Jointly investigate fairness in machine learning with core ML researchers when deploying adaptive collaborative learning support¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The editorial proposes that CSCL researchers should partner with core machine learning researchers to study fairness in trained models used for adaptive collaborative learning support. It states the topics "should be jointly investigated going forward by CSCL researchers in collaboration with core Machine Learning researchers" so that root causes of potential unfairness are understood alongside context of use, enabling effective mitigation strategies.

Design Implications¶

Context¶

Requirements¶

• Deep insights into the root causes of potential unfairness in trained models
• deep expertise into the context of use

Constraints¶

• Automated forms of adaptive collaborative learning support carry potential costs including blind spots, bias, or lack of nuance in underlying model predictions, as the squib weighs

Target Learners¶

• CSCL researchers and machine learning researchers

Target Learning Goals¶

• Equitable design of adaptive collaborative learning support and mitigation of model unfairness

Related Strategies¶

• Regularly audit ML models for fairness and proactively apply bias mitigation during model training
• Use explainability tools (SHAP/LIME) and human monitoring for fair, transparent AI-driven personalized learning
• Use interpretable feature-driven machine learning as a human-in-the-loop baseline for flagging ethnic bias in learning content
• Use this Toolkit: Mitigating Racial Bias in Edtech
• Address core ethical challenges—data privacy, algorithmic fairness, and transparency—when deploying AI-driven personalized learning

Examples¶

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Key Sources¶

• Carolyn P. Rosé & Sanna Järvelä. (2020). Building community together: towards equitable CSCL practices and processes. International Journal of Computer-Supported Collaborative Learning. https://doi.org/10.1007/s11412-020-09329-z