Intelligent links: AI‐supported connections between employers and colleges
Intelligent links: AI‐supported connections between employers and colleges
复制标题
智能链接：人工智能支持雇主和大学之间的连接
DOI：
10.1002/aaai.12040
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发表时间：
2022
期刊：
影响因子：
0.9
通讯作者：
Ray, Fritz
中科院分区：
文献类型：
--
作者：
Robson, Robby;Kelsey, Elaine;Goel, Ashok;Nasir, Sazzad M.;Robson, Elliot;Garn, Myk;Lisle, Matt;Kitchens, Jeanne;Rugaber, Spencer;Ray, Fritz
当现代化和其他变化要求劳动力重新技能时，雇主往往会转向当地大学进行培训。这样做可能是一个令人沮丧的经验。人力资源和人才专业人员很难识别和沟通需求，特别是对于新的工作和角色，而大学继续教育（CE）和专业发展办公室很难理解和响应公司的需求。本文描述了一个名为SkillSync™的NSF融合加速器项目，其中使用多种形式的人工智能来解决这一特定问题，并为国家努力（例如，美国商会人才管道管理计划）提供技能数据和技能调整服务。Skillsync使用Siamese Multi-depth Transformer-based Hierarchical Encoder（SMITH）和其他自然语言理解方法的变体，将职位描述和课程信息映射到技能分类，使用机器学习模型将技能需求与学习成果和培训相匹配，并结合基于格鲁吉亚理工学院的Jill沃森“虚拟教学助理”的智能教练来回答有关Skillsync词汇的问题，功能和过程。本文描述了这些AI方法，这些方法如何在Skillsync中使用，以及所涉及的挑战。
When modernization and other changes demand workforce reskilling, employers often turn to local colleges for training programs. Doing so can be a frustrating experience. HR and talent professionals have difficulty identifying and communicating requirements, especially for new jobs and roles, while college continuing education (CE) and professional development offices have difficulty understanding and responding to company needs. This article describes an NSF Convergence Accelerator project called SkillSync™ in which multiple forms of AI are used to address this specific problem and provide national efforts (eg, the US Chamber of Commerce Talent Pipeline Management initiative) with skills data and skills alignment services. Skillsync uses variations on the Siamese Multi-depth Transformer-based Hierarchical Encoder (SMITH) and other natural language understanding methods to map job descriptions and course information to skills taxonomies, uses machine-learned models to align skills needs with learning outcomes and training, and incorporates an intelligent coach based on Georgia Tech's Jill Watson “virtual teaching assistant” to answer questions about Skillsync's vocabulary, functionality, and process. This article describes these AI methods, how these methods are used in Skillsync, and the challenges involved.