Educators today are inundated with data, but not necessarily the kind they can effectively use.
Ask most faculty or student support staff what it takes to access meaningful student data, and they’ll describe a process that involves juggling log-ins, running clunky reports, and cross-referencing dashboards — if they have time to try at all. According to NCES data, while 75% of K–12 administrators report having access to useful data, only 39% say they have sufficient time to utilize it.
In postsecondary education, the challenge is similar: the data exists, but it’s fragmented, labor-intensive to access, and hard to interpret without technical training. When educators do have tools, they’re often underutilized because the data isn’t designed with them in mind.
This challenge is at the core of why WGU Labs was established in the first place: to leverage the data-driven insights we know exist regarding individual student persistence and apply them to effective interventions, enabling all students to achieve their educational goals. Today, we believe that this is one of the most promising areas for the responsible and human-centered deployment of AI.
The educator data challenge
Despite increasing access to educational data, many educators struggle to transform that data into usable insights. Four persistent barriers tend to exist for educators and administrators:
- Data Fragmentation: Educational data exists, but is trapped in siloed systems across tools and departments.
- Data Literacy Gaps: Educators tend to excel at educating students, but most are not trained to perform the types of complex analysis required to gain insights from the systems and data they ostensibly have access to.
- Time Constraints: Even those who could navigate the precarious landscape of their education data, almost universally, have very little time to spend locating, cleaning, and interpreting that data amid their existing demanding workloads. This point is supported by respondents to the 2024-25 School Pulse Panel, in which 75% of polled administrators reported having sufficient access to data about their schools, but only 39% feel they have adequate time to utilize that data.
- Underutilization of Resources: Even when tools are available, they’re often underused. Studies show that up to 70% of software licenses go unused in districts due to poor usability and unclear value. In focus groups conducted with WGU Program Mentors, many data tools were identified as valuable resources for mentors. However, the use and recognized value of these tools were very often distinct and different across colleges, mentor teams, and individual mentors.
Despite a prevalence of data, particularly in online learning contexts, many educators struggle to leverage this information for actionable insights.
AI as a bridge, not a black box
AI isn’t a magic solution, but it can be a powerful bridge between complex data systems and the educators who need actionable insights to support students.
In a recent pilot with WGU Program Mentors, we tested this hypothesis using an AI-powered data tool built to make querying and interpreting student data as easy as asking a question in plain English. What we found was striking.
Mentors who had struggled to pull reports from multiple systems were suddenly able to retrieve tailored student lists, visualize risk indicators, and spot patterns on demand. No SQL. No dashboards. Just answers.
One mentor described using AI-generated insights to flag a high-risk student who was in danger of failing an assessment before it occurred. With that information, the mentor proactively adjusted the student’s plan, preserving both their transcript and their confidence.
This is the promise of data made usable: proactive support, not reactive damage control.
Closing the achievement gap
When data becomes accessible, interpretable, and actionable, educators are empowered to do what they do best: support students with intention and impact. AI tools designed with educators in mind can help close the gap between recognizing a problem and knowing what to do about it. The future of student success may depend not just on more data, but on making it usable for the people who shape the learning experience every day.
