3.7. Key Concepts and References

Key Concepts

• Fairness: Ensuring that AI systems do not systematically disadvantage individuals or groups based on characteristics such as race, gender, class, or ability. Fairness aligns with principles of equity in education and society, requiring continuous auditing of datasets and outputs (Mehrabi et al., 2021).
• Bias: The presence of skewed or unrepresentative data and assumptions in AI models that can lead to distorted, unfair, or discriminatory results. In cognitive terms, this parallels human cognitive biases, but in AI it originates from training data and design choices (Barocas et al., 2023).
• Transparency: The practice of making AI processes, datasets, and decision-making mechanisms open, understandable, and reviewable by users and stakeholders. Transparency supports trust-building and aligns with the information processing principle of metacognition, where learners monitor and evaluate thinking.
• Accountability: Assigning responsibility for AI-driven outcomes and ensuring there are clear pathways to address harm, misuse, or errors. Accountability highlights the role of human oversight in preventing technological determinism and preserving ethical agency.
• Privacy: The safeguarding of personal information and the respect for individuals’ rights over how data is collected, stored, analyzed, and shared. Protecting privacy resonates with self-regulated learning principles, where control over one’s information is linked to autonomy and agency.
• Human Agency: Preserving human capacity to make meaningful choices, exercise judgment, and maintain authority over decision-making. This concept resists over-reliance on AI and reflects constructivist theories that learning and action are human-driven.
• Explainability: The ability of AI systems to provide clear, interpretable justifications for their outputs. Explainability enables users to evaluate, verify, and learn from AI recommendations, paralleling how educators make thinking visible in the classroom (Chi & Wylie, 2014).
• Ethical Use of Data: Applying responsible practices in the collection, analysis, storage, and sharing of data to prevent exploitation or harm. Ethical data stewardship is foundational for both research integrity and AI development.
• Sustainability: Considering the environmental, social, and economic costs of AI training and deployment, including the energy footprint of large models and broader global impact. This principle situates AI within systems thinking and ecological literacy frameworks.
• Professional Integrity: Upholding honesty, transparency, and ethical responsibility when using, teaching, or deploying AI in academic and professional contexts. Professional integrity ensures AI is used as a tool to augment—not undermine—trustworthy human work.

References

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