The number of AI-powered education tools being developed is quickly growing, but many organizations struggle with a core challenge: finding the technical talent to realize, refine, and sustain impactful solutions.
Given the rapid evolution of AI—rumor has it ChatGPT-5 could drop any day—the skills that count as “cutting-edge” are constantly in flux. Meanwhile, even as demand for expertise in AI, machine learning, automation, and data science increases across industries, the talent pool remains limited. In this competitive market, projects can struggle to scale and earn the trust of investors, particularly nonprofits and early-stage ventures unable to meet the high costs of hiring technical leads.
And it’s not enough for a tool to simply be technically functional. Its features should be built to generate meaningful improvements on learning outcomes. This often calls for specialized knowledge that not all engineering leads necessarily have.
The Learning Engineering Virtual Institute (LEVI), a program of Renaissance Philanthropy, is designed with these challenges in mind. Structured as a hub-based program that leans into the value of collaboration, LEVI delivers the strategic technical guidance organizations need at different stages of research and development.
A New, Flexible Approach to Sourcing Tech Talent
The LEVI program’s network of expert hubs—five currently available to teams in Year 3—provide on-demand access to targeted support and resources. Balancing deep field experience and a strong understanding of learning engineering needs, these are housed across a set of affiliate organizations, including RYE Consulting, Teaching Lab, the Digital Harbor Foundation, Learning Data Insights, and the University of Minnesota.
Each hub was selected in alignment with the program’s current goal of doubling the rate of middle school math and with specific attention to the challenges teams are likely to face based on their interventions. From assistance with user engagement to help establishing research infrastructure, these hubs offer flexible, fractional support, allowing teams to tap into supplemental expertise they might not be able to sustain full-time.
Thomas Christie, Director of Learning Engineering at the Digital Harbor Foundation, leads LEVI’s Engineering Hub, which is dedicated to helping teams overcome technical roadblocks. He shares, “One of LEVI’s strengths is its willingness to invest in ideas and products from a wide range of organizations, from startups to established non-profits to University labs. The diversity of teams brings new ideas to the table. However, that diversity also requires a range of skills—scaling technology, designing experiments—and not every team has the necessary expertise in-house.”
The Engineering Hub’s centralized, project-based framework means teams can seek guidance in short bursts, tackling specific technical hurdles without committing to long-term hires who may not end up fitting a team’s needs the following month. With how quickly the AI landscape shifts, access to diverse technical backgrounds and skillsets means that teams can quickly adapt and source specialized input as their needs change. This interim access is particularly important as teams experiment with different approaches and A/B test their solutions, pivoting when something doesn’t yield the intended impact.
Translating Technical Talent into Education Impact
What does this support look like in practice?
Zach Levonian, a Senior Machine Learning Engineer with Digital Harbor and the LEVI Engineering Hub, has worked closely with the team at Rising Academies to build a data infrastructure and alerting system for their AI-powered math tutor, Rori. This system ensures that Rori can effectively moderate student input and generate safe LLM outputs.
“This kind of work requires a broad range of skills,” notes Christie. “It requires back-end development, automated testing, data pipelines, user experience research, and knowledge of the educational domain. It’s difficult to find a single person with this broad of a skillset, but Zach has it.”
What makes experts like Lenovian even rarer finds is their ability to speak to both technological and educational audiences. Someone may have a strong grasp of natural language processing, but may not have a sense of how to design feedback loops that actually align with student learning needs and progressions. Designed and staffed to support LEVI’s target learning outcome, the Engineering Hub brings that dual perspective.
Christie explains, “We have team members who have experience with software engineering, applied machine learning, and social science research, so we can translate between these concerns.”
The Engineering Hub has also collaborated with the Carnegie Learning team to level up their LiveHintAI tool, which uses an LLM to provide chat-based support as students work through math problems. To provide students with more engaging and effective guidance, the hub is helping the team embed a plotting functionality that enables visual representations of math concepts through plots and graphs.
Take the Eedi team as another example. Their organization is developing a map of student math misconceptions that both helps predict future misconceptions and recommend ways teachers can help address student needs. Microsoft Research initially developed a model to power one of Eedi’s features, but when they weren’t able to continue supporting production long-term, Christie’s team at the Engineering Hub was able to step in. “We picked up the code that Microsoft developed, understood how it worked, and worked with Eedi to build a cloud-based model re-training pipeline,” Christie said. “Our team’s experience with both education-focused machine learning approaches and cloud deployments made this project possible.”
Fostering Collaboration in Ed Tech Development
Beyond the individual guidance LEVI innovators receive from hubs, teams are encouraged, and often do, source support, resources, and data from across the full cohort. In a recent convening, participants had the chance to demo their solutions for one another, and monthly calls with the entire cohort allow teams to exchange ideas and source feedback.
Christie notes that this collaborative ethos isn’t common within the ed tech ecosystem, where both privately owned and non-profit organizations often operate in silos due to concerns about intellectual property and competition. He adds, “Teams work together to produce innovative technology that is shared at conferences and often open-sourced. The hubs, which work with multiple teams, make each team aware of emerging best practices and common pitfalls, so we can learn from each other and improve together.”
This culture is especially valuable as teams navigate the uncharted territory of breakthroughs in AI and education. “LEVI teams are on the bleeding edge of building student-facing, LLM-powered products, so we’re managing novel risks, and addressing lots of unsolved problems,” shares Christie. The hubs ensure that any technical advance one team makes helps elevate the entire field.
This spirit of cross-organizational knowledge and resource sharing is a framework that others—beyond the LEVI community—can build on, too. With a successful track record, the hub approach offers a strong, adaptable blueprint the broader innovation community can reference to uplevel their impact as AI development continues to grow.
Interested in connecting with the LEVI community or sharing your perspectives with our team?
- Reach out to ioanna@the-learning-agency.com to share your thoughts on technical talent needs across ed tech.
- Join a Learning Engineering Networking Event to meet others in the LEVI network.
