Burns H
Air Force Human Resources Laboratory, Brooks Air Force Base, Texas 78235-5000.
Aviat Space Environ Med. 1988 Nov;59(11 Pt 2):A69-75.
In knowledge-based educational systems, the key concept is that information and procedures are represented in the same data structure. These structures can search for each other in flexible and, consequently, very robust ways. At the Air Force Human Resources Laboratory (AFHRL), our researchers are building computer environments that know what they know, know how people can best use them, and know how to draw inferences about their state--self-referential electronic tutors. In September 1986, artificial intelligence researchers participated in AFHRL's Research Planning Forum for Intelligent Tutorial Systems (ITS). This essay reviews the state of the philosophy, art, and science of artificial intelligence (AI) approaches to education. Then it summarizes the research issues which were presented, discussed, and better defined in this Forum--namely the nature and representation of 1) expertise modules, 2) student diagnostic modules, 3) adaptive instructional and curriculum modules, 4) instructional environments, and 5) man-machine interfaces. Advances in artificial intelligence, cognitive science, and instructional discourse have provided a means for investigating human learning, for representing an individual's own "knowledge processing." Research and development in knowledge-based educational systems seems promising, not only for helping people learn how to perform complex tasks, but also for explicitly expressing how people learn to learn. Therefore, would it not be wise to establish a scientific legacy for the development of effective knowledge-based tutorial systems which is informed by the best studies of mind and meaning, language and thought, purpose and paradox?
在基于知识的教育系统中，关键概念是信息和程序以相同的数据结构表示。这些结构能够以灵活且因而非常稳健的方式相互搜索。在空军人力资源实验室（AFHRL），我们的研究人员正在构建计算机环境，这些环境知道自己所拥有的知识，知道人们如何能最佳地利用它们，并且知道如何对自身状态进行推理——即自我参照的电子辅导系统。1986年9月，人工智能研究人员参加了AFHRL的智能辅导系统（ITS）研究规划论坛。本文回顾了人工智能（AI）在教育领域应用的哲学、艺术和科学现状。然后总结了在该论坛上提出、讨论并得到更清晰界定的研究问题——即1）专业知识模块、2）学生诊断模块、3）适应性教学和课程模块、4）教学环境以及5）人机界面的性质和表示。人工智能、认知科学和教学话语方面的进展为研究人类学习、表示个体自身的“知识处理”提供了一种手段。基于知识的教育系统的研发似乎很有前景，不仅有助于人们学习如何执行复杂任务，还能明确表达人们如何学会学习。因此，以关于心智与意义、语言与思维、目的与悖论的最佳研究为依据，为开发有效的基于知识的辅导系统建立科学遗产，难道不是明智之举吗？
