实现教育领域的视觉问答：作为多模态AI的GPT-4V
Realizing Visual Question Answering for Education: GPT-4V as a Multimodal AI
AI总结：
该立场论文提出GPT-4V可推动教育领域视觉问答（VQA）的落地，梳理VQA技术发展与教育领域图像分析应用，演示GPT-4V的教育研究用法并探讨未来启示，为教育研究方法提供新参考。
摘要：
教育学者已对从教学场景中获取的各类图像数据展开分析，例如展现课堂动态的照片、学生围绕学习内容绘制的图画、教材插图等。毋庸置疑，大多数针对图像数据的定性分析与阐释均由人类研究人员完成，尚未实现基于机器的自动化。部分原因在于，多数图像处理人工智能模型要么普通教育学者难以获取使用权限，要么因其复杂的深度神经网络架构而缺乏可解释性。\n然而，近期视觉问答（Visual Question Answering，VQA）技术的发展催生了实用的视觉语言模型，这类模型接收用户以自然语言提出的、关于给定图像的问题，并以自然语言返回答案。尤其是OpenAI发布的GPT-4V，极大地开放了最先进的视觉语言模型服务，使得VQA可被用于多种用途。不过，VQA和GPT-4V尚未被大量应用于教育研究中。\n在这篇立场论文中，我们提出GPT-4V有助于实现教育领域的VQA。我们所说的“实现”VQA包含两层含义：（1）GPT-4V让所有教育学者都能使用VQA技术，不存在技术或获取门槛；（2）GPT-4V让教育学者认识到VQA对教育研究的实用价值。基于此，本文旨在介绍面向教育研究的VQA，为教育研究方法提供一个里程碑。本文第二章回顾了以GPT-4V发布为重要节点的VQA技术发展历程；第三章梳理了图像分析在教育研究中的应用场景；第四章演示了GPT-4V如何应用于第三章提及的各类研究用途，并提供了操作提示词；最后，第五章探讨了未来的研究启示。
英文摘要：
Educational scholars have analyzed various image data acquired from teaching and learning situations, such as photos that shows classroom dynamics, students' drawings with regard to the learning content, textbook illustrations, etc. Unquestioningly, most qualitative analysis of and explanation on image data have been conducted by human researchers, without machine-based automation. It was partially because most image processing artificial intelligence models were not accessible to general educational scholars or explainable due to their complex deep neural network architecture. However, the recent development of Visual Question Answering (VQA) techniques is accomplishing usable visual language models, which receive from the user a question about the given image and returns an answer, both in natural language. Particularly, GPT-4V released by OpenAI, has wide opened the state-of-the-art visual langauge model service so that VQA could be used for a variety of purposes. However, VQA and GPT-4V have not yet been applied to educational studies much. In this position paper, we suggest that GPT-4V contributes to realizing VQA for education. By 'realizing' VQA, we denote two meanings: (1) GPT-4V realizes the utilization of VQA techniques by any educational scholars without technical/accessibility barrier, and (2) GPT-4V makes educational scholars realize the usefulness of VQA to educational research. Given these, this paper aims to introduce VQA for educational studies so that it provides a milestone for educational research methodology. In this paper, chapter II reviews the development of VQA techniques, which primes with the release of GPT-4V. Chapter III reviews the use of image analysis in educational studies. Chapter IV demonstrates how GPT-4V can be used for each research usage reviewed in Chapter III, with operating prompts provided. Finally, chapter V discusses the future implications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
