AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
It is suggested that current efforts to mitigate mode collapse are insufficient for open-ended educational generation, and new training and data collection strategies to support pedagogical diversity are needed.
While various organizations now actively encourage LLM use in classrooms, we still lack rigorous, systematic evaluations of how well these models actually perform the fundamental tasks of language pedagogy. This paper examines whether state-of-the-art LLMs can deliver the kind of corrective feedback and methodological...
Although LLM-based benchmark ratings can approximate expert judgments and reduce the need for labor-intensive human triple coding, limitations remain regarding cost, genre- and task specificity, and sensitivity to text presentation and student grade level—factors that constrain immediate classroom use, particularly for...
Generative AI can produce fluent prose before students have worked through the linguistic choices that writing courses are intended to teach. This study examined how one university instructor responded to that problem in a second-year English-major writing course. Forty-two students (18 men and 24 women; mean age = 20....
ELBench is introduced, the first benchmark to evaluate all four requirements (General Capability, Safety and Trustworthiness, Basic Education, and High-Level Cultivation) on the same models under a common protocol, combining curated public sources with newly synthesized safety and cultivation data.
The increasing prevalence of artificial intelligence (AI), in particular large language models (LLMs), is transforming how scientific texts are produced and consumed. A growing share of online content is at least partially generated by AI, often without readers’ awareness, raising the question of whether students can r...
This work evaluates prompting, supervised fine-tuning (SFT), and reinforcement learning (RL) methods for writing simulation using automated essay scoring as a measure of profile alignment and suggests that explicit supervision enables substantially stronger profile alignment than prompting alone.
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