How to Combat AI Bias in the Classroom

“The new risk of algorithmic bias is that it is more systematic than human bias.”

Stéphan Vincent-Lancrin, PhD, Senior Analyst and Deputy Head, Directorate for Education and Skills, Organization for Economic Co-operation and Development (OECD)

As artificial intelligence (AI) becomes a cornerstone of modern education, its potential to revolutionize classrooms is undeniable. AI tools are now used in tasks ranging from personalized learning to automated grading and even predictive systems that identify at-risk students. The OECD’s Digital Education Outlook 2023 highlights this rapid adoption, noting that over 60 percent of schools in high-income countries now employ AI-driven educational tools. However, with these advancements comes a pressing concern: the risk of AI bias.

Studies show that AI systems often reflect the biases of the data they are trained on, unintentionally disadvantaging underrepresented groups. For example, automated essay scoring systems have been found to systematically underrate the work of Black and Hispanic students compared to their peers, while language-learning tools frequently underperform for students with non-standard accents or dialects. Left unaddressed, these biases threaten to entrench systemic inequalities in education.

The challenge for educators, EdTech developers, and policymakers is clear: to ensure that AI supports equity rather than exacerbating disparities. This article delves into the roots of AI bias in education, its impact on marginalized communities, and the strategies needed to combat it. By addressing these issues, stakeholders can harness the full potential of AI to create a more inclusive and effective educational landscape.

To better guide and inform educators and institutions on the subject, the Organization for Economic Co-operation and Development’s (OECD’s) Directorate for Education and Skills recently hosted a panel called “Battling AI bias in the classroom.”

Stéphan Vincent-Lancrin, a senior analyst and deputy head at the OECD’s Directorate for Education and Skills, who moderated the panel and discussed with OnlineEducation.com about their research findings, and actionable solutions to combat AI bias in classrooms.

Dr. Stéphan Vincent-Lancrin is a senior analyst and deputy head of the “Innovation and Measuring Progress” Division at the Organization for Economic Co-operation and Development (Directorate for Education and Skills). He currently leads work on the OECD’s project on digitalization in education, “Smart data and digital technology in education: AI, learning analytics and beyond.”

Dr. Vincent-Lancrin also focuses on disciplined innovation and change management, showing what kind of support, environment, and tools school teachers and university professors could give to improve their teaching and students’ learning. Generally speaking, he works on educational innovation, research, higher education, and how new trends influence the future of learning and education policy at the schooling and higher education levels.

Dr. Vincent-Lancrin holds a PhD in philosophy and economics from the University of Paris, a master’s degree in economics from the Ecole des Hautes Etudes en Sciences Sociales, a master’s degree in philosophy from the University of Paris, and a grande école diploma in public management and action from the ESCP Business School.

Artificial intelligence, while a powerful tool in education, is not immune to the biases that exist in society. These biases often stem from the data used to train AI systems, the algorithms that process this data, and how these tools are implemented in educational settings. In classrooms, such biases can have far-reaching consequences, particularly for students from underrepresented or disadvantaged groups.

According to Dr. Vincent-Lancrin, one of the most common forms of bias in educational AI systems is performance bias. This occurs when an AI tool does not perform equally well for all subgroups of users. “Automated essay scoring systems, early warning systems for predicting course failure, and dropout prediction models often show racial bias, particularly against Black and Hispanic students in the United States,” explains Dr. Vincent-Lancrin. He adds that language-learning tools can also be problematic, as they “work less effectively for students from a migrant background or those with certain accents or ways of speaking.”

The impact of these biases can be profound. Students from marginalized groups may receive unfairly low grades from automated systems, leading to missed opportunities or stigmatization. Worse, these tools can falsely identify some students as needing intervention, which Dr. Vincent-Lancrin warns could lead to unnecessary burdens or interventions that are “useless to them.”

Unlike human bias, which may be inconsistent and situational, AI bias is systematic and widespread, potentially entrenching inequities across entire education systems. “The new risk of algorithmic bias is that it is more systematic than human bias,” Dr. Vincent-Lancrin emphasizes. Without intervention, AI tools could inadvertently widen achievement gaps, as they tend to be more effective for advanced learners than for those who are struggling.

Addressing the roots and impacts of AI bias is not just a technical challenge but a moral imperative. Without intentional safeguards, these systems risk perpetuating inequalities rather than bridging them. Ensuring fairness requires a multi-faceted approach that combines responsible AI design, thoughtful implementation, and continuous oversight. By understanding the nuances of AI bias and its implications, educators and institutions can begin to create learning environments where technology serves all students equitably, empowering rather than disadvantaging the most vulnerable.

