S. Bekkouch, T. Constantinou, M. Ovaere, et al. (2026) — arXiv preprint. Venue: arXiv:2607.08009.
Introduces a Bloom-aligned framework for measuring 'educational control' in LLMs: the ability to preserve a task's instructional intent while shifting its cognitive demand toward higher-order Bloom levels, offering a metric for evaluating whether AI assistance scaffolds or shortcuts learning. The work connects to broader debates about how generative-ai systems reshape student-experience and the conditions under which AI support scaffolds rather than undermines learning. It has direct implications for pedagogy-ai-mistakes and the risk of over-reliance when assistants absorb too much of the cognitive load. Findings also bear on ai-literacy and self-regulated-learning, and on how institutions should govern student-ai-interaction and academic-integrity. Practitioners in higher-ed and teachers can use the evidence to calibrate when to deploy llm-based help and how to pair it with feedback that preserves learning gains.
Related Pages
- scaffolding — see this paper's treatment of the topic
- llm — see this paper's treatment of the topic
- generative-ai — see this paper's treatment of the topic
- feedback-loop — see this paper's treatment of the topic
- over-reliance — see this paper's treatment of the topic
- higher-ed — see this paper's treatment of the topic