SBIR Phase I: Artificial Intelligence, Scientific Reasoning, and Formative Feedback: Structuring Success for STEM Students

这个SBIR第一阶段项目使用人工智能技术来识别本科生在科学课程中解释问题、描述程序、提出主张、提供证据、提供资格和得出结论的方式。由于强调科学推理的形式，这种新的人工智能使用来识别与科学推理相关的语言模式，将允许学生在提交书面实验室报告之前改进他们的书面实验报告，从而解放教师将宝贵的教学时间投入到为学生准备实践科学家的角色上。随着美国继续经历快速的多样性增长，这种通过创新使用技术来帮助学生的重点，通过通过自主写作和修改培养学生的能力，具有扩大科学教育的潜力。由于人工智能技术旨在扩展能力，正在使用的技术全天候在网络上提供，将对增加我们的技术和科学劳动力产生直接影响，从而扩大许多通往STEM职业的动态途径。正如国家科学基金会在其2015年的报告《重温STEM劳动力》中指出的那样，这些工作是广泛的，对创新和竞争力至关重要，对于个人和国家繁荣和竞争力的相辅相成的目标至关重要。因此，对这种技术的投资就是对国家竞争力、教育政策、创新和劳动力多样性的投资。NSF SBIR支持将用于设计和推出基于深度人工神经网络(DANN)的人工智能技术，该技术由自然语言处理(NLP)、潜在语义分析(LSA)和人工智能算法的最新进展驱动。由于NLP和LSA技术目前仅用于识别语法和组织模式，因此DANN的应用在实现从识别语言使用模式到捕获科学推理模式的飞跃方面具有很高的风险。人工智能应用程序在一个由10万份实验报告组成的专有语料库上进行培训，教师和学生使用单一的题目对报告进行评分和注释，人工智能应用程序将识别学生实验报告中科学推理的逻辑结构。一旦被有条不紊地确定，根据能力水平进行分类，并得到STEM教师的验证，数字化教学将被用来帮助学生提高他们的科学推理过程。这项创新的唯一目标是通过异步机器学习构建学生的成功结构，这一创新有望在与STEM教育相关的国家竞争力、教育政策、创新和多样性的讨论中发挥有意义的作用。

This SBIR Phase I project uses artificial intelligence techniques to identify the ways that undergraduate students in scientific courses explicate problems, describe procedures, make claims, provide evidence, offer qualifications, and draw conclusions. With emphasis on forms of scientific reasoning, this new use of artificial intelligence, to identify language patterns associated with scientific reasoning, will allow students to improve their written laboratory reports before they are submitted, therefore freeing instructors to devote precious instructional time to preparing students for roles as practicing scientists. As the U.S. continues to experience rapid diversity growth, this focus on helping students through innovative uses of technology holds the potential to expand science education by cultivating student ability through autonomous writing and revision. Because artificial intelligence techniques are intended to expand capabilities, the techniques being used, available 24/4 on the web, will have the direct impact of growing our technical and scientific workforce, thus expanding the many dynamic pathways to STEM occupations. As the NSF observed in 2015 in its report Revisiting the STEM Workforce, these jobs are extensive and critical to innovation and competitiveness and are essential to the mutually reinforcing goals of individual and national prosperity and competitiveness. An investment in such a technology is thus an investment in national competitiveness, education policy, innovation, and workforce diversity.NSF SBIR support will be used to design and launch artificial intelligence techniques based on Deep Artificial Neural Network (DANN) as driven by Natural Language Processing (NLP), Latent Semantic Analysis (LSA), and the latest advances in AI algorithms. Because NLP and LSA techniques are presently used solely to identify grammatical and organizational patterns, the application of DANN is high risk in making a leap from identifying patterns of language use to capturing patterns of scientific reasoning. Trained on a proprietary corpus of 100,000 lab reports scored and annotated by instructors and students using a single rubric, the AI application will identify logic structures of scientific reasoning in student laboratory reports. Once methodically identified, categorized according to ability level, and validated by STEM instructors, digital instruction will be used to help students improve their scientific reasoning processes. With the singular goal of structuring student success through asynchronous machine learning, this innovation holds the promise to figure meaningfully in discussions of national competitiveness, education policy, innovation, and diversity as related to STEM education.