Explainable AI and agentic LLM guide students and empower advisors and faculty with personalized, research-driven interventions
Students often lack consistent, tailored guidance from lead to enrollment, completion, and career success, while faculty and advisors struggle with limited tools for personalized, evidence-based interventions. CML Insight leverages Explainable AI and agentic LLMs to guide students seamlessly through their journey, pairing this with hybrid LLM-AI solutions for advisors and faculty to deliver research-based, customized support with human-AI collaboration.
High-Precision, Accurate Predictive Models for Student Lifecycle
- Advanced predictive models leverage AI to map each stage of the student journey, from lead to graduation.
- These tools predict risks, influenceable factors, and opportunities, enabling proactive support for better outcomes.
Integration of Post-Graduate Career Outcomes Data from Equifax in the Age of Gainful Employment
- We work with institutions and Equifax to integrate post-graduate career data to track employment, earnings, affluence, and more meaningful trends for each student.
- This aligns with Gainful Employment goals, ensuring programs prepare students for sustainable careers.
Curation of Real-World Evidence from High-Quality Research Papers
- Our team curates evidence from top-tier research papers to ground interventions in proven, research-based insights.
- This ensures strategies are robust, relevant, and tailored to real-world student needs.
Agentic LLM to Develop and Deliver Evidence-Based Interventions for Human-AI Collaboration
- An agentic LLM crafts precise, evidence-based interventions, adapting to individual student profiles.
- It empowers advisors and faculty, enhancing human-AI collaboration for impactful, personalized support with Q&A to deliver learnings on student success science.
Real-World Results
See how we've delivered measurable impact
UT Arlington
Results
The approach pinpointed program sweet spots and quantified social mobility impact using time-series post-grad success data, driving better outcomes.
Challenge
Institutions lack unified data to assess program effectiveness and struggle to measure impacts on student outcomes and social mobility post graduation.
Solution
CML Insight processed integrated institutional and post-grad job data from Equifax, launching the CML Insight SaaS app to track and measure heterogeneous impact results for various student success programs.
United States Air Force
Results
We are working with AETC to transform their learning data into student journey, touchpoints, and KPIs to enable scalable predictive and causal AI deployments to help improve force development outcomes.
Challenge
USAF Air Education and Training Command (AETC) has a number of student success programs to foster a culture of learning and improve force development outcomes.
Solution
CML Insight has been in multiple conversations with AETC leadership to deploy our SaaS application for causal impact assessment and opportunity identification.
Matter and Space
Results
In beta testing
Challenge
An agentic LLM-based flexible learning environment with causal learning to help learners of all kinds
Solution
Working with Matter & Space and partners, we are embedding flexible learning pathways with n-of-1 causal learning that can help LLM agents work seamlessly with learners in their learning journey to improve learning, human skills, and wellness outcomes.
National University
Results
Excellent, equitable model performance results with MLOps in NU's cloud infrastructure in a scalable Kubernetes environment with Kubeflow workflow orchestration.
Challenge
An end-to-end modeling platform from prospects to students in a complex term structure
Solution
Leveraging their CRM, SIS, and LMS, we built six predictive models from lead to apply to enroll to persist, native to NU's complex term structure. Both relative and absolute engagement features from LMS were used to improve model performance.
KidsReadNow
Results
CML Insight confirmed KRN's effectiveness for 3rd-grade reading, with KRN schools showing a 0.2 standard deviation improvement in scores compared to non-KRN schools, equating to a 7 percentile point gain.
Challenge
KidsReadNow (KRN), a non-profit providing in-home reading programs for PreK-5, struggled to gather and analyze enough data to assess its program's effectiveness.
Solution
CML Insight tackled KRN's data issues by combining public and private data sources with fuzzy matching. Using its Causal AI application, CML Insight evaluated KRN's impact across various student groups and schools while enabling KRN's CEO and staff to run their own experiments.