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

这个SBIR一期项目使用人工智能技术来识别科学课程本科生解释问题、描述程序、提出主张、提供证据、提供资格和得出结论的方式。强调科学推理的形式，这种人工智能的新应用，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报告之前改进，从而使教师腾出宝贵的教学时间，让学生为实践科学家的角色做好准备。随着美国继续经历快速的多样性增长，这种通过创新地使用技术来帮助学生的重点，有可能通过自主写作和修改来培养学生的能力，从而扩大科学教育。由于人工智能技术旨在扩展能力，因此正在使用的技术将在网络上全天候可用，这将对我们的技术和科学劳动力的增长产生直接影响，从而扩大了许多通往STEM职业的动态途径。正如美国国家科学基金会在2015年的报告《重新审视STEM劳动力》中所指出的那样，这些工作范围广泛，对创新和竞争力至关重要，对于实现个人和国家繁荣和竞争力的相互促进的目标至关重要。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的应用在从识别语言使用模式到捕获科学推理模式的飞跃中具有很高的风险。人工智能应用程序在由教师和学生使用单一标题评分和注释的100,000个实验室报告的专有语料库上进行训练，将识别学生实验室报告中科学推理的逻辑结构。一旦系统地识别，根据能力水平分类，并由STEM教师验证，数字教学将用于帮助学生提高他们的科学推理过程。通过异步机器学习构建学生成功的单一目标，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.