为STEM学习者提供人工智能准备教育的平衡教学方法:通过传统课堂和自主在线环境中的主动学习,灌输人工智能能力的平衡观点

A. Fong, Ajay K. Gupta, Steve M. Carr, Shameek Bhattacharjee, Michael A. Harnar
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引用次数: 0

摘要

人工智能(AI)越来越多地应用于计算机科学(CS)以外的学科。工程师、统计学家、商业分析师、生物学家、物理学家、医生和药剂师等许多非计算机科学专业人士利用人工智能算法和系统的力量来解决他们的领域特定问题。尽管人们发现人工智能在解决许多以前无法解决的问题方面很有用,但与当代人工智能相关的重要限制仍然存在。因此,让当前和未来的人工智能用户了解当前形式的人工智能的优势和劣势,以及人工智能在可预见的未来会是什么样子,这一点非常重要。在本文中,作者描述了一种从一系列STEM学科教育人工智能用户的教学方法,以便他们能够最好地利用人工智能所提供的东西。具体来说,采用一种平衡的方法来确保学习者获得人工智能可以或不能为他们做的知识和技能。越来越多的体验式学习模块补充了现有的教育资源,作为一种工具,让STEM学习者为未来大量使用人工智能技术的工作场所做好准备。这些学习模块促进主动学习,可以应用于传统的课堂环境,自主在线学习,或两种模式的混合。论文最后介绍了在跨多个定量学科的混合模式设置中实际使用体验式学习模块的令人鼓舞的结果。所有的项目工件,包括开发的经验学习模块、推荐的用途和最佳实践,都可以在项目网站上免费获得。欢迎感兴趣的教育工作者和研究人员使用现有资源和/或为正在进行的研究做出贡献。
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A Balanced Pedagogical Approach toward AI Readiness Education for STEM Learners: Instilling a balanced view of AI capabilities through active learning in both traditional classroom and self-directed online environments
Artificial intelligence (AI) is increasingly being applied to disciplines beyond computer science (CS). Engineers, statisticians, business analysts, biologists, physicists, physicians, and pharmacists, are among the many non-CS professionals who leverage the power of AI algorithms and systems for solving their domain-specific problems. Although AI has been found useful for solving a wide range of previously unsolvable problems, there are important limitations associated with contemporary AI. It is therefore important to inform current and future AI users regarding both strengths and weaknesses of AI in its current form, as well as what AI will be like in the foreseeable future. In this paper, the authors describe a pedagogical approach toward educating AI users from a range of STEM disciplines so that they can best exploit what AI has to offer. Specifically, a balanced approach is taken to ensure that learners gain knowledge and skills in what AI can or cannot do for them. A growing suite of experiential learning modules, which complement existing educational resources, serve as a vehicle for getting STEM learners ready for a future workplace characterized by significant use of AI technologies. These learning modules promote active learning and can be applied in a traditional classroom setting, self-directed online study, or a mix of the two modes. The paper ends with a presentation of encouraging results of actual use of the experiential learning modules in a mixed mode setting across multiple quantitative disciplines. All project artifacts, including the developed experiential learning modules, recommended uses, and best practices, are freely available on the project website. Interested educators and researchers are welcome to use the available resources and/or contribute to the on-going research.
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