As higher education evolves, technology and data are no longer just tools—they are foundational pillars shaping the future of learning and student success. This transformation was explored by a panel of experts from Western Governors University (WGU), Doowii and Instructure, who gathered to discuss how holistic data integration and artificial intelligence (AI) can enhance student support systems, democratize access to data and ultimately improve outcomes for learners navigating complex educational journeys.

What Is Holistic Data Integration?

At the heart of higher ed transformation lies the concept of holistic data integration. But what does that really mean? Data integration creates a unified platform from which institutions can harness advanced analytics and AI-driven insights. This is crucial because students today face myriad challenges—from financial hardships to mental health struggles, all while balancing work and family obligations.

Gathering useful data and using it to improve the student experience is a key focus at WGU. David Morales, the university’s senior vice president of technology and CIO at WGU highlighted the importance of viewing students as whole individuals and interpreting data to provide “just in time interventions” tailored to their unique needs.

"We try to figure out exactly where they are at any given point across the entire journey such that we can help them with whatever they need because that is the expectation,” said Morales. “Our expectation nowadays is that we get everything as fast as possible with the best support that we can."

To achieve this, WGU integrates data from over 800 different applications, creating a centralized hub—not a system of record, but a space for learning and understanding students better. This holistic approach ensures that interventions are timely, relevant, and supportive of student success.

Data Quality and Democratizing Access to Insights

Narine Hall of Instructure brought a data scientist’s perspective to the discussion, emphasizing the critical role of data quality in AI’s effectiveness. She highlighted that “AI doesn’t happen without data,” and the age-old adage “garbage in, garbage out” still holds true. The democratization of AI, she explained, means making data and AI tools accessible to more than just data scientists.

"Now it’s no more sort of this hot commodity only available to data scientists,” said Hall. “Now everybody in the organization can use the data, they can access it, they can do really cool things with it, and they are the domain experts."

This shift allows various departments and roles within institutions to leverage data for innovation and improved decision-making. Hall also underscored the rapid evolution of learning and pedagogy, fueled by AI breaking down silos and encouraging cross-organizational collaboration.

Building Trust Through Transparency and Security

As AI becomes more deeply embedded in education, trust and transparency around data use are paramount. The panelists acknowledged concerns around data privacy and ethical AI use, drawing parallels to earlier conversations about data disaggregation and student privacy. Hall emphasized that trust must be built through transparency and an opt-in culture.

"We should always go with that opt-in culture,” said Hall. “We should always be communicating 100% transparently what is happening with the data, what models are being used, or not, and all of the details."

Michael Lysaght, CTO at Instructure, echoed this sentiment by highlighting how security remains a top priority for customers adopting AI. 

"We have this thing which we call AI nutrition facts which allows everybody who's using any one of our products that is driven by AI to understand how their data is being used,” said Lysaght. “We will never use your data or the institution’s data to train models."

Morales shared a relatable example from his own life to illustrate the importance of transparency for users.

"My daughter logged into my Netflix account and started watching anime, so all my recommendations changed,” he said. “I wish we were transparent to users to say you are getting these results because this is what I know of you."

This example highlights the need for users—students, faculty and administrators—to understand how their data influences AI-driven recommendations and outcomes and to have control over their data.

Real-World Applications and Pilot Programs

The panelists shared practical examples of how their organizations are applying AI and data integration to improve student journeys. Hall described Instructure’s collaboration with Doowii as a way to empower users to interact with data naturally and intuitively.

"It allows our users to be able to talk to their data in a very natural way, ask questions and really know where everything is," she said.

Lysaght highlighted the importance of delivering actionable insights rather than just raw data.

"People don’t care about data, they care about insights,” he said.

At WGU, Morales revealed that there are currently over 76 AI initiatives underway, all focused on one key aspect—outcomes.

"Everything that we do that implements or brings AI to the table always has an outcome attached to it which then enables us to stay on top of that and make sure that the outcome of the process is exactly what we're looking for, not just the outputs,” he said.

