Artificial Intelligence and Comprehensible Input in Second Language Acquisition

Extensive Reading in the Age of Artificial Intelligence Key Constructs in Language Teaching

Artificial Intelligence in English Second-Language Acquisition: Roles, Challenges, and Optimization Pathways

Artificial intelligence (AI) is extending the range of input, feedback, practice and personalised support for English as a second language learning. AI has been applied widely in language education for automated writing evaluation, chatbot-based interaction, adaptive learning, and instructional assistance, and is now helping to enhance the efficiency of learning and student autonomy. However, the rapid development of AI has also brought about several issues, such as a lack of long-term studies, inconsistent integration into teaching, over-reliance on machine-generated answers, and unresolved ethical problems of privacy, bias and academic integrity. This paper examines the application of artificial intelligence in English as a foreign language learning and explores how to improve it. Based on narrative literature review and conceptual analysis of representative studies in AI-enhanced language education, this paper will collect relevant research results and point out existing deficiencies. According to the above analysis, AI will act as a support for teaching and learning, not a replacement for it. The future direction will be focused on personalised guidance, multimodal learning, stronger empirical validation, and responsible governance.

Impact of Artificial intelligence tools on learning motivation in English instruction: a network meta-analysis

The rapid expansion of English-Medium Instruction (EMI) programs and the growing demand for English language proficiency have introduced significant motivational challenges for EFL learners who face linguistic barriers. Artificial intelligence (AI) tools offer a potential solution by providing personalized scaffolding and interactive support. This study conducts a network meta-analysis (NMA) of 16 empirical studies (N = 1,923) to compare the effectiveness of three AI interventions, including Generative AI Chatbots, AI Writing Assistants, and AI Language Learning Applications, on the learning motivation of K-12 and university EFL students in English instruction contexts. Results indicate that all AI tools significantly outperform traditional instruction. AI Language Learning Applications (g = 0.907, k = 2) showed the largest effect, followed by Generative AI Chatbots (g = 0.824, k = 11) and AI Writing Assistants (g = 0.692, k = 3). These findings suggest that interactive and adaptive AI tools are particularly effective in fostering motivation by fulfilling students' basic psychological needs of autonomy and competence. The study provides evidence-based recommendations for the strategic integration of AI into English instruction curricula to optimize student engagement and academic success.

Artificial Intelligence in English Language Teaching and Learning: A Scoping Review of Intelligent Computer-Assisted Language Learning (2015–2025)

The significance of this study is that it uses Intelligent Computer-Assisted Language Learning as an interpretive lens to make sense of a rapidly shifting field, offering a framework to help educators navigate modern generative tools.

Development and Usability Evaluation of an AI-Powered English Vocabulary Learning Application

Learning vocabulary is a major challenge for beginner foreign-language learners, as repetition-based methods can be tiring and often lead to rapid forgetting. Generative AI, particularly Large Language Models (LLMs), enables the automatic creation of large amounts of learning content. However, without appropriate pedagogical guidance, AI-generated materials may be inconsistent and provide limited support for effective learning progress. This study therefore developed and evaluated an adaptive application for beginner-level English vocabulary practice. The application uses an LLM to provide contextual assistance and combines it with a modified spaced repetition mechanism. Its vocabulary and learning activities follow the appropriate CEFR level, while the difficulty is adjusted based on the accuracy of each learner’s answers. The application was evaluated through a mixed-methods usability study involving 72 learners with beginner-level English proficiency. User experience and acceptance were measured using the System Usability Scale (SUS) and selected measures from the Technology Acceptance Model (TAM). Usability was rated highly. Although the participants were beginners with limited English, they generally found the interface comfortable and manageable. Their SUS responses produced a mean of 81.46 (SD = 7.18), a result classified as “Excellent.” They also viewed the application as useful and relatively easy to operate. The respective scores were M = 4.38 (SD = 0.42) for Perceived Usefulness and M = 4.29 (SD = 0.45) for Perceived Ease of Use.

Human vs AI-Generated Texts in Language Learning: A Linguistic Comparison

The findings show that AI-generated texts exhibit greater lexical diversity and syntactic complexity; however, they often exhibit structural uniformity, overuse of cohesive devices, and limited pragmatic depth, and should not replace professionally designed educational materials.