Artificial Intelligence and Comprehensible Input in Second Language Acquisition

Abstract

Artificial Intelligence (AI) has emerged as an increasingly influential tool in second language acquisition (SLA), offering new opportunities for personalized language instruction, immediate feedback, and more interactive learning experiences. Although it has been widely adopted, limited studies have focused on examining whether AI-generated language input aligns with established theories of SLA, particularly Krashen’s Input Hypothesis. The mixed-methods study investigates the extent to which AI-powered learning tools support the learners’ language development by providing comprehensible input (i+1) as proposed in Krashen’s input hypothesis. Quantitative data were collected from 40 first-year students of the Department of English at Shaheed Benazir Bhutto University, Shaheed Benazir Abad (SBBU SBA) using  13-item Likert-scale questionnaire, while qualitative data were collected from 38 screenshots of learner–AI interactions analyzed using content analysis. The quantitative results indicated that the learners perceived AI tools as effective in providing clear language input, immediate feedback, vocabulary support, and increased motivation for language learning. The qualitative results revealed that 60% of AI-generated responses aligned with Krashen’s i+1 principle, 25% were below the learners’ current proficiency level, while 15% exceeded the learners’ current level of proficiency. The findings suggest that although AI tools provide sufficient comprehensible input, they do not consistently provide language at an optimal level as proposed by Krashen’s input hypothesis (i+1). The study contributes to the growing body of literature on SLA by linking established theories with AI-powered language-learning tools. It provides practical implications for educators, curriculum designers, and AI developers seeking to design more adaptive and pedagogically sound language-learning systems.