SBIR Phase I: An online learning and assessment platform for sophisticated and secure exams

这个小型企业创新研究(SBIR)第一阶段项目的更广泛/更广泛的商业影响是为更广泛的STEM(科学、技术、工程和数学)教育工作者提供一个强大而复杂的评估工具，以改善学生的学习，提高教学效率，并减少作弊事件。这项技术的核心技术是一个在线平台，用于创建和提供高质量的评估，这些评估由人工智能(AI)算法自动评分，为学生提供即时反馈。这项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。自动评分功能减少了评分工作，使教师能够专注于课程设计，纳入更频繁和第二次机会的测试，并有更多时间直接帮助学生。该平台可以自动为每个学生生成个性化评估并评分，这有助于将作弊降至最低，并使学生能够重复练习。这种学习体验适合帮助少数民族、第一代大学生和社会经济地位较低的学生，他们传统上较少获得最高素质的人类导师。提高STEM教育的效率将有助于创造和持续支持受过高等教育的STEM劳动力，这对相关领域的国家竞争力非常重要。这个第一阶段项目旨在开发一个无代码、图形创作环境，允许没有先前编程经验的教师创建基于AI的自动评分内容。教师将能够通过将该项目现有的核心人工智能技术与以下创新相结合来创建复杂的自动评分评估：a)使用基于块的语言和数据流可视化的无代码图形创作；(2)通过使用验证算法指定和检查对学生答案的约束，针对结构化数据的新的AI自动评分器；以及(3)使用新的AI自动评分器的图形界面，用于结构化数据，包括相关的数据流可视化。所有这三项新功能都将通过以用户为中心的研究对一小群来自不同背景和编程技能水平的教师进行评估，从新手到专家不等。这些半结构化的定性研究将遵循扎根的理论方法，针对每个目标的具体指标。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to provide a robust and sophisticated assessment tool to a wider range of STEM (Science, Technology, Engineering and Mathematics) educators to improve student learning, make teaching more efficient, and reduce the incidences of cheating. The core technology of this technology is an online platform for creating and delivering high-quality assessments that are auto-graded by artificial intelligence (AI) algorithms, providing immediate feedback to students. The technology provides students with the opportunity to practice questions in a personalized environment until mastery is achieved. The auto-grading features reduce grading effort, allowing instructors to focus on course design, incorporate more frequent and second-chance testing, and have more time to directly help students. The platform can automatically generate and grade personalized assessments for each student, which helps to minimize cheating and enables repeated practice by students. This learning experience is suited to help minorities, first-generation college students, and students of low socioeconomic status, who have traditionally had less access to the highest quality human instructors. Making STEM education more effective will facilitate the creation and continuing support of a highly educated STEM workforce and is important for national competitiveness in related fields.This Phase I project aims to develop a no-code, graphical authoring environment that will allow instructors without prior programming experience to create AI-based auto-graded content. Instructors will be enabled to create sophisticated, auto-graded assessments by combining the existing core AI technology of this project with the following innovations: a) a no-code, graphical authoring using block-based language and data-flow visualizations; (2) new AI auto-graders for structured data, such as student data analyses within spreadsheets, by using verification algorithms to specify and check constraints on student answers; and (3) a graphical interface to use the new AI auto-graders for structured data, including associated data-flow visualizations. All three of these new capabilities will be evaluated via user-focused studies with a small group of instructors from a variety of backgrounds and programming skill levels, ranging from novice to expert. These semi-structured qualitative studies will follow a grounded theory approach, addressing metrics specific to each objective.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.