Artificial Intelligence and Comprehensible Input in Second Language Acquisition: Evaluating AI Language Learning Tools through Krashen's Input Hypothesis
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.
Keywords
Second Language Acquisition (SLA), Artificial Intelligence (AI), Krashen’s Input Hypothesis, Computer-Assisted Language Learning, AI tools in Language Learning
References
- De Costa P, Park J, W. L. (2019). Linguistic entrepreneurship as affective regime: organizations, audit culture, and second/foreign language education policy. Language Policy, 18(3), 387–406. https://doi.org/10.1007/S10993-018-9492-4
- Ellis, R. (2015). Understanding second language acquisition. 365. https://global.oup.com/academic/product/understanding-second-language-acquisition-9780194422048
- Krashen, S. D. (1982). Principles and Practice in Second Language Acquisition
- Zhu, M., Wang, C., & College, C. (2025). A RTICLE A systematic review of research on AI in language education : Current status and future implications. 29(1), 1–29.
- Chapelle C. (2009). The relationship between second language acquisition theory and computer-assisted language learning. Modern Language Journal, 93(SUPPL. 1), 741–753. https://doi.org/10.1111/j.1540-4781.2009.00970.x
- Wang, F., Cheung, A. C. K., & Chai, C. S. (2024). Language learning development in human-AI interaction: A thematic review of the research landscape. System, 125, 103424. https://doi.org/10.1016/j.system.2024.103424
- VanPatten, B. (2020). Input Processing in Adult L2 Acquisition. Theories in Second Language Acquisition, 105–127. https://doi.org/10.4324/9780429503986-6
- Swain, M. (2005). The output hypothesis: Theory and research. Handbook of Research in Second Language Teaching and Learning, 471–483. https://doi.org/10.4324/9781410612700-38
- Levy M. (1997). Computer-Assisted Language Learning. https://doi.org/10.1093/oso/9780198236320.001.0001
- Zhao Y. (2003). Recent Developments in Technology and Language Learning. CALICO Journal, 21, 7–27. https://doi.org/10.1558/cj.v21i1.7-27
- Kern, R. (2014). Technology as Pharmakon: The promise and perils of the internet for foreign language education. Modern Language Journal, 98(1), 340–357. https://doi.org/10.1111/j.1540-4781.2014.12065.x
- Godwin-Jones, R. (2021). Evolving technologies for language learning. 25(3), 6–26.
- Warschauer, M., & Xu, Y. (2024). Artificial intelligence for language learning : Entering a new era. 28(2), 1–4.
- Lai & Li. (2021). TechnologyandLearnerAutonomy_autonomous.
- Seters, V. (2020). Computers and Education : Artificial Intelligence. 1, 1–5. https://doi.org/10.1016/j.caeai.2020.100001
- Zawacki-Richter, O., Marín, V. I., & Bond, M. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators ?
- Stockwell, G. (2013). Technology and Motivation in English-Language Teaching and Learning. International Perspectives on Motivation, 156–175. https://doi.org/10.1057/9781137000873_9
- Yaqoob, A., Nawab, M., & Sattar, S. (2025). The Impact of Technology Integration on Language Learning: Examining Learner Motivation, Engagement, and Outcome. International Journal of Law and Policy, 3(7), 28–51. https://doi.org/10.59022/ijlp.347
- Apriani, E., Cardoso, L., & Obaid, A. J. (2024). Impact of AI-Powered Chatbots on EFL Students ’ Writing Skills, Self-Efficacy, and Self-Regulation : A Mixed-Methods Study. 1(2), 57–72.
- Alaqlobi, O., Alduais, A., Alasmari, M., & Qasem, F. (2024). Artificial Intelligence in Language Acquisition : A Balancing Act of Potential and Challenges. 06(06), 1103–1122.
- Asim Kumar Betal. (2023). Enhancing Second Language Acquisition through Artificial Intelligence (AI): Current Insights and Future Directions. 7(39), 167–186.
- Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for Language Teaching and Learning. 1–14. https://doi.org/10.1177/00336882231162868
- Tzu Yu Tai & Howard Chen. (2024). Navigating elementary EFL speaking skills with generative AI chatbots: Exploring individual and paired interactions. Computers & Education, 220, 105112. https://doi.org/10.1016/j.compedu.2024.105112
- Bibauw, S., François, T., & Desmet, P. (2022). Dialogue Systems for Language Learning : Chatbots and beyond. 121–134.
- Bender, E, Gebru, T, McMillan-Major, A, & S. S. (2021). On the dangers of stochastic parrots: Can language models be too big? FAccT 2021 - Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922
- Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15(3), 625-632. https://doi.org/10.1007/s10459-010-9222-y
- Sultan A. Almelhes. (2023). AReviewofArtificialIntelligenceAdoptioninSecond-LanguageLearning.pdf.
- Kohnke, L., & Moorhouse, B. L. (2020). Facilitating Synchronous Online Language Learning through Zoom. https://doi.org/10.1177/0033688220937235