Shaping AI-driven language learners’ learning approaches, and motivation through computational thinking skills in computer-assisted language learning (CTCALL): A bisymmetric research design

Abstract With the development and integration of information and communication technologies, the learning needs and skills have revised and refined, and 21st-century digital skills have recently become more important than before. Computational thinking skill, the heart of the 21st-century digital skills, has recently been developed and validated in the field of computer-assisted language learning (CTCALL), but has received little attention in CALL. Consequently, the researcher cultivated this important skill in Intelligent CALL (ICALL) for 104 Iranian EFL learners and explored it on the language learners' motivation and their learning approaches to ICALL. The bisymmetric research design validated the factorial validity of this skill and showed that the abstraction and evaluation skills were among the necessary conditions to shape language learners' deep approach to ICALL, and further level of them can shape their deep approach to it as well. However, generalization was not among the necessary conditions, but the higher language learners can identify patterns in solving specific language tasks with AI, and applying those patterns to other language tasks involves new ways of thinking and cross-referencing ideas with AI, culminating in maximizing their ICALL for learning language. Based on these findings, the study provides a new conceptual model to the field of CALL and recommends that teachers adopt a problem-solving approach within ICALL and cultivate new skills, such as CTCALL, in their language learning environment to foster motivation and deep, organized approaches to ICALL.