The study examines how generative AI tools like ChatGPT perform on typical first‑year undergraduate mathematics assessment questions. By generating, transcribing, and blind‑marking AI responses to eight assessments covering the entire curriculum, the authors find that AI attains a first‑class level of performance, with consistency across modules that exceeds that of students in invigilated exams. The results suggest a need to redesign mathematics assessments to address the impact of generative AI.
By Benjamin J. Walker, Nikoleta Kalaydzhieva, Beatriz Navarro Lameda, Ruth A. Reynolds
arXiv:2606. 06546v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know.
By Tao Liu, Ye Lu, Ruohua Zhang, Siyu Song, Wentao Liu, Aimin Zhou, Hao Hao
arXiv:2606. 15591v1 Announce Type: new Abstract: Generating high-quality Physics Word Problems (PWPs) that are novel, complex, and solvable remains a challenging and underexplored problem in educational content generation.
By Tirthankar Mittra
arXiv:2608.30035v1 Announce Type: new
Abstract: Self-evolving reasoning frameworks train a Challenger to generate questions exposing a Solver's weaknesses, creating adaptive curricula without human d...
By Vinoth Selvendran, Zhanming Zhang
The study reports a mixed‑methods pilot evaluation of an AI‑orchestrated research workflow in which a human researcher coordinated multiple LLM‑based agents to perform data extraction, corpus construction, artifact generation, and artifact evaluation. Using the generation and assessment of multiple‑choice questions (MCQs) as a testbed, the team collected 1,071 SAT Math MCQs and employed LLM agents to extract questions from PDFs, retrieve and convert open textbooks into structured representations, align each MCQ with relevant textbook content, generate new MCQs under specified difficulty and cognitive levels, and evaluate both original and generated MCQs using a 24‑criterion quality framework. Across all evaluations, average MCQ quality was high, but criterion‑level analysis and equivalence testing show that generated MCQs are not fully comparable to expert‑vetted baseline questions, with persistent gaps in skill depth, cognitive engagement, difficulty calibration, and metadata alignment.
"whyItMatters":"The findings highlight how AI‑orchestrated workflows shift researcher labor toward specification, orchestration, verification, and governance, underscoring emerging AI research operations skills needed for AI‑empowered research pipelines."
By Yuan An
arXiv:2601. 07055v2 Announce Type: replace Abstract: As high-quality data becomes increasingly difficult to obtain, self-evolution without curated training data has emerged as a promising paradigm.
By Zhenrui Yue, Kartikeya Upasani, Xianjun Yang, Suyu Ge, Shaoliang Nie, Yuning Mao, Zhe Liu, Dong Wang