Artificial intelligence is transforming the way education is delivered across the world.

Lessons that were once shared through chalk and paper are now mediated by screens, algorithms, and cloud-based platforms.

From virtual tutors to intelligent grading systems, technology is reshaping how students learn and how educators teach in the 21st century.

Yet as innovation progresses, a new inequality is emerging. The digital divide today is no longer just about who can access the internet. It is about who has the skills, confidence, and knowledge to use it meaningfully.

This divide is about capability, representation, and inclusion. It separates those who can harness digital tools for learning and research from those who are left behind, unable to participate fully in a fast-changing digital environment.

Behind every story about AI breakthroughs lies a quieter crisis of digital exclusion. Many communities remain disconnected, not only from the internet but from the skills and opportunities that come with it.

The promise of technology as a tool to democratise learning and expand opportunity is at risk of becoming another source of inequality.

The digital promise and unequal reality

When the internet first became widespread, it was celebrated as the great equaliser. It was expected to bridge gaps in geography, class, and opportunity. More than two decades later, this goal remains incomplete.

While millions of students around the world log in daily to online classrooms, others struggle with limited access, unstable connections, and outdated devices.

The COVID-19 pandemic exposed this inequality clearly. In wealthier communities, families transitioned smoothly to online learning. In contrast, many students in rural or low-income areas faced blank screens and missed lessons.

Access to the internet alone is no longer enough. The new divide is about digital competence and the ability to use technology critically and productively.

Without this, access can remain superficial and may not lead to genuine participation or progress.

This article focuses on three major challenges shaping this evolving divide: algorithmic bias, data inequality, and the second-level digital divide.

Algorithmic bias and data inequality

Artificial intelligence is now widely used in higher education. It supports grading, predicts student performance, and screens applications for scholarships and admissions.

However, many of these systems are built on data that contain historical biases. As a result, they often reproduce the same social inequalities that exist in society.

Studies have shown that facial recognition tools are more likely to misidentify individuals with darker skin tones.

Automated recruitment systems have filtered out women’s résumés. Predictive models label students from lower-income backgrounds as “at risk” based on biased data rather than their actual potential.

This issue extends into research. Global academic databases often index publications from Western institutions more extensively, giving higher visibility to knowledge produced in the Global North.

Research from Asia, Africa, and Latin America remains underrepresented, creating a structural imbalance in how knowledge is recognised and shared.

To move towards fairness, institutions must demand transparency and regular audits for AI systems. Algorithmic fairness should be prioritised to ensure that technology supports inclusion rather than reinforcing discrimination.

The second-level digital divide

In the early years of digital transformation, the main question was who had access to the internet. Today, the more urgent question is who can use it effectively.

Many students spend long hours online but still lack the digital literacy to evaluate sources, use research tools, or manage data privacy.

They may know how to consume information but not how to analyse or create with it. This is called the second-level digital divide, which refers to inequality in skills rather than in access.

In universities, this gap has real consequences. A postgraduate student in Europe may use advanced AI-based software and cloud platforms for research.

Another student in a developing country may depend on free tools with limited features.

This results in a hierarchy of knowledge production, where those with stronger digital literacy and resources are more visible in academic discussions.

Governments and educational institutions must integrate digital literacy and critical thinking into curricula from an early stage.

Students need to learn how to use digital tools responsibly, evaluate information accurately, and apply technology for meaningful learning and research.

Digital access without digital literacy creates the illusion of inclusion.

Towards digital justice

Bridging the digital divide requires more than providing devices and internet access. It demands a commitment to digital justice – an approach that ensures fairness, inclusion, and ethics in every stage of digital development.

Digital justice involves questioning who designs the technology, whose interests it serves, and who benefits from its use.

It calls for policies that promote equitable access, ethical AI governance, and inclusive education that reflects diverse cultures and perspectives.

Education has always been described as the great equaliser. But without digital justice, it could become the next great divider.

A shared responsibility

Policymakers, universities, and technology developers must work together to build inclusive systems. Institutions should review their digital platforms for fairness and accessibility.

Educators must guide students not only in using technology but in understanding its social and ethical implications.

The goal is not simply to connect everyone, but to ensure that everyone can participate fully and fairly in the digital age. Only then can technology truly fulfil its promise as a tool for empowerment.

Digital justice is essential for education that is inclusive, ethical, and equitable. It is not an option, but a responsibility.

-- BERNAMA

Sharon Pasion Vinluan is a Language Lecturer at the Faculty of Language and Linguistics, Universiti Malaya.