这篇观点论文提出了一系列利用人工智能（AI）强化课堂教学的交互场景，例如对话自动补全、知识与风格迁移以及AI生成内容评估。借助大语言模型（Large Language Models, LLMs）的最新进展，我们探究了AI增强并丰富师生对话、提升教学质量的潜力。我们的目标是生成师生间新颖且有意义的对话，制定评估标准，并提高AI赋能教育项目的成效。在第3节中，我们讨论了利用现有LLMs有效完成教育相关任务所面临的挑战，并提出了一个统一框架，用于处理多样化教育数据集、处理长对话以及压缩信息，以更好地完成更多下游任务。在第4节中，我们梳理了核心任务，包括师生对话自动补全、专家教学知识与风格迁移以及AI生成内容（AI-Generated Content, AIGC）评估，为未来研究指明了清晰路径。在第5节中，我们还探讨了借助外部可调节LLMs，通过人在回路监督和强化学习来优化生成内容的方案。最终，本文旨在凸显AI助力教育领域的潜力，并推动对该方向的进一步探索。
英文摘要：
This perspective paper proposes a series of interactive scenarios that utilize Artificial Intelligence (AI) to enhance classroom teaching, such as dialogue auto-completion, knowledge and style transfer, and assessment of AI-generated content. By leveraging recent developments in Large Language Models (LLMs), we explore the potential of AI to augment and enrich teacher-student dialogues and improve the quality of teaching. Our goal is to produce innovative and meaningful conversations between teachers and students, create standards for evaluation, and improve the efficacy of AI-for-Education initiatives. In Section 3, we discuss the challenges of utilizing existing LLMs to effectively complete the educated tasks and present a unified framework for addressing diverse education dataset, processing lengthy conversations, and condensing information to better accomplish more downstream tasks. In Section 4, we summarize the pivoting tasks including Teacher-Student Dialogue Auto-Completion, Expert Teaching Knowledge and Style Transfer, and Assessment of AI-Generated Content (AIGC), providing a clear path for future research. In Section 5, we also explore the use of external and adjustable LLMs to improve the generated content through human-in-the-loop supervision and reinforcement learning. Ultimately, this paper seeks to highlight the potential for AI to aid the field of education and promote its further exploration.
Centre for Language Technology (CST)（语言技术中心（CST））；University of Copenhagen / Københavns Universitet（哥本哈根大学）
In this article, we show and discuss our experience in applying the flipped classroom method for teaching Conditional Random Fields in a Natural Language Processing course. We present the activities that we developed together with their relationship to a cognitive complexity model (Bloom's taxonomy). After this, we provide our own reflections and expectations of the model itself. Based on the evaluation got from students, it seems that students learn about the topic and also that the method is rewarding for some students. Additionally, we discuss some shortcomings and we propose possible solut…
Artificial intelligence (AI) algorithms have emerged in the educational domain as a tool to make learning more efficient. Different applications for mastering particular skills, learning new languages, and tracking their progress are used by children. What is the impact on children from using this smart technology? We conducted a systematic review to understand the state of the art. We explored the literature in several sub-disciplines: wearables, child psychology, AI and education, school surveillance, and accountability. Our review identified the need for more research for each established t…
Slide presentations are an effective and efficient tool used by the teaching community for classroom communication. However, this teaching model can be challenging for blind and visually impaired (VI) students. The VI student required personal human assistance for understand the presented slide. This shortcoming motivates us to design a Classroom Slide Narration System (CSNS) that generates audio descriptions corresponding to the slide content. This problem poses as an image-to-markup language generation task. The initial step is to extract logical regions such as title, text, equation, figure…
Technische Universität Ilmenau（伊尔默瑙工业大学）；Friedrich-Schiller-Universität Jena（耶拿弗里德里希·席勒大学）
Sequence-to-sequence models based on LSTM and GRU are a most popular choice for forecasting time series data reaching state-of-the-art performance. Training such models can be delicate though. The two most common training strategies within this context are teacher forcing (TF) and free running (FR). TF can be used to help the model to converge faster but may provoke an exposure bias issue due to a discrepancy between training and inference phase. FR helps to avoid this but does not necessarily lead to better results, since it tends to make the training slow and unstable instead. Scheduled samp…
Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools
Abilene Christian University（阿比林基督教大学）；Aalto University（阿尔托大学）；Nuremberg Tech（纽伦堡工业大学）；Google（谷歌公司）；UC San Diego（加州大学圣迭戈分校）；Temple University（天普大学）；University of California Berkeley（加州大学伯克利分校）；FernUniversität in Hagen（哈根函授大学）；Virginia Tech（弗吉尼亚理工大学）
TAL Education Group（好未来教育集团）；Tsinghua University（清华大学）；Michigan State University（密歇根州立大学）
Asking questions is one of the most crucial pedagogical techniques used by teachers in class. It not only offers open-ended discussions between teachers and students to exchange ideas but also provokes deeper student thought and critical analysis. Providing teachers with such pedagogical feedback will remarkably help teachers improve their overall teaching quality over time in classrooms. Therefore, in this work, we build an end-to-end neural framework that automatically detects questions from teachers' audio recordings. Compared with traditional methods, our approach not only avoids cumbersom…
