OVERVIEW

The vast network of school districts across the United States was investing heavily in premium instructional content with great anticipation for improved learning outcomes. Yet, despite these significant expenditures, academic outcomes were falling short of forecasts, raising questions about the effectiveness of instructional approaches. As educators and policymakers sought new solutions, integrating AI into the learning process emerged as a promising avenue for transformation.

CLIENT

A US-based K-12 educational consortium that focused on addressing curriculum redundancies and
High Quality Instructional Materials (HQIM) adoption needed an intuitive AI solution to drive data-driven education for enhanced learning and quality of instruction.
They partnered with Quantrium to implement an advanced AI platform tailored for collaborative K-12 educational settings in the US, enabling schools to deliver innovative learning approaches, personalized education paths, and effective teaching outcomes.

THE QUANTRIUM LEAP FORWARD

Quantrium designed a secure, robust, and scalable platform for real-time K–12 curriculum management, teacher support, and student learning. Seamlessly integrating it with student outcome data, it was fully interoperable with multiple district-level systems such as Learning Management Systems (LMS) and Student Information Systems (SIS).

• Mapping the core knowledge areas for each grade and detecting the inter-grade dependencies.
• Identifying the source of the problem - curriculum, teaching pedagogy, or the specific needs of the students.
• Performing AI-powered root cause analysis to uncover the underlying problems and creating student cohorts to help the teachers deliver personal attention where necessary.
• Using AI to generate interactive multimedia content (audio/visual/text/others) tailored for each cohort as recommendations to effectively address the learning gaps.
• Monitoring and updating knowledge comprehension and proficiency with interactive tools like online quizzing and tests for impactful learning.

IMPACT

• The implementation reinforced the potential of AI in enhancing personalized learning journeys and teacher engagement.
• It offered targeted support to struggling schools, even as it enhanced instructional outcomes.
• Instructors supported the recommendations because they were clearly outlined and backed by evidence, based on data insights from multiple sources ( LMS, SIS, quizzes, and surveys)
• It connected the gaps in curriculum delivery and classroom journeys by delivering AI-generated,
  hyper-customized, context-aware content that scaled and adapted to the requirements.

Collectively, the enhanced personalization and effectiveness of learning and teaching practices made a meaningful impact on the overall K-12 education ecosystem.

TECHNOLOGY STACK

TensorFlow

Federated (TFF)

PySyft

GenAI (LLMs)

Scikit-learn

Causal ML

Knowledge Graphs

LLMs