I-Corps: A flexible learning and assessment tool based on a skill mastery approach

这个I-Corps项目的更广泛的影响/商业潜力将通过提高大学培养具有全球竞争力的STEM（科学，技术，工程和数学）劳动力的能力来服务于国家利益。该项目的核心技术为学生提供了在个性化环境中练习问题的机会，直到掌握为止。 该技术还使教师可以轻松地将频繁和第二次机会测试结合起来，同时扩展以支持更多的学生。这些教学策略已被证明可以降低STEM课程的失败率，并且可以不成比例地使妇女，少数民族，第一代学生和社会经济地位低下的学生受益。帮助这些学生可以提高保留的努力，一般来说，并为不同的学生，特别是。该项目的商业潜力包括在快速增长的大学教育技术市场中占据相当大的份额，该市场具有全球以及非大学教育领域的潜在市场。该I-Corps项目旨在开发一个软件即服务（SaaS）在线平台，用于为学生创建和提供学习体验和评估，重点是大学级别的STEM（科学，技术，工程和数学）课程。该平台允许教师轻松地将联合收割机自适应、基于规则和人工智能（AI）算法相结合，以生成和评分活动和评估的不同随机版本。它包括针对复杂问题类型的新颖AI自动评分算法，包括符号数学，交互式学生绘图，实时编码和自然语言输入。核心算法在三个关键方面推进了最先进的技术：（1）使用人工智能自动评分可以完全消除大学课程中使用人类评分员，大大降低人员配备水平和成本，并能够扩展到更大和更多样化的学生群体;（2）该平台通过一套新颖的算法阻止作弊;以及（3）该平台使用复杂的评分算法，激励掌握学习，并导致学习成果的实质性改善。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。

The broader impact/commercial potential of this I-Corps project will serve the national interest by improving the ability of universities to train a globally competitive STEM (Science, Technology, Engineering, and Mathematics) workforce. The core technology in this project provides students with the opportunity to practice questions in a personalized environment until mastery is achieved. The technology also makes it easy for instructors to incorporate frequent and second chance testing while scaling to support more students. These pedagogical strategies have been shown to reduce the failure rates in STEM classes and could disproportionately benefit women, minorities, first-generation students, and students of low socio-economic status. Helping these students can improve retention efforts, in general, and for diverse students, in particular. The commercial potential of this project includes capturing a significant fraction of the rapidly-growing university education technology market with potential markets worldwide as well as in non-university education sectors.This I-Corps project aims to develop a Software as a Service (SaaS) online platform for creating and delivering learning experiences and assessments for students, with a focus on university-level STEM (Science, Technology, Engineering, and Mathematics) courses. The platform allows instructors to easily combine adaptive, rule-based and artificial intelligence (AI) algorithms to generate and grade different randomized versions of activities and assessments. It includes novel AI auto-grading algorithms for complex question types including symbolic mathematics, interactive student drawing, live coding, and natural language input. The core algorithms advance the state of the art in three key ways: (1) The use of AI auto-grading can entirely eliminate the use of human graders in university courses, dramatically reducing staffing levels and costs, and enabling expansion to larger and more diverse student populations; (2) The platform deters cheating with a set of novel algorithms; and (3) The platform uses sophisticated grading algorithms that incentivize mastery learning and that lead to substantial improvements in learning outcomes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.