Evidence-Based AI Education for Real-World Impact
ShifterLabs designs and deploys AI-powered learning experiences grounded in learning science, ethical AI, and real-world pilots across education and workforce development.
1️⃣ 🔬 Research Foundations
ShifterLabs’ learning experiences are grounded in two core research frameworks developed by our team:
ShifterLabs Learning Model (SLM)
Multimodal, AI-driven learning framework based on learning science, cognitive load theory, connectivism, and microlearning.
ShifterLabs AI Literacy Scale
A nature-inspired competency framework (Seed → Ecosystem) for building ethical, practical, and strategic AI capabilities.
📄 Download THE WHITEPAPERS: http://bit.ly/4rBRszs
Our solutions are currently in early-stage pilots with educators, professionals, and institutions. Initial results indicate:
2️⃣ 📊 Early Evidence & Pilot Results
High learner engagement in chat-based microlearning environments
Rapid adoption in low-friction mobile-first contexts (WhatsApp-based learning)
Positive qualitative feedback from educators and AI literacy programs
Strong alignment with Microsoft Education and responsible AI principles
✔️ + 20 early testers
✔️ Launch pipeline with 100+ users
✔️ Academic validation in progress (IKIAM, USFQ)
✔️ Expert validation via Microsoft EDU conversations
3️⃣ Partners & Ecosystem ValidatioN
Microsoft Education (Exploratory conversations & roadmap alignment)
Women in Tech GLOBAL (Launch partner & community access)
IKIAM University (Academic endorsement in progress)
UNIVERSIDAD SAN FRANCISCO DE QUITO USFQ (Academic conversations & endorsement in progress)
While formal longitudinal studies are forthcoming, ShifterLabs is actively collaborating with academic and industry partners to build a rigorous evidence base for impact.
4️⃣ 🧪 Evidence Roadmap (2026)
2026 Q2: Structured pilot evaluation with educators
2026 Q3: Independent research collaboration (university partner)
2026 Q4: Public impact report & case studies
2027: Longitudinal outcomes study
