Double-Edged Sword or Sharp Tool? Designing and Evaluating Triadic LLM-Teacher Collaboration for K-12 Writing at Scale

Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know. Existing benchmarks emphasize domain-general correctness or depend on manually designed rubrics that scale poorly to long-tail pedagogical scenarios. We introduce Elmes*, an end-to-end framework for constructing, refining, and applying fine-grained scenario-specific rubrics. Elmes* combines a declarative multi-agent engine for teacher--student--judge interactions with SceneGen, a self-evolving module that co-optimizes evaluation criteria and test data from expert-defined pedagogical dimensions. Using Elmes*, we build Edu-330, covering 330 scenarios across 11 subjects, 3 grade bands, and 10 task types, with over 1{,}000 second-level indicators. Experiments on Edu-330 and four expert-authored gold-standard scenarios show that educational capability is multidimensional: top-tier LLMs differ mainly in creativity and values integration, knowledge-strong models may fail at Socratic scaffolding, and the education-specialized InnoSpark achieves the best human-evaluated average score. LLM judges preserve human-comparable rankings with much lower scoring variance, but exhibit judge-specific biases such as self-preference. Ablations show that expert-scored few-shot anchoring improves human--LLM alignment, while reasoning enforcement and greedy decoding are model-dependent. Elmes* thus provides scalable diagnostic infrastructure for pedagogically grounded LLM evaluation.

Qualitative researchers increasingly encounter interaction corpora whose scale exceeds what manual coding alone can address, and large language models (LLMs) are frequently proposed as analytic assistants. The open questions are not whether LLMs can participate in qualitative analysis but to what extent, in what phases, and under what safeguards. This article provides a detailed procedural account of a multi-phase human-LLM collaborative pipeline that adapted open, axial, and selective coding to develop a hierarchical codebook from 45,000 messages exchanged between K-12 educators and a generative AI platform. Across three phases, LLMs generated candidate labels and structured annotations at scale, while human researchers retained conceptual authority over category definitions, merging decisions, and interpretive frameworks. The resulting instrument was then tested through systematic human coding, in which three trained coders with educational domain expertise applied the codebook to an independent sample of 2,560 messages, established reliability through iterative calibration using set-valued agreement measures appropriate for multi-label annotation, and extended the instrument with five codes that the LLM-assisted phases had not surfaced. The final codebook comprises 72 items within 19 categories and six domains. We reflect on the methodological decisions the pipeline required, including the choice of a conversational unit of analysis, the treatment of the LLM as a labeling instrument rather than an interpretive agent, the measurement of intercoder agreement under multi-label coding, and the conditions under which human domain expertise remained decisive. The account is offered as an auditable template for qualitative researchers considering LLM assistance in codebook development while preserving human interpretive authority.

Students increasingly use the assistance of large language models (LLMs) in their academic writing. While slight assistance (e.g., grammar and style correction, as well as feedback) is permitted under most institutional policies, it is usually forbidden to offload entire writing tasks to LLMs. Unfortunately, current approaches to LLM-generated text detection predominantly assume a binary distinction between human-written and LLM-generated text, ignoring the breadth of realistic human-AI collaboration practices and limiting the validity of detection systems for educational assessment. In this paper, we propose a contribution-aware evaluation framework for LLM-based detection systems in education. We introduce a scale of eight student contribution levels that model realistic writing scenarios ranging from fully human-written texts to LLM-assisted revisions to fully LLM-generated and adversarially humanized texts. Institutional policies regarding LLM use can then be translated to thresholds of acceptable LLM assistance on this scale. We further present Generative Essay Detection in Education (GEDE), a novel benchmark dataset comprising more than 900 human-written and over 12,500 generated essays across 886 tasks and all contribution levels. Using this benchmark, we conduct a systematic evaluation of four state-of-the-art zero-shot and supervised detection methods across policy boundaries, contribution levels, generative models, out-of-distribution data, and text length. We show that most detectors struggle to accurately classify texts at intermediate student contribution levels, in particular LLM-assisted revisions of human-written texts. Such errors pose a substantial risk of false accusations, indicating that current text detection systems are (still) unsuitable to reliably support the enforcement of institutional policies regarding LLM assistance in education.