Research Article
Decoding Divides: The Role of Socioeconomic Status and Personality Traits in AI Divides and Educational Inequality
Synthesis: Wang and colleagues investigate whether integrating AI into primary education deepens the digital divide and educational inequality, and whether socioeconomic status (SES) or personality traits matter more for that risk. Using survey and national registry data from 4,497 Grade 6 students in the Netherlands, the study models the mediating roles of AI usage and digital literacy in the links between student background, personality, and academic performance.
Key Findings
- Digital literacy — not AI usage intensity — is the key mediator. The link between personality traits and academic performance operates through students' digital literacy rather than how intensively they use AI.
- A personality-driven digital skills divide emerges. Differences in digital literacy are driven more by personality traits than by socioeconomic status, complicating the classic SES-focused framing of the digital divide.
- SES advantages operate independently of AI engagement. Socioeconomic advantages on performance persist even apart from how students engage with AI, suggesting AI use is neither the main driver of inequality nor an automatic equalizer.
- The findings point to a more nuanced digital divide in AI-era primary education, where skills and dispositions — not just access or SES — shape who benefits.
Implications for Practice
- For primary educators: Digital literacy and its development matter more than raw AI access or usage; teaching AI-era digital skills is an equity-relevant intervention.
- For policymakers: Equity policy should target digital-skills formation and personality-supportive learning environments, not merely device or AI-tool access.
- For researchers: The study models AI-usage and digital-literacy as distinct pathways, urging future work to separate "access/usage" from "skills" when measuring AI-related inequality.
Connected Concepts
Connected Articles
- Generative AI and the Productivity Divide: Human-AI Complementarities in Education — the GenAI productivity divide
- Access is Not Enough: Human Support Improves Engagement with AI Tutoring — access alone does not equal benefit
- The Illusion of Competence: Self-Perceived Digital Literacy and AI Readiness Among European Secondary Students — assumptions about digital competence
- Why Put in This Much Effort?": How AI Availability Shapes Students’ Motivation in Introductory Programming — how AI availability shapes student dispositions
Citation
Decoding divides: The role of socioeconomic status and personality traits in AI divides and educational inequality — Wang, Z., van Wetten, S., Segers, E., & Haelermans, C. (2026). Computers and Education: Artificial Intelligence, 10, 100566.