Constructionism, long before it had a name, was intimately tied to the field of Artificial Intelligence. Soon after the birth of Logo at BBN, Seymour Papert set up the Logo Group as part of the MIT AI Lab. Logo was based upon Lisp, the first prominent AI programming language. Many early Logo activities involved natural language processing, robotics, artificial game players, and generating poetry, art, and music. In the 1970s researchers explored enhancements to Logo to support AI programming by children. In the 1980s the Prolog community, inspired by Logo's successes, began exploring how to adapt logic programming for use by school children. While there have been over 40 years of active AI research in creating intelligent tutoring systems, there was little AI-flavoured constructionism after the 1980s until about 2017 when suddenly a great deal of activity started. Amongst those activities were attempts to enhance Scratch, Snap!, and MIT App Inventor with new blocks for speech synthesis, speech recognition, image recognition, and the use of pre-trained deep learning models. The Snap! enhancements also include support for word embeddings, as well as blocks to enable learners to create, train, and use deep neural networks. Student and teacher project-oriented resources highlighting these new AI programming components appeared at the same time. In this paper, we review this history, providing a unique perspective on AI developments—both social and technical—from a constructionist perspective. Reflecting on these, we close with speculations about possible futures for AI and constructionism.

中文翻译：

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建构主义和人工智能：历史和可能的未来

建构主义早在它有名字之前就与人工智能领域密切相关。在 BBN 诞生 Logo 后不久，Seymour Papert 成立了 Logo Group 作为麻省理工学院人工智能实验室的一部分。Logo 基于 Lisp，这是第一个著名的 AI 编程语言。许多早期的 Logo 活动涉及自然语言处理、机器人技术、人工游戏玩家，以及产生诗歌、艺术和音乐。在 1970 年代，研究人员探索了 Logo 的增强功能，以支持儿童进行 AI 编程。1980 年代，Prolog 社区受到 Logo 成功的启发，开始探索如何调整逻辑编程以供学童使用。虽然在创建智能辅导系统方面已经有超过 40 年的活跃 AI 研究，1980 年代之后几乎没有 AI 风味的建构主义，直到 2017 年左右突然开始了大量活动。其中包括尝试通过语音合成、语音识别、图像识别和使用预先训练的深度学习模型的新模块来增强 Scratch、Snap! 和 MIT App Inventor。快拍！增强功能还包括对词嵌入的支持，以及使学习者能够创建、训练和使用深度神经网络的模块。同时出现了突出这些新的 AI 编程组件的学生和教师面向项目的资源。在本文中，我们回顾了这段历史，从建构主义的角度对人工智能的发展——社会和技术——提供了独特的视角。反思这些，