What would the matrix do?: a systematic review of K-12 AI learning contexts and learner-interface interactions
Robert L. Moore, Shiyan Jiang, Brian Abramowitz
Abstract
This systematic review examines the empirical literature published between 2014 and 2021 that situates artificial intelligence within K-12 educational contexts. Our review synthesizes 12 articles and highlights artificial intelligence’s instructional contexts and applications in K-12 learning environments. We focused our synthesis on the learning contexts and the learner-interface interactions. Our findings highlight that most of intelligent systems are being deployed in math or informal settings. Also, there are opportunities for more collaboration to facilitate teaching and learning in domain-specific areas. Additionally, researchers can explore how to implement more collaborative learning opportunities between intelligent tutors and learners. We conclude with a discussion of the reciprocal nature of this technology integration.