Artificial Intelligence and the Future of Education： Big Promises -Bigger Challenges  

作　　者：Jonathan Michael Spector Du Jing 

机构地区：[1]University of North Texas, USA [2]Beijing Normal University, Beijing 100875

出　　处：《学术界》2017年第4期257-265,共9页Academics

摘　　要：The history of educational technology in the last 50 years contains few instances of dramatic improvements in learning based on the adoption of a particular technology.An example involving artificial intelligence occurred in the 1990s with the development of intelligent tutoring systems( ITSs). What happened with ITSs was that their success was limited to well-defined and relatively simple declarative and procedural learning tasks(e. g.,learning how to write a recursive function in LISP; doing multi-column addition),and improvements that were observed tended to be more limited than promised(e. g.,one standard deviation improvement at best rather than the promised standard deviation improvement).Still,there was some progress in terms of how to conceptualize learning. A seldom documented limitation was the notion of only viewing learning from only content and cognitive perspectives( i. e.,in terms of memory limitations,prior knowledge,bug libraries,learning hierarchies and sequences etc.). Little attention was paid to education conceived more broadly than developing specific cognitive skills with highly constrained problems. New technologies offer the potential to create dynamic and multi-dimensional models of a particular learner,and to track large data sets of learning activities,resources,interventions,and outcomes over a great many learners. Using those data to personalize learning for a particular learner developing knowledge,competence and understanding in a specific domain of inquiry is finally a real possibility. While the potential to make significant progress is clearly possible,the reality is less not so promising. There are many as yet unmet challenging some of which will be mentioned in this paper. A persistent worry is that educational technologists and computer scientists will again promise too much,too soon at too little cost and with too little effort and attention to the realities in schools and universities.The history of educational technology in the last 50 years contains few in- stances of dramatic improvements in learning based on the adoption of a particular technology. An example involving artificial intelligence occurred in the 1990s with the development of in- telligent tutoring systems （ 1TSs）. What happened with ITSs was that their success was limited to well - defined and relatively simple declarative and procedural learning tasks （ e. g. , learn- ing how to write a recursive function in LISP ; doing multi - column addition） , and im- provem,,~nts r}mt were obset-ved tended to be more limited than promised （ e g. , one standard deviatior~ imFrovement at best rather than the promised standard deviation improvement）. Still, them was some progress in terms of how to conceptualize learning. A seldom documen- ted limitation was the notion of only viewing learning from only content and cognitive per- spectives （i. e. , in terms of memory limitations, prior knowledge, bug libraries, learning hi- erarchies and sequences etc. ）. Little attention was paid to education conceived more broadly than ＆veloping specific cognitive skills with highly constrained problems. New technologies offer the potential to create dynamic and multi - dimensional models of a particular learner, and to track large data sets of leaming activities, resources, interventions, and outcomes over a great many learners. Using those data to personalize learning for a particular learner develo- ping knowledge, competence and understanding in a specific domain of inquiry is finally a real possibility. While the potential to make significant progress is clearly possible, the reality is less not so promising. There are many as yet unmet challenging some of which wilt be mentioned in this paper. A persistent worry is that educational technologists and computer scientists will again promise too much, too soon at too little cost and with too little effort and attention to the realities in schools and universities.

关 键 词：智能教学系统 认知技能 教育技术人员 中国 

分 类 号：G434[文化科学—教育学]