Register free for the Human-AI Collaboration Challenge — July 25–26 at Morgan State University!

About us · CECE Lab · CEAMLS · Morgan State University

AI education that leaves no community behind

We are the Children's Education in Computing Exploration (CECE) Lab, part of the Center for Equitable AI & Machine Learning Systems at Morgan State University, building AI literacy where it's needed most.

The CEAMLS K–12 AI team with students at a Morgan State University program event

AI is reshaping what students need to know but access to AI education is spreading unevenly, and under-resourced communities risk being left behind. Based at Morgan State University, a historically Black research university in Baltimore, we design AI curriculum that promotes AI literacy, ethical understanding, and college readiness that works even where devices and internet don't. Everything we build is free and open-source.

The pilot year

  1. 2023

    Launch

    With support from corporate sponsors and government funding, our journey began in 2023 at a single public library in Baltimore. We launched our first series of interactive AI workshops with a simple mission: make artificial intelligence accessible, engaging, and fun for young learners. The response from students, families, and educators exceeded our expectations, confirming that hands-on, game-based learning could inspire curiosity and build confidence in emerging technologies.

  2. 2024

    A Strategic Pivot

    As we expanded, technology audits revealed that the greatest barrier to AI education was not interest, it was access to reliable devices and internet connectivity.Rather than scaling back, we redesigned our programs with an offline-first approach. We developed printed learning materials, facilitator guides, and hands-on activities enabling meaningful AI learning experiences without requiring computers or internet access.

  3. March 2026

    Empowering Educators

    Recognizing that sustainable impact begins with teachers, we launched our AI Professional Development Program. Through workshops, classroom-ready resources, and practical training, we equipped educators with the knowledge and confidence to introduce AI concepts in their classrooms.

  4. 2026

    Scaling Through Open Resources

    To expand our reach, we enhanced our educator programs and made our curriculum and teaching resources open source. By providing freely accessible, high-quality materials, we enabled schools, libraries, and community organizations to adopt and adapt our AI education programs.

  5. 2027→

    Statewide Expansion & Research

    Building on our success in Baltimore, we are expanding our programs to schools across Maryland. At the same time, we are establishing a dedicated research initiative to measure educational outcomes, evaluate best practices, and contribute evidence-based insights that advance equitable AI education.

What the pilot showed

0

Point gain in student AI comprehension (38% → 85% pre/post)

0

Of stakeholders found the DEAR model adaptable for future lessons

0

Said culturally responsive design improved access

2.7→3.9

Teacher confidence (out of 5) across the PD series

0

Student engagement rating during hands-on activities

From the K–12 AI Curriculum Development Project final report to the Maryland Center for Computing Education (2026). Pre/post cohort: 175 grade 4–5 students at Title I primary sites.

The DEAR Method

Design, Educate, Assess, Refine: an iterative instructional framework for AI literacy in low-resource settings. The framework is grounded in culturally responsive teaching and built to run offline with a single facilitator. It is the backbone of our curriculum.

Explore the curriculum →

Meet the team

The core CEAMLS K–12 team, supported by Morgan State student mentors, volunteers, and 50 from Baltimore schools.

Dr. Kofi Nyarko

Principal Investigator · Director, CEAMLS

Zamalia Bennett

Program Lead, K–12 Initiatives

Farhana Begum

Graduate Researcher, CECE Lab

Jamal Williamson

Graduate Researcher, CECE Lab

Oludamisi Arowosegbe

Graduate Researcher, CECE Lab

Peer-reviewed and publicly funded

SITE 2026 papers

Two peer-reviewed papers at the Society for Information Technology and Teacher Education conference: the DEAR Method as a multimodal AI-literacy framework for under-resourced K–12 schools, and preparing K–12 teachers for AI integration through Challenge-Based Learning. Both include replicable methodology other researchers and practitioners can use.

Questions or partnership ideas?

We would love to hear from you, educators, researchers, funders, and families alike.

ceamlsmsu@morgan.edu →