This outcome-driven approach ensures that AI tools and data platforms are not just innovative but truly effective in supporting student success.

Partnering with Educators for Effective AI Use

Dodson described the importance of collaboration between AI developers and educators to tailor solutions to real challenges.

"We have spent a lot of facetime with program mentors talking about outcomes, metrics, and what they struggle with,” said Dodson. “Without that, we would have missed the mark."

This human element remains vital—AI is a powerful tool, but its success depends on understanding and partnering with those on the front lines of education. 

How Can Higher Ed Use AI to Drive Real Outcomes?

One of the most compelling discussion points was how higher ed professionals can ensure AI is widely adopted, understood and focused on achieving outcomes. They call it democratizing AI usage and integration.  

Lysaght framed democratization primarily in terms of cost and accessibility.

"The first thing with democratization is cost,” he said. “The cost of running these models has dramatically improved and gotten cheaper... It’s about understanding use cases that will drive outcomes and focusing on those."

Hall reflected on the progression from data science education to accessible AI tools, noting the importance of enabling everyone in an organization to use AI.

"The first step is getting everyone comfortable using AI, then integrating AI into daily workflows, and finally building AI."

Morales offered a vivid analogy comparing AI adoption to the evolution of credit cards.

"Credit cards went from clunky machines and carbon copies to paying with phones in a tap,” he said. “That level of interconnectivity, trust, and standardization is what AI democratization needs."

He emphasized that building trust, interoperability, and education are key pillars to accelerating AI adoption in education.

The Civic Ideal of AI Democracy

Dodson offered a thoughtful perspective on the democratic ideal of AI.

"A true democracy is government by the people, for the people, and of the people,” he said. “Democratizing AI means not just access, but active participation in deciding how AI is used."

In other words, democratizing AI is not just about technology access—it’s about empowering all stakeholders to shape AI’s role in education ethically and effectively.

Measuring Success: The Importance of Tracking Outcomes

As AI tools become more integrated into education, measuring their impact becomes critical. Morales explained how WGU tracks outcomes rigorously.

"We have weekly meetings to look at whether the outcomes we're after are really being achieved,” he said. “We have to stay on top of that, or we risk poor decisions impacting real human beings."

Hall added the importance of balancing efficiency gains with potential losses in the educational process.

"When AI creates efficiencies, we ask, ‘What might we be missing?’ For example, grading assistance might speed up the process, but teachers also get to know students through grading,” said Hall. “How do we put that back in?" 

A nuanced approach ensures that AI enhances education without sacrificing critical human elements like personalized feedback and connection.

Conclusion: Embracing AI to Transform Higher Education

The future of higher education lies in leveraging AI and holistic data integration to provide personalized, timely and effective support for students. As the panelists emphasized, this requires not only advanced technology but also a commitment to transparency, trust, collaboration and outcome-driven innovation.

Morales summed up their shared vision eloquently.

"What we're after is a drastic change in outcomes,” he said. “Technology is phenomenal, but outcomes drive us every morning."

Lysaght added a forward-looking perspective.

"AI is here to stay,” he said. “It’s a platform change, not a fad. The pace of change will accelerate, and it’s existentially important to invest and figure this out."

For educators, administrators, and policymakers, the call to action is clear: embrace AI thoughtfully, center student outcomes and build systems that democratize access and empower all stakeholders.  

Take Action

• Explore AI Solutions: Investigate AI tools and platforms that prioritize holistic data integration and student-centered outcomes.

• Build Trust: Foster transparency and ethical use of data to build confidence among students and educators alike.

• Collaborate Across Teams: Engage educators, data scientists, and technologists to co-create AI-driven solutions that meet real-world needs.

• Measure and Adapt: Establish clear metrics to track AI’s impact and continuously refine approaches based on outcomes.

• Empower Users: Democratize access to data and AI tools, enabling all stakeholders to participate actively in shaping the educational experience.