From Execution to Education: A Bloom-Aligned Framework for Measuring Educational Control in LLMs
arXiv:2607.08009v1 Announce Type: new Abstract: We introduce a Bloom-aligned framework for measuring educational control in Large Language Models (LLMs): the ability to preserve a task's instructional intent while shifting its cognitive demand toward specified learning objectives. We apply this framework to programming tasks in computer science education to study the gap between solving tasks and adapting them for learners. Using revised Bloom's Taxonomy as an operational scale of cognitive demand, we evaluate two intervention settings: general difficulty control, where models are asked to mak
The rapid advancement and widespread adoption of LLMs necessitate robust methods for evaluating their nuanced capabilities, especially in critical application areas like education.
This framework addresses a core challenge in LLM development: moving beyond mere task execution to sophisticated pedagogical control, which is essential for safely and effectively integrating AI into learning processes.
The ability to systematically measure an LLM's 'educational control' introduces a new dimension for evaluating AI, shifting focus from raw performance to its adaptability for specific human-centric objectives like teaching.
- · AI developers focused on education
- · EdTech sector
- · Students
- · Researchers in AI safety and alignment
- · LLMs with limited pedagogical adaptability
Improved educational outcomes through AI-adapted learning materials.
Increased demand for LLMs capable of fine-grained instructional control, leading to specialized model development.
Potential for AI to personalize education at scale, fundamentally altering traditional teaching methodologies and potentially closing achievement gaps.
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Read at arXiv cs.CL