We present Aptly, an extension of the MIT App Inventor platform enabling
mobile app development via natural language powered by code-generating large
language models (LLMs). Aptly complements App Inventor's block language with a
text language designed to allow visual code generation via text-based LLMs. We
detail the technical aspects of how the Aptly server integrates LLMs with a
realtime collaboration function to facilitate the automated creation and
editing of mobile apps given user instructions. The paper concludes with
insights from a study of a pilot implementation involving high school students,
which examines Aptly's practicality and user experience. The findings
underscore Aptly's potential as a tool that democratizes app development and
fosters technological creativity.

Aptly 是一种扩展了 MIT App Inventor 平台的工具，通过代码生成的大型语言模型实现自然语言驱动的移动应用开发；Aptly 通过文本语言与 App Inventor 的块语言相结合，允许通过文本模型实现可视化代码生成；论文详细介绍了 Aptly 服务器如何集成语言模型，以实时协作的方式自动创建和编辑用户指令下的移动应用；最后，通过对涉及高中学生的试点实验的研究结果，论文强调了 Aptly 作为一个工具的潜力，它民主化了应用程序开发并促进了技术创造力。

Aptly: 利用自然语言编写移动应用

Aptly: Making Mobile Apps from Natural Language

The evolution of Artificial Intelligence (AI) stands as a pivotal force
shaping our society, finding applications across diverse domains such as
education, sustainability, and safety. Leveraging AI within mobile applications
makes it easily accessible to the public, catalyzing its transformative
potential. In this paper, we present a methodology for the rapid development of
AI agent applications using the development platform provided by MIT App
Inventor. To demonstrate its efficacy, we share the development journey of
three distinct mobile applications: SynchroNet for fostering sustainable
communities; ProductiviTeams for addressing procrastination; and iHELP for
enhancing community safety. All three applications seamlessly integrate a
spectrum of generative AI features, leveraging OpenAI APIs. Furthermore, we
offer insights gleaned from overcoming challenges in integrating diverse tools
and AI functionalities, aiming to inspire young developers to join our efforts
in building practical AI agent applications.

通过使用 MIT App Inventor 提供的开发平台，本文提出了一种用于快速开发 AI 代理应用程序的方法论，并展示了三个不同的移动应用程序的开发过程：用于培育可持续社区的 SynchroNet；用于解决拖延症的 ProductiviTeams；以及用于增强社区安全的 iHELP。这三个应用程序都无缝整合了多种生成性 AI 功能，并利用了 OpenAI 的 API，同时在整合不同工具和 AI 功能方面提供了洞察，旨在鼓励年轻开发者参与我们建立实用的 AI 代理应用程序的努力。