AI system developers should ensure human-centric design, institutional leaders should stay updated on global AI regulations, and LA experts should engage policymakers through dialogue¶

Strategy · All strategies
Evidence · no claims cited

Description¶

This strategy distills the article's Notes for Practice into implementable practices for the LA community. The article states that "AI system developers should ensure that AI systems are human-centric and prioritize user needs," that researchers and institutional leaders should stay updated and comply with global AI regulations, and that through dialogue, LA and AI experts should work with policymakers and implementers to understand the trade-offs of specific regulations as they are proposed. These practices aim to ensure legality, minimize risks, and keep AI-driven LA systems ethical and compliant.

Design Implications¶

Context¶

Requirements¶

• Developers must prioritize user needs in AI systems; institutional leaders must monitor and comply with evolving global AI regulations

Constraints¶

• Regulations differ across jurisdictions and are constantly evolving, so compliance practices must be revisited as policies change

Target Learners¶

• AI system developers
• Learning analytics researchers
• Institutional leaders and policymakers

Target Learning Goals¶

• Ensuring AI-driven learning analytics systems are human-centric, legally compliant, and ethically deployed

Related Strategies¶

• LA researchers and practitioners should adapt to changing regulations across jurisdictions, use regulatory sandboxes, and carefully consider data nature and privacy risks before cross-border data transfer
• Center co-design with teachers and students throughout AI tool R&D, using human-in-the-loop workflows as a primary risk mitigation
• Involve teachers as co-designers of AI-based intelligent systems, with agency to train, correct, interpret, and override system recommendations
• Ground AI integration in higher education on core principles of human agency, academic freedom, transparency, ethics, inclusivity, and critical thinking
• Design AI-integrated, ethically grounded, and adaptable business education models guided by bibliometric evidence
• Integrate GPT models into LA-supported feedback systems with an iterative instructor-student validation loop to improve reliability

Examples¶

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Key Sources¶

• Kaliisa, R., Baker, R. S., Wasson, B., & Prinsloo, P. (2025). The Coming but Uneven Storm: How AI Regulation Will Impact AI and Learning Analytics Research in Different Countries. Journal of Learning Analytics, 12(2). https://doi.org/10.18608/jla.2025.8495