Convergence Accelerator Phase I (RAISE): Prepare the US labor Force for Future Jobs in the Hotel and Restaurant Industry: A hybrid Framework and Multi-Stakeholder Approach

NSF融合加速器支持以团队为基础的多学科努力，以应对国家重要性的挑战，并在不久的将来展示可交付成果的潜力。 这个融合加速器第一阶段项目的更广泛影响将通过开发跨学科方法来支持人工智能（AI）和未来工作的未来研究和知识增长。该项目将展示如何整合数据挖掘和分析，定性分析，调查研究和可视化技术的进步，以预测和可视化人工智能对未来工作的影响，并提出相关的技能培训策略。该项目将提供急需的关于工作演变路径、工作任务趋势、所需技能和工具以及酒店和餐饮业工人适应能力的数据，这些数据在公共劳动数据库中的代表性明显不足。该研究小组将创建一个在线、开放访问的储存库，储存预测模型的产出、未来工作内容和所需技能的描述以及政策建议。所有调查结果和开发的培训模块将被纳入一个基于网络的专家建议系统，使任何用户都能查阅今后的工作任务说明、所需技能和定制的技能再培训模块。该项目中开发的模型也可以在未来进行评估，扩展和应用于不同的行业。建议的方法和研究成果将用于丰富酒店管理本科生和研究生课程。这个融合加速器第一阶段项目有助于从多个角度理解人工智能，工作和工人之间相互交织的关系。该项目将联合收割机深度学习、半结构化访谈、调查和工作生活日志数据分析的最新进展，构建一个混合框架，预测人工智能对人力资源行业未来工作的多维度影响。该项目还将有助于确定可能影响工人适应能力的各种社会经济因素、家庭背景和个人经历。这个项目弥合了我们对工人现状的理解与满足未来工作需求的定制再技能战略之间的差距。该项目将产生：（1）更完整地记录和分析人力资源行业受人工智能影响的工作内容的多方面演变（酒店管理），（2）更全面地了解技术，工作和劳动力之间的三角关系（社会学），（3）面向人类主体研究的异构数据挖掘方法的进展（计算机科学与工程），（4）对如何在一个由快速技术进步，工作任务演变，个人的背景和经历（人力资源管理）该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估被认为值得支持。影响审查标准。

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact of this Convergence Accelerator Phase I project will support future research and knowledge growth on Artificial Intelligence (AI) and future jobs by developing transdisciplinary methods. This project will demonstrate how to integrate advances in data mining and analytics, qualitative analysis, survey research, and visualization techniques in predicting and visualizing the impact of AI on future jobs and proposing contextual reskill training strategies. This project will provide critically needed data on the evolutionary paths of jobs, trends of job tasks, required skills and tools, and workers' adaptation capabilities in the hotel and restaurant industry, which are significantly under-represented in the public labor databases. The research team will create an online, open-access repository of the forecasting model's outputs, description of future job content and required skills, and policy recommendations. All the findings and developed training modules will be integrated into a web-based Expert Recommendation System, which allows any user to access future job task descriptions, required skills, and customized reskill training modules. The models developed in this project can also be evaluated, scaled, and applied across different industries in the future. The proposed methodology and research findings will be used to enrich undergraduate and graduate courses in hospitality management.This Convergence Accelerator Phase I project contributes to the understanding of the intertwined relationships among AI, jobs, and workers from a spectrum of angles. This project will combine the most recent advances in deep learning, semi-structured interviews, surveys, and work-life journal data analysis in building a hybrid framework to predict the multi-dimensional impact of AI on future jobs in the HR industry. This project will also contribute to identifying various social-economic factors, family backgrounds, and personal experiences that may influence workers' adaptation capabilities. This project bridges the gap between our understanding of the workers' current conditions and customized reskilling strategies for meeting the needs of future jobs. This project will result in: (1) more complete documentation and analysis of the multi-faceted evolution of job contents influenced by AI in the HR industry (hospitality management), (2) a more complete understanding of the triangular relationships among technology, jobs, and labor force (sociology), (3) advances in heterogeneous data mining methods for human subject research (computer science and engineering), (4) an enhanced understanding of how to design effective reskilling programs in a complex system consisting of rapid technological advances, job task evolution, and individuals' backgrounds and experiences (human resources management).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.