Design
Organize lessons and learning objectives around a real question students care about.
Educate
Introduce AI concepts through low-tech, multimodal activities — stories, games, and hands-on investigation.
Assess
Measure student understanding through observation and performance tasks, not just quizzes.
Refine
Use formative feedback to improve instruction — the same loop that produced our 2.0 curriculum.
Inside a lesson
Anatomy of a 60-minute session
Every DEAR session follows the same structure and can run with or without devices. Here's the Facial Recognition unit as an example:
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Opening & prior-knowledge activation · 10 min
A short animated video or an illustrated read-aloud of "Cece and the Case of the Misidentified Face" introduces vocabulary — algorithm, database, pixel, false positive — in a narrative context.
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Vocabulary & concept check · 5 min
A low-stakes matching or word-search activity on terms like biometric, faceprint, and surveillance gives facilitators a quick read on prior knowledge.
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Hands-on investigation: Panda Detective · 20 min
Students use rulers to measure facial landmarks on printed panda sheets, simulating how a facial-recognition algorithm collects and compares biometric data. Our highest-engagement phase (4/4 in classroom observations) — and it requires no digital devices.
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Structured worksheets · 15 min
Tiered, grade-differentiated tasks connect the concept to everyday examples like phone Face ID and airport verification.
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Ethical discussion & reflection · 10 min
Guided questions ground fairness, privacy, and responsible use in students' own neighborhoods — closing with a Kahoot quiz or a printed emoji exit ticket.
The curriculum
Five student units + a community workbook
Each unit combines videos, guided discussions, hands-on activities, and creative projects — with vocabulary, reading comprehension, design challenges, and reflection built in.
Intro to Coding
Computational thinking and programming concepts that support AI — how code relates to the design and operation of AI systems.
Pattern Recognition
The core skill behind machine learning, taught through games and hands-on classification challenges.
Smart Cities
AI applied to community challenges: agriculture, waste management, water conservation, renewable energy, and sustainable cities.
Facial Recognition
Biometrics, algorithms, and false positives — plus the ethics of surveillance, explored through the Panda Detective investigation.
2.0 edition
AI & Transportation 2.0
Our offline-first flagship, redesigned from pilot feedback: screen-free, multimodal delivery with scripted facilitator guides, so any teacher or librarian can lead after a short orientation.
Also in the repository
What else you'll find inside
30 teacher-created CBL lessons
Challenge-Based Learning lesson plans and student learning games built by our AI Teacher Fellows — including an interactive web-based fractions game.
Scripted facilitator guides
Step-by-step lesson plans with embedded ethics discussion prompts — written so classroom teachers, librarians, and community educators can lead with confidence.
Evaluation toolkit
The AI-literacy pre/post assessments, six-domain classroom observation rubric, and stakeholder surveys we used in the pilot — reusable for your own evaluation and grant reporting.
Alignment
Standards-aligned
The curriculum aligns with the Common Core State Standards, the CSTA K–12 Computer Science Standards, the Next Generation Science Standards (NGSS), the ISTE Standards for Students, and the AI4K12 Big Ideas — supporting Maryland's computational literacy goals and ready for integration into MCCE projects and other K–12 computer science initiatives.
Common Core
CSTA K–12
NGSS
ISTE
AI4K12 Big Ideas