Policy Risk and Innovation A is the central topic of this scientific publication.
Abstract
This study examines how the adoption of artificial intelligence (AI) and the implementation of policies shape inclusive educational outcomes for marginalized learners in Bangladesh, using evidence from Sherpur Sadar Upazilla. A convergent mixed-methods design integrated a student survey (N = 213; seven institutions; March–September 2024) with qualitative data from 37 stakeholders (teachers and policymakers) collected through semi-structured interviews and focus group discussions. Quantitative findings show that AI tool adoption was the strongest predictor of a composite educational outcome score (β = 0.38, p < 0.001), followed by institutional support (β = 0.25, p = 0.01). In contrast, the policy implementation gap—defined as the mismatch between policy intent and on-the-ground delivery—was negatively associated with outcomes (β = −0.12, p = 0.04). Digital infrastructure quality was positively associated with the outcome but was not statistically significant in the multivariable model (β = 0.17, p = 0.12). The model demonstrated strong explanatory power (R² = 0.67; F(4, 208) = 42.3; p < 0.001). Disparity analyses revealed persistent urban–rural inequities in reliable internet access (94.6% vs. 69.7%) and device readiness, with tablet access emerging as a key enabler of advanced AI-supported learning. Qualitative results corroborated three binding constraints: limited teacher AI preparedness, affordability barriers, and trust concerns related to privacy and algorithmic bias. Building on these findings, the paper proposes a policy–innovation framework centered on localized AI toolkits, sustained teacher upskilling, device-access interventions, and enforceable fairness and transparency safeguards to advance equitable learning opportunities.
Conclusions
This study shows that artificial intelligence can support more inclusive and equitable education in Bangladesh when it is applied as a pedagogical enhancer (e.g., personalized learning environments and intelligent tutoring systems) rather than treated primarily as a curricular subject. Mixed-methods evidence from Sherpur Sadar Upazilla indicates that AI-related benefits are not distributed evenly: improvements are shaped by a persistent rural–urban implementation gap and by differences in schools’ capacity to translate technology into learning gains. The findings highlight that effective AI-supported learning depends less on general “technology availability” and more on whether learners and schools have access to suitable learning devices, reliable connectivity, and the instructional capacity to integrate AI into everyday teaching practices.
The study also underscores that policy impact is determined by implementation quality, not only policy adoption. Where policies are not fully operationalized at the school level—through appropriate resourcing, training, and support—AI initiatives tend to produce uneven outcomes and may unintentionally reinforce existing inequalities. In addition, risk and trust factors are central to sustainable adoption. Concerns related to privacy, bias, and transparency—especially in disadvantaged settings—can reduce acceptance and participation, underscoring the need to embed ethical safeguards into AI deployment rather than address them only after systems are scaled.
On this basis, three priorities emerge. First, efforts to improve inclusion should focus on device readiness and infrastructure parity, ensuring that marginalized learners can access AI tools that support advanced learning activities rather than only basic, mobile-compatible functions. Second, teacher development and institutional support should be strengthened through sustained, locally relevant training and ongoing mentoring that helps educators translate AI tools into effective classroom practice. Third, governance and risk protections should be reinforced through precise accountability mechanisms, transparency practices, and practical fairness safeguards that build trust and reduce harm for vulnerable groups.
Future research should assess the long-term educational effects of different device strategies, compare tablet-centered and mobile-first approaches under realistic cost constraints, and evaluate scalable teacher-support models that can be embedded within existing education systems. Additional work is also needed to refine culturally adaptive AI tools and communication approaches that strengthen trust and participation across diverse communities. Overall, the evidence suggests that AI can advance educational inclusion, but only when innovation is paired with targeted implementation capacity and robust safeguards so that technology reduces—rather than reproduces—structural disadvantage.
How to cite
Karmaker, R., & Cvetković, V. M. (2026). Policy, Risk and Innovation: A Mixed-Methods Framework for Using AI to Foster Inclusion in Marginalized Communities in Bangladesh. International Journal of Disaster Risk Management, 8(1), 385-408. https://doi.org/10.66050/scv48268
