智能教研的本质、误区与回归  

Intelligent Teaching Research:Essence,Misunderstanding and Return

作　　者：王丽华[1,2] 时一帆 卢国成 WANG Lihua;SHI Yifan;LU Guocheng

机构地区：[1]浙江师范大学教育学院,浙江金华321004 [2]浙江全省智能教育技术与应用重点实验室 [3]广西职业技术学院马克思主义学院,广西南宁530226

出　　处：《现代远程教育研究》2025年第2期33-41,共9页Modern Distance Education Research

基　　金：国家社会科学基金“十三五”规划教育学一般课题“近70年中小学教研活动的中国经验研究”(BHA190131)。

摘　　要：智能教研是人工智能热潮下的政策导向与实践热点,然而,当前普遍存在对智能教研本质的认识不清,由此导致对其的盲目追捧及实践误区频现。基于对人工智能本质和教研本质的双重考察,智能教研在本质上是一种以可信模型采集的教研数据为基础,通过“人—机—人”协同教研决策,实现“三性”(即科学性、技术性和经验性)优化的循证实践。据此审视智能教研的典型误区主要体现在囿于可疑模型的教研数据、高估数据效用的教研决策、陷入单性异化的教研循证实践三个方面。为走出现有误区,未来智能教研亟须通过三大途径实现理性回归:一是联合研发保障智能教研数据质量的可信基础模型;二是借鉴相关领域的已有研究,合力探索智能教研“人—机—人”协同决策的实践新课题;三是内外联动推进科学性证据库的建设,正视经验性证据的合理价值,持续优化智能教研的循证实践。Intelligent teaching research(ITR)is a policy direction and practical hotspot under the trend of artificial intelligence.However,there is currently a widespread lack of understanding of the essence of ITR,which leads to blind pursuit and frequent practical misunderstandings.Based on a dual examination of the essence of artificial intelligence and teaching research,it is proposed that ITR is an evidence-based practice of“three-essence”(scientific,technical and empirical)optimization,which is realized by human-machine-human collaborative decision making from the teaching research data by trusted models.Based on this,the typical misconceptions of ITR are mainly reflected in three aspects:teaching research data constrained by suspicious models,teaching research decisions that overestimate the utility of data,and evidence-based teaching research practice that fall into singularity.In order to avoid misunderstandings,ITR urgently need to achieve rational regression through three ways in the future:first,it is essential to jointly develop trustworthy basic models to ensure the quality of ITR data;the second is to draw on existing research in related fields and work together to explore new practical topics for ITR“human-machine-human”collaborative decision making;the third is to promote the construction of a scientific evidence base through internal and external linkage,recognize the reasonable value of empirical evidence,and continuously optimize the evidence-based practice of ITR.

关 键 词：智能教研 可信模型 “人—机—人”协同决策 循证实践 

分 类 号：G434[文化科学—教育学]