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This paper introduces a Bloom-aligned framework to assess educational control in Large Language Models (LLMs), focusing on their ability to maintain instructional intent while adjusting cognitive demand for educational purposes. By applying this framework to programming tasks, the authors evaluate two intervention strategies鈥攇eneral difficulty control and Bloom's control鈥攗sing a matched pair of Qwen3 models across 2,520 tasks. The findings reveal a significant asymmetry: while both models can increase cognitive demand, they struggle to decrease it, highlighting that high execution performance does not guarantee effective educational adaptation.
Strong execution in LLMs doesn't equate to effective educational control, as they struggle to lower cognitive demand despite being able to increase it.
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 make tasks harder or easier, and Bloom's control, where models are asked to target higher or lower Bloom's levels. We evaluate a matched Qwen3-Next model pair, comparing Qwen3-Next-80B-A3B-Instruct with Qwen3-Coder-Next across 2,520 tasks from three benchmarks. The framework reveals a robust directional asymmetry: both models reliably increase cognitive demand, but struggle to lower it. We further characterize these outcomes with semantic-delta clustering and layer-wise Fisher's Discriminant Ratio probing. Within this controlled comparison, the general model shows clearer middle-layer separability for both general difficulty and Bloom-control contrasts, whereas the coder model shows weaker separability for general difficulty and a deeper peak for Bloom-control contrasts. These results show that strong execution performance does not automatically entail Bloom-aligned educational control.
