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The affordances of AI‐enabled automatic scoring applications on learners’ continuous learning intention: An empirical study in China
Author(s) -
Fu Shixuan,
Gu Huimin,
Yang Bo
Publication year - 2020
Publication title -
british journal of educational technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.79
H-Index - 95
eISSN - 1467-8535
pISSN - 0007-1013
DOI - 10.1111/bjet.12995
Subject(s) - affordance , computer science , pronunciation , language acquisition , educational technology , artificial intelligence , natural language processing , human–computer interaction , mathematics education , psychology , linguistics , philosophy
Traditional educational giants and natural language processing companies have launched several artificial intelligence (AI)‐enabled digital learning applications to facilitate language learning. One typical application of AI in digital language education is the automatic scoring application that provides feedback on pronunciation repeat outcomes. This research is motivated by the usage of automatic scoring‐empowered digital learning tools by language learners, and set out to uncover the influencing mechanisms of AI‐enabled automatic scoring application affordances on learners’ continuous learning intention. Specifically, based on affordance theory, we found several automatic scoring application affordances through in‐depth interviews. Considering the current lack of investigations on the mechanisms underlying automatic scoring application and its implications for learners’ learning behaviors, we built a model to examine the role of automatic scoring application affordances on cognitive/emotional engagement and following continuous learning intention. We further examined the moderation role of in‐job learners and student learners on the above relationships. The model was tested using a survey of 260 Chinese foreign language learners who used AI‐empowered learning tools to facilitate their language learning practices. This study explores why learners continuously use AI‐enabled automatic scoring applications by identifying the affordances that differentiate it from traditional educational technologies. Practitioners could take the identified affordances into account when designing AI‐enabled language learning applications.