Harnessing Data and Artificial Intelligence in Higher Education
By Caroline Maulana, Interim Chief Data Officer & Director of Analytics, University of South Carolina
The Rise of Data & AI in Higher Education
A Digital Transformation in Higher Education is taking place that has never been witnessed before. Universities are no longer just centers of teaching and research; they are also complex enterprises managing student experiences, faculty productivity, healthcare operations, financial sustainability, and research competitiveness. At the heart of this transformation is data and increasingly, artificial intelligence (AI).
As Interim Chief Data Officer at the University of South Carolina, I have witnessed firsthand how analytics and AI are reshaping the academic enterprise. Higher education has always had an abundance of data, but institutions are now starting to realize its full value thanks to developments in machine learning and predictive modeling. From forecasting enrollment trends to improving student retention, AI and analytics are emerging as powerful levers for strategic decision-making and operational efficiency.
The future of Higher Education will depend on how well institutions can balance innovation with responsibility.
Practical Use Cases: Transforming the University
Some of the most impactful applications of AI and analytics in Higher Education include:
- Student Success & Retention: Predictive analytics models can identify students at risk of attrition early, enabling timely interventions such as targeted advising or financial support.
- Admissions & Enrollment: AI-driven insights help enrollment teams forecast demand, personalize outreach, and balance class composition.
- Research Enablement: Data mining and visualization tools accelerate research by identifying funding opportunities, collaboration networks, and emerging areas of scholarship.
- Resource Optimization: AI can optimize faculty workload planning, classroom scheduling, and even energy usage across campuses.
- Data Governance & Compliance: With rising scrutiny around privacy and ethics, AI-powered monitoring can strengthen compliance with FERPA, HIPAA, and other regulatory frameworks.
Each of these use cases reflects a broader trend: universities are learning to act more like data-driven enterprises, leveraging analytics to inform both academic and business decisions.
Challenges on the Journey
Adopting these technologies in Higher Education is not without obstacles. At USC, and across the sector, several challenges surface repeatedly:
- Cultural Readiness
Universities have a long history, fragmented organizational systems, and a wide range of stakeholders. Driving adoption of AI requires not just technical solutions, but cultural alignment. Faculty and staff must trust the insights produced by these systems. - Data Quality & Governance
AI is only as good as the data it ingests. Many institutions, including ours, are still building strong governance foundations, clarifying data ownership, stewarding definitions, and addressing inconsistencies across systems. - Privacy & Ethical Considerations
Unlike the corporate world, universities deal heavily in sensitive student and health data. Striking the right balance between innovation and compliance is a daily challenge. Ethical use of AI avoiding bias, ensuring transparency is especially critical in academic settings. - Talent & Skills Gap
Higher Education is competing with industry to attract top analytics and AI talent. For public universities, constrained budgets can make it difficult to compete on salary, which means creative strategies are needed to insource, train, and retain analytics professionals. - Change Management
Technology implementation is rarely the hardest part; it’s the change management around it. Building trust, aligning with faculty governance, and ensuring usability often determine whether a project thrives or stalls.
Positive and Negative Impacts
The promise of data and AI is enormous but so are the risks.
- Positive Impacts
- Improved Student Outcomes: Personalized learning paths and early interventions help more students succeed.
- Efficiency Gains: Automation and predictive analytics reduce administrative burdens and free staff to focus on high-value tasks.
- Informed Strategy: Leaders have stronger evidence to guide institutional investments and priorities.
- Negative Impacts
- Equity Concerns: If not carefully designed, algorithms can reinforce systemic inequities in admissions or financial aid.
- Data Fatigue: Overreliance on dashboards and metrics can overwhelm staff and cloud decision-making.
- Resistance to Change: Faculty and administrators may mistrust models or fear that AI will replace, rather than augment, human judgment.
The lesson is clear: technology must be implemented with intentional governance, transparency, and stakeholder collaboration.
Looking Forward: The Role of the Chief Data Officer
The emergence of roles like the Chief Data Officer (CDO) in Higher Education signals a growing recognition that data is a strategic asset. Yet the role is not static, it has quickly evolved. Chief Data Officers are increasingly being replaced with Chief Data and Artificial Intelligence Officers (CDAIOs). This shift reflects the reality that AI is no longer a separate, experimental technology; it is now inseparable from how data is collected, governed, and used to drive insight.
A modern CDAIO must understand both the governance of data and the application of AI, ensuring that institutional innovation is pursued responsibly. AI is part of our lives, whether we shoulder the responsibility of stewarding data for an entire institution or are simply exploring new tools as individuals. The CDO role is therefore not just about cataloging and governing data but also about anticipating how AI will intersect with everything from admissions processes to faculty research to student learning experiences.
At USC, I have seen the value of pairing governance with innovation: ensuring that as we build predictive models or deploy new dashboards, we are also establishing stewardship, defining terms, and educating stakeholders. AI and analytics are not just about tools; they are about trust. The evolution of the CDO into the CDAIO underscores this point leaders must not only deliver insights but also instill confidence that the use of AI aligns with institutional values, ethical frameworks, and the broader mission of Higher Education.
The future of Higher Education will depend on how well institutions can balance innovation with responsibility. Universities that establish strong data governance, invest in analytics and AI talent, and embrace ethical use will not only improve operational efficiency but also redefine how they deliver on their academic mission, all while sustaining student success.