Mitigating AI bias in education requires collaboration between educators, EdTech developers, and policymakers. Each stakeholder is crucial in ensuring AI tools are designed, implemented, and used responsibly to promote fairness and inclusivity.

For edtech developers, the responsibility begins at the design stage. Developers must thoroughly test algorithms for potential biases before deploying them in educational settings. Dr. Vincent-Lancrin emphasizes the importance of these steps, noting, “Regulators should put in place mechanisms that allow (or at least do not prevent) the identification of bias and actually mandate that new software is tested against algorithmic bias.” By embedding accountability measures into the development process, EdTech companies can help prevent biased systems from reaching classrooms.

While developers hold much of the responsibility, educators also play an essential role in addressing bias during implementation. Their primary focus should be on how they interpret and use AI outputs, as Dr. Vincent-Lancrin advises: “Educators should not have to take care about it [bias in AI systems]. They should rather focus on their own human biases and think about how to use responsibly the information and AI systems that are made available to them.” Teachers must remain vigilant, ensuring that the insights provided by AI tools do not replace critical thinking or ethical judgment.

Regulators and policymakers have the authority to create the structural changes necessary for addressing bias. Policies such as bias audits, mandated by the EU AI Act and similar frameworks, can help standardize fairness in AI development. However, as Dr. Vincent-Lancrin points out, these policies must be practical to ensure their implementation is effective. He underscores that regulatory frameworks should balance privacy protections with transparency, enabling the identification and mitigation of bias without compromising user rights.

When these stakeholders work together—developers designing ethically, educators using tools responsibly, and policymakers setting clear guidelines—the risks of AI bias can be significantly reduced. These partnerships are vital to creating AI systems that enhance learning for all students, regardless of background.

The lack of widespread policies directly targeting AI bias in education underscores the need for urgent action. While frameworks like the EU AI Act represent initial steps toward addressing bias, practical implementation remains a challenge. Dr. Vincent-Lancrin points out, “At this stage, I am not aware of such policies. I believe the EU AI Act mandates something like that, but it is unclear how in practice this can be implemented.” This gap highlights the need for targeted strategies that translate high-level guidelines into actionable measures within schools and education systems.

One key area for improvement is integrating bias mitigation into privacy policies. While privacy concerns often dominate discussions around AI regulation, Dr. Vincent-Lancrin argues that these policies should also consider the implications of bias. “Regulators should consider bias when they consider their privacy policy so that bias can be identified and tackled,” he advises. Balancing privacy with transparency is essential to uncovering and addressing systemic inequities embedded in AI systems.

In addition to regulatory frameworks, successful practices can emerge from collaborations between governments, educators, and unions. The OECD-Education International Guidelines, for instance, advocate for a dialogue between policymakers and teacher unions to develop ethical and effective uses of AI in education. These collaborative efforts help ground abstract principles in the realities of classroom implementation, ensuring policies are both practical and inclusive.

Addressing AI bias requires more than isolated efforts—it demands systemic change. By embedding equity-focused policies into the core of AI governance and fostering collaborations across sectors, education systems can move closer to realizing the promise of AI as a tool for inclusion and empowerment.

Eliminating bias in AI systems is just the first step; the ultimate goal is to create tools that actively promote equity and inclusivity in education. AI has the potential to narrow achievement gaps and provide tailored support to students from diverse backgrounds—but only if designed and implemented with inclusivity at the forefront.

Dr. Vincent-Lancrin highlights the risk of poorly designed AI systems widening disparities. “AI systems risk being more effective for advanced learners than for those who are struggling,” he cautions. Adaptive learning tools, for example, may prioritize high-achieving students who produce consistent and predictable data, leaving others behind. Developers must build systems that identify struggling students and provide meaningful interventions that address their specific needs.

Through intentional design and ethical use, AI has the potential to avoid bias and actively champion inclusivity, ensuring that every student has the opportunity to thrive in the classroom.

The integration of artificial intelligence in education holds immense promise, but it also carries significant risks if biases are not carefully addressed. As Dr. Vincent-Lancrin notes, “The new risk of algorithmic bias is that it is more systematic than human bias.” This systemic nature requires intentional efforts from developers, educators, and policymakers to ensure that AI systems do not reinforce inequalities but instead foster equity and inclusion in learning environments.

Without decisive action, AI risks entrenching the disparities it can potentially resolve. However, by approaching these challenges with intentionality and a commitment to equity, AI can become a transformative force in education—one that empowers every student, regardless of their background, to thrive.