Mitigation strategies pairing each AI-DSS challenge with a targeted countermeasure¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The review maps each identified challenge—model interpretability, algorithmic bias, scalability, data security, ethical implications, cost and infrastructure, and resistance to adoption—to a specific mitigation strategy, such as explainable AI models, diverse representative datasets, cloud-based architectures, encryption and GDPR/FERPA compliance, ethical design frameworks with stakeholder input, open-source tools, and training that emphasizes evidence-based pedagogical benefits. As the article puts it, institutions should "Offer training and emphasize evidence-based pedagogical benefits" to address educator resistance, and adopt explainable AI models such as decision trees for interpretability.

Design Implications¶

Context¶

Requirements¶

• Stakeholder engagement and comprehensive training programs to demonstrate AI-DSS value
• Regular fairness audits, e.g., maintaining disparate impact ratios below 0.8

Constraints¶

• Resource-constrained institutions often lack the budget to deploy advanced systems, limiting equitable access

Target Learners¶

• online learners in MOOCs, institutional LMSs, and adaptive learning environments

Target Learning Goals¶

• equitable and trustworthy AI-supported learning and assessment

Related Strategies¶

• Address core ethical challenges—data privacy, algorithmic fairness, and transparency—when deploying AI-driven personalized learning
• Audit AI vocal-assessment systems for bias, transparency, privacy, and recording-context robustness before evaluative use
• AI in Education Toolkit for Racial Equity
• Use this Toolkit: Mitigating Racial Bias in Edtech
• Teachers adopting EAI should address digital literacy and ethical design challenges identified for each AI dimension
• Integrate IoT and biometric technologies to augment the security, scalability, and operational efficiency of online proctoring systems

Examples¶

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Key Sources¶

• Mahamad, S.; Chin, Y.H.; Zulmuksah, N.I.N.; Haque, M.M.; Shaheen, M.; Nisar, K. (2025). Technical Review: Architecting an AI-Driven Decision Support System for Enhanced Online Learning and Assessment. Future Internet 2025, 17, 383. https://doi.org/10.3390/fi17090383