人工智能背景下专业人才培养的发展路径与方向——基于会计职业相关数据的实证研究  

Professional talent cultivation in the context of Al:an empirical study of the profession of accounting

作　　者：王奕俊[1] 杨悠然 Yijun Wang;Youran Yang

机构地区：[1]同济大学职业技术教育学院

出　　处：《中国远程教育》2020年第1期35-45,76,77,共13页Chinese Journal of Distance Education

基　　金：全国教育科学规划项目“德国职业教育治理体系应对‘工业4.0’的进程、举措、方向研究”(项目编号：BJA180104)

摘　　要：人工智能技术被普遍认为是一种通用目的技术,将对劳动力技能和劳动力市场产生广泛而深远的影响,由此引起技能市场中关于技术性失业的忧虑和争议。人工智能时代专业人才培养应何去何从?为回答这一问题,从厘清弱人工智能和强人工智能的概念出发,在吸纳经典劳动经济学理论的基础上,以Autor、Levy与Murnane创建的ALM模型为框架,以会计职业为例,利用美国职业信息教育网络(O*net online)数据与中国相关数据,分析会计职业总体技能需求的变化,结果显示在原本由程式化认知技能主导的会计职业中,非程式化认知技能与交互技能的需求在逐渐扩大并加速增长。进一步利用Python技术,对中国各大招聘网站的会计岗位招聘文本进行挖掘和词频分析,验证了以上结果并将具体技能要素分析细化。针对人工智能背景下专业人才培养面临的挑战,提出了多层次、复合型、前端化和终身化等对策。Artificial intelligence(AI)is commonly recognized as a General Purpose Technology(GPT)that will have a profound influence on labor skills and labor markets,hence leading to concerns and controversies over technological unemployment.This study sets out to investigate how to cultivate qualified profes-sionals in the age of AI.It starts by distinguishing Artificial Narrow Intelligence and Artificial General Intelligence.Informed by classical theories of labor economics,it then adopts the ALM model created by Autor,Levy and Murnane as the research framework.Based on the O’net online data and China-related data,it analyzes the changes in the overall skill demands from the accounting profession.Findings show that instead of routine cognitive skills traditionally required by the profession,it is non-routine analytic skills and non-routine interactive skills that are in increasing demand.Further,Python technology is used to identify word frequency of the accounting job recruitment texts from major recruitment websites in China.The results verify the findings from the O’net online data and China-related data and refine the specific skill elements.Implications and suggestions are also discussed.

关 键 词：人工智能 弱人工智能 强人工智能 专业人才培养 ALM模型 会计 程式化技能 非程式化技能 招聘文本 数据挖掘 词频分析 

分 类 号：G642.0[文化科学—高等教育学]