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:2608.28601v1 Announce Type: new
Abstract: Interactive visualizations support conceptual understanding in undergraduate mathematics, but building them has required programming expertise most ins...
By Mahesh Sunkula, Kuan-Hua Chen
arXiv:2511. 13271v2 Announce Type: replace-cross Abstract: The rise of Generative AI (GenAI) tools like ChatGPT has created new opportunities and challenges for computing education.
By Rufeng Chen, Shuaishuai Jiang, Jiyun Shen, AJung Moon, Lili Wei
The paper titled "With a Thermomix You Lose the Ability to Cook: A Kitchen Machine Analogy for Applications of Generative AI in Education" uses the Thermomix kitchen appliance as an analogy to explore how generative AI tools like ChatGPT are adopted in education. By mapping Thermomix use cases onto learning scenarios and situating them within the ICAP and SAMR frameworks, the authors illustrate how different modes of tool use can either support or undermine meaningful engagement and learning. The study emphasizes that the key issue is not merely whether learners use AI, but how such use shapes their learning processes, offering a conceptual lens for researchers and practitioners to critically examine and guide AI integration in education.
By Nikol Rummel, Valentina Nachtigall, Ernesto Panadero
arXiv:2608.21391v1 Announce Type: cross
Abstract: In this research-to-practice paper we present a survey that can be used to assess students' AI knowledge. As the use of artificial intelligence (AI),...
By Aditya Johri, Cory Brozina, Akriti Bagale
arXiv:2607. 12296v1 Announce Type: cross Abstract: With the increased use of generative AI (GenAI) applications such as ChatGPT, higher education institutions (HEIs) have released a range of guidelines and policies to direct adoption within their institutions.
By Amrita Ganguly, Aditya Johri, Nora McDonald, Areej Ali, Umama Dewan, Aayushi Hingle Collier
arXiv:2607. 24755v1 Announce Type: cross Abstract: This full research paper examines how different forms of learner-AI interaction relate to learning outcomes in object-oriented programming (OOP) courses.
By Marina Lepp
The study examined the impact of expert‑verified AI‑generated study materials on first‑year university economics students. In a two‑cohort difference‑in‑differences design, half of the 170 students received podcasts, FAQs, and quiz‑based guides that were produced by a source‑grounded model and checked by a graduate teaching assistant. The intervention produced a 2.34‑mark advantage on a 50‑mark component, reduced the share of marks below the upper‑second classification boundary by 24.7 percentage points, and had the largest gains for students in the bottom quintile. Interviews with 36 students suggested that the verification label encouraged engagement while preserving critical scrutiny of the AI output.
By Canh Thien Dang, An Nguyen
arXiv:2601. 18685v4 Announce Type: replace-cross Abstract: The capabilities of generative AI in mathematics education are rapidly evolving, posing significant challenges for research to keep pace.
By Anselm Strohmaier, Samira B\"odefeld, Oliver Straser, Frank Reinhold
The paper describes a pilot study of a generative AI practice platform designed to give immediate, scaffolded feedback to students in a large mathematics class. By moving human oversight to the verification of solutions rather than real‑time grading, the platform aims to reduce delays while maintaining trust and accountability. The study investigates student engagement, perceived value, and reliability of the AI feedback, and explores how these findings might apply to other engineering subjects.
By Lili Chen, Gavin Buskes, Yuxin Ren, Chin Tong Leong
arXiv:2606. 04273v1 Announce Type: new Abstract: For centuries, human mathematicians have written proofs to substantiate their mathematical arguments; yet, the ability to automatically verify the validity of proofs has long been a challenge.
By Katherine M. Collins, Simon Frieder, Jonas Bayer, Jacob Loader, Jeck Lim, Peiyang Song, Fabian Zaiser, Lexin Zhou, Shanda Li, Sam Looi, Joshua B. Tenenbaum, Umang Bhatt, Adrian Weller, Jose Hernandez-Orallo, Cameron E. Freer, Valerie Chen, Ilia Sucholutsky
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