Beyond Experimentation: Responsible AI in Education Use Cases
AI in education is no longer a side experiment owned by individual innovators. From 2024 to 2025, AI implementation in higher ed grew from 49% to 66%, indicating that institutional leaders are treating the technology as core infrastructure.
However, moving from pilots and strategic intent to a sustainable operational reality requires more than technological know-how: it requires expertise and capabilities in data strategy and governance, stewardship, and technology modernization.
“AI pilots are easy to start and hard to sustain. The real work is connecting the data, setting the right guardrails, integrating the tool into daily workflows, and proving that it helps learners or instructors in a measurable way. Unicon helps institutions move from AI ambition to working tools that fit their people, platforms, and long-term goals.” — Rob Nield, Data Architect, Unicon
Several use cases illustrate how Unicon supports the creation of governed, measurable, and deployable AI systems that add immediate value for learners and instructors.
AI Tutoring, Support, and Guided Intervention
One of the main areas where institutions are integrating AI is providing academic support, feedback, and personalized instruction to students directly, as well as helping instructors identify learning roadblocks.
Case Study
Learnvia
Introductory mathematics classes are one of the biggest barriers to degree completion. Learnvia began as a collaboration between the Gates Foundation and Carnegie Mellon University to reduce drop, fail, and withdrawal rates in math courses.
Unicon was tasked with developing the mobile-first courseware, using Learning Tools Interoperability (LTI) to integrate grades and student rosters between the platform and university LMS. Open-source components helped contain licensing costs and support adoption by institutions nationwide.
The courseware includes an AI-powered tutor and coach for real-time guidance and problem-solving help, adaptive learning paths that deliver a personalized experience, and interactive exercises and short videos.
Instructors use a dashboard to identify and analyze where students face challenges in real time — such as difficulties grasping concepts or with specific assignments — so they can tweak assessments, adjust the pace of lessons, or step in with other adjustments before issues become significant learning gaps.
38 higher education institutions across the country — from community colleges to private universities — are now using Learnvia.
Case Study
Southern New Hampshire University AI Advisor
The Gates Foundation also engaged Unicon to develop the Learner Implementation Framework (LIF), an initiative intended to support standardized, portable records of student skills and experiences among under-resourced institutions. Unicon built the framework, tech stack, and data model for the project.
Southern New Hampshire University used LIF to develop an AI tutor and advisor to support more than 200 students. Raw data from SNHU's internal analytics platform — including advisor, GPA, and student profile information — flows into a data orchestrator and translator, creating a record that informs the tutor. Information from the AI conversations is cross-referenced with LIF and stored in a database to create a comprehensive picture of each student.
This model optimizes data retrieval for AI-powered advising, reducing response times from 15 seconds to 3 seconds. SNHU plans to scale the tool to more than 100,000 students and extend it across a range of courses, learning experiences, and career advising.
Case Study
Literacy Nonprofit
Unicon partnered with a literacy-focused nonprofit to build a teacher-facing dashboard that turns student reading signals into clear, actionable guidance for K–3 educators.
The nonprofit already had an automatic speech recognition (ASR) capability that captured students practicing early reading skills, producing structured data that breaks student reading down into granular components. Unicon implemented a controlled prompt workflow that combined client context, instructions, constraints, and aggregated reading signals.
A dashboard provides visuals showing patterns, progress, and top issues for teachers to focus on. Instructors can drill down into findings to explore longer, structured guidance — balancing immediate actions with in-depth direction for long-term student support and instructional value.
AI and Career Advising
As AI reshapes industries, colleges and universities are under increasing pressure to demonstrate career relevance and workforce alignment. High student-to-advisor ratios and a growing demand for personalized guidance are also driving the growth of AI-powered career advising solutions.
Two examples show how institutions and skills-based education organizations are responding to this demand with solutions rooted in real-world labor demand and skill requirements.
Case Study
Western Governors University Achievement Wallet
WGU leveraged LIF's capabilities to build Achievement Wallet, a shareable learner mobility platform that integrates academic achievements, work experience, and skills. Students add their experiences, course credits, skills, employment history, career goals, and desired salary to their profiles.
Since companies use different systems to hire and track applicants, WGU developed a UI tool that enables employers and recruiters to prioritize hiring based on desired skills and experience — showing what students can do, not just what they have learned. This also supports non-traditional learners who may not have earned a degree or other formal credentials.
The solution structures digital credentials at the course level using Open Badges 2 and 3, as well as the Learning and Employment Record Resume Standard, further supporting data interoperability, portability, and the verifiability of skills.
Case Study
Opportunity @ Work STAR Mobility Data Model
Opportunity @ Work (O@W) is a national nonprofit dedicated to promoting a skills-first labor market, helping individuals who are Skilled Through Alternative Routes (STAR) secure middle- and high-wage jobs. The organization used LIF to build the STAR Mobility Data Model (SMDM), which predicts career pathways for STARs using census and labor market data to track career transitions over ten years. The model also maps skills to occupations based on the U.S. Department of Labor's O*NET skill taxonomy.
O@W used LIF to standardize and analyze worker profile data and AI career counseling chat histories from Jobcase, identifying “gateway jobs” that would realistically enable STARs to move into higher-paying roles. The organization then built its own chatbot career counselor, which can suggest specific upskilling programs and point to actual job postings, helping STARs create a clear plan for career advancement.
Since SMDM's inception, O@W has seen a two-fold increase in STARs clicking on higher-paying job listings and 60% more job applications for roles offering upward job mobility.
The Infrastructure Behind Meaningful AI Outcomes
All of these use cases leverage AI-powered tools and real-time analytics to provide personalized experiences and guidance based on data, whether for students, instructors, or job seekers. Each project sought to drive specific, measurable improvements in learning and career readiness by deploying AI in a targeted manner. And they were all built on a foundational infrastructure layer that connects fragmented data and enables interoperability across systems.
Unicon was integral in building that layer, and we continue to help clients and partners plan, build, and execute AI-powered applications strategically and responsibly to ensure long-term sustainability and meaningful outcomes for organizations, students, and employers.
“If we are not helping the client implement something tangible — integrated into their platforms, measured in real usage, and supported operationally — then we are not ‘enabling’ anything. We are speculating.” — Dan McCallum, Chief Services Officer, Unicon
