Use explainability tools (SHAP/LIME) and human monitoring for fair, transparent AI-driven personalized learning¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends pairing AI-driven learning predictions with interpretability techniques and oversight. It states that "Techniques like SHAP or LIME can determine which input properties have the greatest effect on the model's conclusions, improving interpretability for stakeholders and educators", and, in its ethical implementations section, advises "utilising explainability tools and human monitoring" for fair and open decision-making in individualized learning settings. The strategy also includes examining model performance across groups and balancing datasets to minimise bias.

Design Implications¶

Context¶

Requirements¶

• Access to SHAP or LIME (or similar) explainability tooling applied to the trained model
• Human oversight of model-driven decisions in personalized learning settings

Constraints¶

• The article notes its models are otherwise uninterpretable and that ethical concerns like data protection and fairness received limited focus

Target Learners¶

• students in AI-supported online learning platforms

Target Learning Goals¶

• transparent and equitable personalized learning decisions

Related Strategies¶

• Address core ethical challenges—data privacy, algorithmic fairness, and transparency—when deploying AI-driven personalized learning
• Use GenAI-co-created data comics to communicate data insights to novice students, with human oversight to address misinformation and bias risks
• Use interpretable feature-driven machine learning as a human-in-the-loop baseline for flagging ethnic bias in learning content
• Deploy agentic AI in education with transparency about agentic decisions, human oversight and institutional responsibility
• Jointly investigate fairness in machine learning with core ML researchers when deploying adaptive collaborative learning support

Examples¶

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Key Sources¶

• Li C, Yin W. (2025). Interactive online learning method for students based on artificial intelligence. Discover Artificial Intelligence 5:201. https://doi.org/10.1007/s44163-025-00401-x