Use inclusive design, calibrated supports, and clear delegation norms to preserve learner agency and epistemic diversity¶

Strategy · All strategies
Evidence · no claims cited

Description¶

This governance-oriented strategy addresses the conditions under which Agentivism's learning mechanisms can operate well. The article argues that interface design, institutional rules, accessibility provisions, and norms for acceptable AI assistance influence whether learners remain active in delegation, verification, and reconstruction or are encouraged toward passive uptake. It states that "Inclusive design, calibrated supports, and clear expectations about acceptable delegation are therefore not external add-ons to learning; they shape whether learners have a genuine opportunity to remain agentic while using AI". It further argues that "preserving epistemic diversity is not only a fairness concern but also a learning concern", since repeated reliance on the same systems may narrow the diversity of sources, framings, and questions learners encounter.

Design Implications¶

Context¶

Requirements¶

• Clear expectations about acceptable delegation and calibrated supports must be established so learners can remain agentic while using AI.

Constraints¶

• These contextual conditions are especially important where learners differ in prior knowledge, resources, and vulnerability.

Target Learners¶

• Learners using AI across differing levels of prior knowledge, resources, and vulnerability

Target Learning Goals¶

• Preserving learner agency, judgment, and epistemic diversity during AI-supported learning

Affordances¶

• Agentivism Learning Theory

Related Strategies¶

• Design AI coaching systems to preserve user agency, recognize vulnerability, and stop sycophantic encouragement during maladaptive thought processes
• Study learning with AI by varying the interactional arrangement and measuring process and delayed reduced-support outcomes

Examples¶

-

Key Sources¶

• Lixiang Yan and Dragan Gašević. (2026). Agentivism: a learning theory for the age of artificial intelligence. https://arxiv.org/abs/2604.07813