Co-constructing concepts: a participatory inquiry into slow learners’ engagement with an adaptive AI tutor

Research from Educational Technology Research and Development (International) highlights the questionable universal effectiveness of adaptive Artificial Intelligence (AI) platforms for personalised learning, particularly concerning atypical learners. A participatory inquiry involving 'slow learners' in rural Indonesia as co-researchers for an adaptive AI mathematics tutor revealed that their nuanced lived realities are often overlooked in current AI design, stressing the need for inclusive design methodologies.

Why it matters

This research underscores a critical gap in the development and deployment of adaptive AI technologies, revealing that their design may not adequately serve diverse user populations. For leaders and policymakers, this highlights the necessity for more inclusive and participatory design processes to ensure technology's equitable and effective application across varying contexts and user capabilities.

Key insights

• The universal effectiveness of adaptive AI platforms for personalized learning is questioned, especially for atypical learners.
• Current adaptive AI designs often fail to account for the nuanced lived realities of 'slow learners'.
• A qualitative case study in rural Indonesia utilized a participatory usability framework, positioning 'slow learners' as expert analysts of their own learning experiences.
• The study engaged five primary school students, identified as 'slow learners' in their local context, as active co-researchers in evaluating an adaptive AI mathematics tutoring system (ALEKS).
• The research approach emphasizes co-construction of concepts and iterative qualitative inquiry to address gaps in understanding AI's impact on diverse student populations.

Source

Educational Technology Research and Development — https://link.springer.com/article/10.1007/s11423-026-10702-2