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The progress of Large Language Models (LLMs) like ChatGPT raises the question of how they can be integrated into education. One hope is that they can support mathematics learning, including word-problem solving. Since LLMs can handle…

计算与语言 · 计算机科学 2025-08-12 Anselm R. Strohmaier , Wim Van Dooren , Kathrin Seßler , Brian Greer , Lieven Verschaffel

This paper was a Workshop Paper. See the full paper which will be presented at CHI 2026: arXiv:2501.10551; As large language models (LLMs) become more powerful and ubiquitous, systems like ChatGPT are increasingly used by students to help…

人机交互 · 计算机科学 2026-02-13 Andrew Jelson , Sang Won Lee

Large Language Models (LLMs) have become part of how students solve programming tasks, offering immediate explanations and even full solutions. Previous work has highlighted that novice programmers often heavily rely on LLMs, thereby…

计算机与社会 · 计算机科学 2026-04-15 Iris Groher , Patrick Heissenberger , Michael Vierhauser

Over the last few years, large language models (LLMs) have emerged as the most important breakthroughs in natural language processing (NLP) that fundamentally transform research and developments in the field. ChatGPT represents one of the…

The paper aims to fulfil three main functions: (1) to serve as an introduction for the physics education community to the functioning of Large Language Models (LLMs), (2) to present a series of illustrative examples demonstrating how…

物理教育 · 物理学 2024-01-31 Giulia Polverini , Bor Gregorcic

Advancements in Large Language Models (LLMs), such as ChatGPT, offer significant opportunities to enhance instructional support in introductory programming courses. While extensive research has explored the effectiveness of LLMs in…

人机交互 · 计算机科学 2025-05-09 Muntasir Hoq , Jessica Vandenberg , Shuyin Jiao , Seung Lee , Bradford Mott , Narges Norouzi , James Lester , Bita Akram

Large Language Models (LLMs) represent a leap in artificial intelligence, excelling in tasks using human language(s). Although the main focus of general-purpose LLMs is not code generation, they have shown promising results in the domain.…

软件工程 · 计算机科学 2024-01-30 Sanka Rasnayaka , Guanlin Wang , Ridwan Shariffdeen , Ganesh Neelakanta Iyer

This paper presents a comprehensive survey of ChatGPT-related (GPT-3.5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains. Indeed, key innovations…

The integration of large language models (LLMs) into computing education offers many potential benefits to student learning, and several novel pedagogical approaches have been reported in the literature. However LLMs also present…

计算机与社会 · 计算机科学 2024-12-13 Shuying Qiao , Paul Denny , Nasser Giacaman

Large Language Models (LLMs) have gained significant attention in the software engineering community. Nowadays developers have the possibility to exploit these models through industrial-grade tools providing a handy interface toward LLMs,…

Large Language Models (LLMs) like GPT and Bard are capable of producing code based on textual descriptions, with remarkable efficacy. Such technology will have profound implications for computing education, raising concerns about cheating,…

新兴技术 · 计算机科学 2024-04-29 Bruno Pereira Cipriano , Pedro Alves

This paper describes the systems submitted by team6 for ChatEval, the DSTC 11 Track 4 competition. We present three different approaches to predicting turn-level qualities of chatbot responses based on large language models (LLMs). We…

计算与语言 · 计算机科学 2023-08-15 Ondřej Plátek , Vojtěch Hudeček , Patricia Schmidtová , Mateusz Lango , Ondřej Dušek

Large Language Models (LLMs) have revo lutionized natural language processing Natural Language Processing (NLP), with Chat Generative Pre-trained Transformer (ChatGPT) standing out as a notable exampledue to its advanced capabilities and…

计算与语言 · 计算机科学 2025-03-25 Azim Akhtarshenas , Afshin Dini , Navid Ayoobi

Prior research shows that how students engage with Large Language Models (LLMs) influences their problem-solving and understanding, reinforcing the need to support productive LLM-uses that promote learning. This study evaluates the impact…

计算机与社会 · 计算机科学 2025-08-25 Jerome Brender , Laila El-Hamamsy , Kim Uittenhove , Francesco Mondada , Engin Bumbacher

The rapid advancement of Artificial Intelligence has resulted in the advent of Large Language Models (LLMs) with the capacity to produce text that closely resembles human communication. These models have been seamlessly integrated into…

机器学习 · 计算机科学 2025-02-04 Zenon Lamprou , Yashar Moshfeghi

Chatbots' role in fostering self-reflection is now widely recognized, especially in inducing users' behavior change. While the benefits of 24/7 availability, scalability, and consistent responses have been demonstrated in contexts such as…

人机交互 · 计算机科学 2024-05-27 Riku Arakawa , Hiromu Yakura

Along with the development of large language models (LLMs), e.g., ChatGPT, many existing approaches and tools for software security are changing. It is, therefore, essential to understand how security-aware these models are and how these…

计算机与社会 · 计算机科学 2023-09-20 Jingyue Li , Per Håkon Meland , Jakob Svennevik Notland , André Storhaug , Jostein Hjortland Tysse

In a rapidly evolving digital landscape autonomous tools and robots are becoming commonplace. Recognizing the significance of this development, this paper explores the integration of Large Language Models (LLMs) like Generative pre-trained…

人机交互 · 计算机科学 2024-03-22 Younes Lakhnati , Max Pascher , Jens Gerken

The use of large language model (LLM)-powered chatbots, such as ChatGPT, has become popular across various domains, supporting a range of tasks and processes. However, due to the intrinsic complexity of LLMs, effective prompting is more…

Large Language Models (LLMs) have the potential to fundamentally change the way people engage in computer programming. Agent-based modeling (ABM) has become ubiquitous in natural and social sciences and education, yet no prior studies have…

人机交互 · 计算机科学 2024-02-02 John Chen , Xi Lu , Michael Rejtig , David Du , Ruth Bagley , Michael S. Horn , Uri J. Wilensky