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. This approach enhances the student experience, boosts completion rates, and drives career success with precise, data-driven interventions.

Explainable AI and agentic LLM guide students and empower advisors and faculty with personalized, research-driven interventions

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.
UT Arlington

UT Arlington

Problem

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.

Results

The approach pinpointed program sweet spots and quantified social mobility impact using time-series post-grad success data, driving better outcomes.

United States Air Force

United States Air Force

Problem

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.

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.

Texas 2036

Texas 2036

Problem

Texas lacked clear, evidence-based insights into how high school course choices affect students' postsecondary success, career readiness, and economic outcomes.

Solution

CML Insight conducted a 23-year longitudinal study of student data, analyzing the impact of programs like AP, CTE, dual credit, IB, OnRamps, and JROTC.

Results

The study revealed which programs best predict success, guiding Texas 2036 in shaping targeted policy recommendations to improve student outcomes and strengthen the state's workforce.

Matter and Space

Matter and Space

Problem

An agentic LLM-lased 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.

Results

In beta testing

National University

National University

Problem

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.

Results

Excellent, equitable model performance results with MLOps in NU’s cloud infrastructure in a scalable Kubernetes environment with Kubeflow workflow orchestration.

KidsReadNow

KidsReadNow

Problem

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, enhancing program design and efficacy.

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.