Artificial Intelligence and the Future of Education
Big Promises - Bigger Challenges
Jonathan Michael Spector Du Jing
ABSTRACT
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.
KEYWORDS
artificial intelligence; assessment; big data; formative evaluation; learning outcomes; program evaluation; summative evaluation; systemic change
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