Editorial
Imagine a student — let’s call her Maria — has worked to overcome barriers on her path to a higher education. As a first-generation college student, she balances coursework with a full-time job, and she relies on university resources to stay on track. One day, Maria applies for a merit scholarship, only to be flagged by an AI-powered system as a “high risk” applicant based on historical data from students with similar backgrounds. Despite her strong academic performance, the system’s algorithm determines she is unlikely to succeed, limiting her access to financial resources.
Maria experienced the consequences of unaddressed bias in AI systems — a reality that goes beyond skewed data. When left unchecked, AI bias restricts access to opportunities, limiting people’s ability to fully participate in both the economy and society.
The AI Dilemma in Higher Ed
Artificial intelligence is transforming our abilities in ways we could have only imagined a decade ago. Today, institutions have access to adaptive learning, automated administrative processes and AI-powered tools that optimize operations, enhance student support and improve faculty engagement. The ability to leverage these systems to provide personalized learning experiences and 24/7 student assistance represents a significant leap forward, addressing some of the most pressing operational challenges in higher education.
However, like many major technological advancements, AI comes with unintended consequences. Just as past innovations have introduced unforeseen challenges in how we live and work, AI’s expansion into higher education raises many unanswered questions and significant ethical concerns — particularly in bias and fairness. Because AI bias isn’t just an abstract problem, it directly impacts students and their educational outcomes.
When these biases reinforce existing disparities, they create inefficiencies for the institution and disparities for others. That’s why institutions can’t afford to overlook the role bias plays in AI-driven decision-making. Higher education is a means for social mobility, and if we want AI to serve that purpose, we need to be intentional about how we design, train, implement and monitor these technologies.
In recent research I co-authored with Dr. James Hutson, we examined these challenges and offered concrete strategies to ensure AI enhances equity rather than deepening existing inequalities.
Related Article: How AI Bias Creates Dependency and Inequality
Understanding AI Bias in Higher Education
AI systems are only as fair as the data that trains them, yet many models still rely on outdated datasets that reinforce bias and inequality.
For instance, an AI system trained on institutional data from the year 2000 — when White students comprised 70.8% of undergraduates — would generate predictions shaped by that demographic reality. Today’s student population is significantly more diverse, with Hispanic students at 20.3%, African American students at 14% and women making up 58% of undergraduates. Failing to reflect these shifts risks misrepresenting marginalized groups and deepening systemic disparities.
Bias in AI distorts institutional decision-making, affecting risk assessments, academic evaluations and hiring practices. Predictive models can disproportionately classify marginalized students as "at risk" based on outdated dropout rates and standardized test scores, failing to account for systemic barriers such as financial instability and unequal access to academic support.
Automated grading tools, intended to assist students, are often trained on Western writing norms, disadvantaging those from diverse linguistic backgrounds. Similarly, AI-driven hiring systems, trained on historical applicant pools, tend to favor overrepresented demographics, further entrenching exclusion rather than promoting diversity.
Addressing these challenges is not just a technical fix — it is an ethical responsibility. Higher education institutions committed to equity must ensure AI serves all students fairly. Dismissing concerns about bias as resistance to innovation ignores the urgent need for responsible AI integration. Until institutions prioritize ethical AI, these technologies will continue to perpetuate inequality rather than correct it.
By implementing bias audits, data augmentation and fairness-aware algorithms, universities can refine AI systems to reflect today’s diverse student body. Integrating representative datasets and continuously monitoring outcomes ensures AI promotes equity rather than reinforcing outdated disparities.
Strategies for Building Ethical AI in Higher Ed
Ensuring AI serves all students equitably requires more than just good intentions — it demands a multi-layered approach that combines technical solutions, institutional policies and AI literacy for both educators and students.
- Diverse and Representative Datasets: AI systems can only be as fair as the data they are trained on. That means institutions must ensure datasets include a broad range of socioeconomic backgrounds, academic performance levels and cultural contexts to prevent AI from reinforcing existing inequalities.
- Transparency and Accountability: AI should never function as a black box — faculty, administrators and students must understand how AI-driven decisions are made. Institutions need to conduct regular audits to ensure their systems remain fair and do not produce biased or discriminatory outcomes.
- Interdisciplinary Collaboration: Ethical AI is not just a tech problem — it requires input from ethicists, educators, policymakers and social scientists. Universities should form cross-disciplinary teams to oversee AI adoption and evaluate its impact on fairness and equity.
- AI Literacy for Faculty and Students: If students and educators don’t understand how AI works, they can’t challenge or question its decisions. That’s why AI ethics and literacy must be integrated into curricula, equipping students to work with AI responsibly and ensuring faculty can critically evaluate AI-driven decisions.
- Governance and Policy Reform: Institutions must establish clear guidelines for AI adoption, ensuring ethical considerations are built into procurement, deployment and oversight. Without policy frameworks, AI risks becoming an unchecked force that dictates academic decision-making without accountability.
Related Article: Tech's Ethical Test: Building AI That's Fair for All
The Path Forward: Creating Fair AI in Education
As AI continues to shape higher education, institutions must be proactive to ensure it enhances learning rather than exacerbates inequities. Without intentional safeguards, AI could deepen the very disparities universities aim to eliminate. But with fair, transparent and inclusive AI practices, institutions can harness AI as a force for equity rather than a barrier to inclusion.
Webinar
Oct
16
Agentic AI Playbook: Real-World Customer Service Use Cases You Can Deploy Now
Boost self-service by 30% and slash call volume by 63% with agentic AI.
Register
Webinar
Oct
22
Beyond Storage: Smarter Content, Bigger Impact with DAM + AI
Discover how the DAM + AI duo makes content smarter, stronger and more accessible.
Register
Webinar
Nov
6
Fix the Content Bottleneck: Build a Better WebOps Strategy
Content stalled? Dev overloaded? You’re not the only one. Learn how streamlined WebOps bridges the publishing gap.
Register
Webinar
On demand
CMS Briefing: A Live Look at What’s Next in AI-Driven Platforms
Learn how leading organizations are using AI‑driven tools to publish faster, personalize smarter and stay secure.
Watch Now
Webinar
On demand
Ready or Not: How Data-First Organizations Are Unlocking Agentforce Potential
Learn how to cut through the noise, activate Agentforce and build a Salesforce AI strategy that actually delivers.
Watch Now
Webinar
On demand
AI in Customer Service: Faster Resolutions, Happier Customers
Don’t let rising demand burn out your team. See how to build a smarter, more resilient support org.
Watch Now
AI’s future in education isn’t set in stone — it depends on the choices we make today. If you’re interested in a deeper dive into these issues, our full research explores these topics in greater detail. I invite educators, policymakers and AI developers to join the conversation and work toward AI systems that uphold fairness, transparency and inclusivity in education.
Learn how you can join our contributor community.
