从执行到教育：一个用于衡量大语言模型中教育控制的布鲁姆对齐框架
From Execution to Education: A Bloom-Aligned Framework for Measuring Educational Control in LLMs
AI总结：
研究提出用于衡量大语言模型中教育控制的布鲁姆对齐框架，应用于编程任务。通过比较一对匹配模型在不同干预设置下的表现，揭示模型提高认知需求易、降低难的不对称性，并用语义 - δ聚类等方法刻画结果，表明强执行性能不意味着有教育控制能力。
摘要：
我们引入了一个用于衡量大语言模型（LLMs）中教育控制的布鲁姆对齐框架，即模型在将任务的认知需求转向特定学习目标时保留任务教学意图的能力。我们将此框架应用于计算机科学教育中的编程任务，以研究解决任务与为学习者调整任务之间的差距。使用修订后的布鲁姆分类法作为认知需求的操作量表，我们评估了两种干预设置：一般难度控制，要求模型使任务更难或更容易；布鲁姆控制，要求模型针对更高或更低的布鲁姆水平。我们评估了一对匹配的Qwen3 - Next模型，将Qwen3 - Next - 80B - A3B - Instruct与Qwen3 - Coder - Next在来自三个基准的2520个任务上进行比较。该框架揭示了一个稳健的方向不对称性：两个模型都能可靠地提高认知需求，但难以降低它。我们进一步用语义 - δ聚类和逐层Fisher判别比探测来刻画这些结果。在这种受控比较中，通用模型在一般难度和布鲁姆控制对比方面都显示出更清晰的中层可分离性，而编码器模型在一般难度方面显示出较弱的可分离性，在布鲁姆控制对比方面有更深的峰值。这些结果表明，强大的执行性能并不自动意味着与布鲁姆对齐的教育控制。
英文摘要：
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.
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