在AI交互媒介的突破浪潮中,人才新质生产力展现出前所未有的“能”与“效”,引领着时代的变革与进步。
扶摇职上教育科技CEO、纽约大学客座教授钱天然在2024 HRoot人力资本论坛·北京站发表的精彩演讲,欢迎扫码观看~
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)
以下为钱天然的部分精彩观点:
AI的风,似乎从去年开始吹得格外猛烈,我们当然要透过现象看到其背后多年的数据积累,事实上积累已经超过5年的时间了。今天主要和大家分享一下我们以往的优秀案例,以及在实操过程中,客户给我们带来的一些反馈。
2019年在疫情爆发之前,我们发现在中国存在大量的人才错配的情况,于是创立了这家公司希望能够运用更多的数据、更科学的方法帮助企业和员工实现人岗匹配。我们“Mapping the Right”的价值主张就是这样应运而生的。
或许今天我们谈人机交互AI,更多谈到的是AI数字人或是AI语义分析,今天我为大家补充一个领域——AI行为数据分析,我们不仅要看一个人才是怎么说的,更要看他是怎么做的,为了能尽快展开观察,我们首先要设置一个实验场。
为了找到最好的实验工具,我们融入了“心流理论”与“游戏化”理念,从严肃游戏(serious game)的角度出发,形成了基于游戏的测评工具,游戏化测评工具想通过游戏的方式去创造实验场并在游戏过程中挖掘参与人的行为数据,同时结合心理学范式作出对应的算法联系,最终勾画精准的人才画像,实现“Mapping the Right”的目的。我们设计的第一款中国交互式AI游戏叫做Up Up Farm,一经上线就受到广泛的关注,尤其是一些大公司的HR们,他们都非常感兴趣一款解密游戏是如何将一个人测算出来的。
Up Up Farm在设计理念上通过受测者全情投入游戏情境之中,不知不觉收集心理学范式数据。例如游戏中的一个关卡,会有鱼篓让玩家钓鱼并卖钱然后会获得相应的金币分数,然而最后测试的结果和这个金币分数是完全没有关系的,我们收集并分析的是玩家的行为和过程的数据,而不是他最后的得分。比如说在钓鱼的过程中,一些鱼篓被设置成会突然破裂的,不同的玩家会表现出截然不同的反应,有些会按耐不住急躁,有些会耐心等待,这才是我们抓数据的好时机。这就是游戏中的心理学的实验——SORT,我们观察和手机的受测者对于刺激的反馈。
游戏完整时长为30分钟,结束之后会生成36页的报告,涵盖受测者的能力、特质与潜力,包括抗压性、风险偏好、学习能力、解决能力等24个维度。以往生成这样一个报告通常需要接受一个小时或者更长的问卷测评,而如今只需要30分钟,效率大大提高。而且从精准度来说,AI行为数据不再只是一个过场,而是可以真正帮助我们筛选到人才的实用工具。
单人游戏的成功上线进一步激发了我们团队的热情,随后在群体测评领域也取得了突破,在这个过程中我们真真切切地观察到游戏化的形式的确可以触发人更多的行为展现,而在AI时代来临的时候,这样的行为更容易被捕捉。在企业出海的大背景下,我们前前后后已经完成1, 000多场交付,帮助包括字节、腾讯、京东、携程在内的互联网大厂在国内外完成测试,帮他们选拔海外人才。
特别是现在,强调新质生产力的情境之下,企业想要招聘的无疑是更精准、更适配地人才。通过下面的案例,我们来看看AI如何做到更精准识人。这是一家国际领先的餐饮企业,他们希望运用AI工具提高效能,从12, 000人的简历投递中最后筛选出15个人。不难看出,这项工作的量比较大,我们通过漏斗分别设计了两个环节——择优环节与劣汰环节来帮助他们做快速mapping,这一过程中我们运用了两个AI工具,一个是刚才说的Up Up Farm游戏化测评,另外一个是群面过程中在后台快速抓取受测者行为的数字化沙盘。
项目整体成效显著,应届生作答率达到了87.1%,完成率达到了96.7%,这也就意味着大家打开这个测评绝大多数都能够认真做完,这就保证了数据的真实有效。最后我们也对HR以及候选人进行了问卷调研,整体满意度达到95%以上。
接下来,我们再看看如何用AI工具交互实现真正的精准育才。其实早在1970年,克拉格·艾伦就已经尝试用场景模拟结合游戏情境来提升教学效果,1977年前后又将行为科学引入游戏中。近些年,我们也注意到在2013年《Nature》杂志上也刊登了游戏对于管理人才的行为改变上具有积极影响的文章,引起了不小的反响。接下去我们要思考的便是如何更专业地设计环境,如何设计实验盒子。设计得越完善,收集的数据也就越精准,在日常工作中也就能更好地实现mapping的功能。
之前我们比较熟悉的培训是通过一对一或者一对多的方式进行讲授,而今这样的培训希望获得更多的突破和补充,我们希望开展参与度更高的培训,甚至在培训过程中一菜两吃,培训的同时实现观察、盘点人才的目标。自从加入游戏元素之后,我们发现参与者的热情被调动起来了,不同的行为开始展现。对于我们而言,这就是收集数据的绝佳时刻。
简简单单的快速沙盘,充分调动了大家的精力、注意力、参与度,学员全身心投入到练中学的形式中,同时这也是个快速调研过程,结合数据后台的采集、现场观察员的输入、学员的复盘等,全面地帮助我们了解团队目前存在什么问题,可以从哪些方面去提升。
经过这些大厂的实践之后,我们手上掌握了大量的人才模型和不同公司的人才卡点,比如对于中国的高管能力,我们通过几百场沙盘数据所得出一个结论,发现与先前一家海外测评公司得出的结论惊人地一致,中国领导力中最强的一项能力是关系敏锐,最弱的一项是商业判断。另外,我们与头部企业开展行为数据与AI语义进行分析研究,发现一旦语义和行为数据相叠加,能力维度相关性和验证会有极大的突破,目前我们已经在踏实尽责、自我驱动、协作共赢、敏捷更新等能力素质维度上积累了数据,相信未来还有广阔的研究前景。
我们期待着在未来新质生产力时代,可以通过AI更快地帮助企业去做出诊断、提供科学的人才全价值链服务,用算法、用数据更好地帮助企业Mapping the Right,人人皆才,人尽其才,谢谢大家。
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