STTR Phase I: EdVision: AI-powered academic guidance for PhD programs

STTR 第一阶段：EdVision：人工智能驱动的博士课程学术指导

• 批准号: 2014338
• 负责人: Ellis Blanton
• 金额: $ 22.5万
• 依托单位: EDVISION CORP.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2014338&HistoricalAwards=false
• 关键词: STTR; Phase; EdVision; AI; powered

The broader impact of this Small Business Technology Transfer (STTR) Phase I project is to use artificial intelligence methods to help all PhD program stakeholders (students, alumni, faculty, administrators) maximize desired student placements by leveraging available courses and other resources on campus. PhD student placement is a great concern for universities. However, in the absence of data-driven tools that can help administrators track PhD student progress and market needs, there is little that university leaders or faculty can do to continually improve PhD programs and align these programs with the needs of the economy. The total addressable market for AI-driven academic guidance for higher education is estimated at over $1 billion annually. By improving the match between PhD academic preparation and the needs of organizations tackling contemporary challenges in knowledge and technology intensive industries, this project will help universities contribute to society’s grand challenges in areas such as energy, food, disease and transportation. The success of this project will demonstrate the feasibility of continuously gathering adequate data from students, alumni and job postings and using this data to make reliable predictions and actionable individualized recommendations to PhD students that support their academic preparation towards improved market readiness. Education is one of the most important applications of AI, and this project focuses on using AI to empower students, faculty and administrators to maximize the outcomes from the large investments by universities in PhD programs.This Small Business Technology Transfer (STTR) Phase I project aims to collect highly granular data from PhD students, alumni and job market postings and use this data to build prediction and recommendation models to maximize the match between each student’s interests and market needs across long time horizons beyond graduation. While the market expectations for PhD graduate competencies are evolving rapidly and include high levels of multi-disciplinary excellence, PhD programs are evolving slowly, largely due to the lack of data-driven recommendations for appropriate interventions. The proposed R&D plan will develop semi-automated methods for data curation in higher education, then use this data to build novel algorithms using neural network architectures and techniques to predict career outcomes of PhD graduates. The company will also use this data and upstream models to build individualized recommendations using model-based reinforcement learning. The system will suggest the most suitable actions for students, faculty and administrators to maximize the impacts of PhD programs in all disciplines.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.

这个小企业技术转让（STTR）第一阶段项目的更广泛的影响是使用人工智能方法来帮助所有博士课程的利益相关者（学生，校友，教师，管理人员）通过利用校园内的可用课程和其他资源来最大限度地提高学生的就业机会。博士生的安置是大学非常关注的问题。然而，由于缺乏数据驱动的工具来帮助管理人员跟踪博士生的进展和市场需求，大学领导或教师在不断改进博士课程并使这些课程与经济需求保持一致方面几乎无能为力。人工智能驱动的高等教育学术指导的潜在市场总额估计每年超过10亿美元。通过提高博士学术准备与应对知识和技术密集型行业当代挑战的组织需求之间的匹配，该项目将帮助大学为社会在能源，食品，疾病和交通等领域的重大挑战做出贡献。该项目的成功将证明持续从学生，校友和招聘信息中收集足够数据的可行性，并使用这些数据为博士生提供可靠的预测和可操作的个性化建议，以支持他们的学术准备，以提高市场准备。教育是人工智能最重要的应用之一，该项目专注于使用人工智能来帮助学生，教师和管理人员最大限度地提高大学在博士课程中的大量投资的成果。这个小企业技术转移（STTR）第一阶段项目旨在从博士生中收集高度精细的数据，校友和就业市场的帖子，并使用这些数据来建立预测和推荐模型，以最大限度地匹配每个学生的兴趣和市场需求之间的长期视野毕业后。虽然市场对博士研究生能力的期望正在迅速发展，包括高水平的多学科卓越，但博士课程发展缓慢，主要是由于缺乏数据驱动的适当干预建议。拟议的研发计划将开发高等教育数据管理的半自动化方法，然后使用这些数据来构建使用神经网络架构和技术的新型算法，以预测博士毕业生的职业成果。该公司还将利用这些数据和上游模型，使用基于模型的强化学习来构建个性化推荐。该系统将为学生、教师和管理人员提出最合适的行动建议，以最大限度地发挥所有学科博士课程的影响。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影响审查标准进行评估来支持。