🏷️ Concept
Digital Divide
Digital divide — the unequal distribution of access to, skills for, and benefits from digital (and increasingly AI) technologies across individuals, communities, and nations. In AI education, the digital divide is a central equity concern: generative AI is rapidly reshaping learning, and the gap between those who can use it effectively and critically and those who cannot threatens to deepen existing educational inequalities.
The digital divide is commonly understood as operating across three levels (van Deursen & van Dijk, 2014): the first-level divide concerns access to technologies and infrastructure (connectivity, devices, supportive environments); the second-level divide concerns skills and competencies (the uneven capacity to use tools effectively and meaningfully); and the third-level divide concerns outcomes and benefits (who actually benefits from technology use, with AI potentially exacerbating social, cultural, and economic disparities). Framing AI literacy through this lens makes clear that equity requires more than closing the device-and-infrastructure gap — it requires building the skills to use AI effectively and critically so that its benefits are distributed fairly rather than reinforcing existing inequalities.
How the digital divide appears in the research
AI deepens (and can close) divides
AI adds new layers to the equity implications of technology. Algorithmic bias can disproportionately impact learners from marginalized communities, and AI literacy — the ability to understand, critically evaluate, and mitigate AI's biases and risks — is itself a key factor in whether AI widens or narrows gaps. Research shows educators with higher AI literacy are more effective at identifying and mitigating biased outcomes. The digital divide in the AI era is therefore not simply a technical provision problem but a question of justice and participation: who can access AI, who can use it critically, and who benefits.
Connections to related concepts
The digital divide is a core concern of Equity and Equity In AI Education research, closely tied to AI Literacy (which is positioned as a central mechanism for addressing structural barriers), and to Ethics and Bias Mitigation (since algorithmic bias disproportionately affects marginalized groups). It connects to AI Education and Higher Ed as the settings where access and capability gaps manifest, and relates to Student Experience as it shapes who can participate meaningfully in AI-shaped learning.