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:2509. 13570v2 Announce Type: replace Abstract: With the rapid rise of generative AI in higher education, understanding how students use AI is increasingly important.
By Hannah Klawa, Shraddha Rajpal, Cigole Thomas
The study investigates how generative AI (GenAI) affects student learning in AI-related courses, using survey data from 118 students across 12 courses. Four distinct user clusters were identified—high-use, light-use, and two moderate-use groups—each showing varying benefits and reliance patterns. The research highlights that early reliance, evaluation literacy, and instructor policies significantly influence perceived academic benefits and negative impacts, underscoring the need for institutional policies to address inequities in AI use.
By Lydia Manikonda, Mei Si, Sirajam Munira, Oshani Seneviratne, Kristin Bennett
arXiv:2605. 21629v2 Announce Type: replace-cross Abstract: How much have students' ordinary learning processes shifted in response to generative AI, and how does that affect their durable learning outcomes?
By Sina Rismanchian, Hasan Uzun, Jeffrey Matayoshi, Eric Cosyn, Eyad Kurd-Misto
arXiv:2606. 12441v1 Announce Type: cross Abstract: The four dominant learning theories of behaviorism, cognitivism, constructivism, and connectivism show significant conceptual limitations as generative artificial intelligence (AI) proliferates in educational settings.
By Shan Li, Juan Zheng
arXiv:2604. 27618v2 Announce Type: replace-cross Abstract: Understanding the impact of large language models (LLMs) on mathematics education requires data on LLMs' mathematical performance and biases.
By Naomi Esposito, Anthony Tricarico, Luisa Porzio, Ali Aghazadeh Ardebili, Massimo Stella