Negotiated learning tasks: learners co-construct assignments with AI that meet curricular goals while being personally meaningful¶

Strategy · All strategies
Evidence · no claims cited

Description¶

In this envisioned system, the AI knows the teacher's learning objectives and the student's current state, draws out the student's interests and background, and together they negotiate a task meeting curricular requirements while being personally meaningful. The paper states it supports genuine autonomy (SDT), shifts goal structures toward personally relevant mastery (AGT), and operationalizes interest development by making interest elicitation the starting point of task design.

Design Implications¶

Context¶

Requirements¶

• The AI must ensure work taps curricular goals and targeted knowledge components; the teacher defines the space and objectives

Constraints¶

• Open research questions include balancing learner agency with consistent learning goals and distinguishing productive negotiation from task avoidance

Target Learners¶

• School students working within curricular requirements

Target Learning Goals¶

• Personally meaningful mastery of curricular knowledge components; willingness to persist through difficulty

Related Strategies¶

• Co Constructing Rubrics With Students

Examples¶

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Key Sources¶

• H. Chad Lane. (2026). AIED's Unfinished Mission: Centering Agency and Motivation in the Age of Effortless Bypass. AIED 2026, CCIS 3032, pp. 502–516. https://doi.org/10.1007/978-3-032-29791-4_38