Instructional design and learning outcomes of intelligent computer assisted language learning: Systematic review in the field

Research on Artificial Intelligence (AI) in language education becomes more popular. This revolutionary technology is regarded as a promising tool to assist language learning environments by empowering automatic feedback, intelligent tutoring, and personalization. While designing Intelligent Computer Assisted Language Learning (ICALL) environments, educators have followed the First Principles of Instruction (FPI), including problem-centered, activation, demonstration, application, and integration. These instructional design principles were adoptable since they provided a problem-solving focus to the learning activities, satisfying learners’ need for relatedness to the process. Although different instructional design principles were used in ICALL environments and their corresponding effects on student learning outcomes have been reported, few researchers have examined the relevant studies in a systematic review approach, and even fewer scholars have explored the challenges of the instructional design principles adopted. To fill the gap, we conducted a systematic review with 83 related papers screened from the Web of Science, ERIC, Scopus, and ProQuest Social Science Database. We aimed at investigating the application of FPI in ICALL with two research questions: (1) How do ICALL environments foster learning from the perspective of FPI? (2) What are the challenges of ICALL environments? Content analysis and thematic analysis were used to analyze the papers collected. Our research findings disclosed the application of demonstration and application principles for automatic feedback, the activation principle for intelligent tutoring, and the problem-centered and integration principles for personalization. Meanwhile, we discussed the challenges of these studies and proposed instructional design insights and empirical suggestions for ICALL educational practice.