ORIGINAL RESEARCH article
Front. Educ.
Sec. Digital Learning Innovations
This article is part of the Research TopicArtificial Intelligence in Educational Technology: Innovations, Impacts, and Future DirectionsView all 20 articles
Personalized Language Learning With an LLM Chatbot: Effects of Immediate vs. Delayed Corrective Feedback
Provisionally accepted
- 1Division of Speech, Music & Hearing, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
- 2Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy
- 3KTH Royal Institute of Technology, Stockholm, Sweden
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The emergence of Large Language Models (LLMs) has opened new possibilities for language learning through conversational interaction with chatbots. Yet, little empirical evidence exists on how students experience such interactions and how corrective feedback should be provided. Research suggests that immediate corrective feedback is generally more effective than delayed feedback (Fu and Li, 2022, Opitz et al., 2011). Nevertheless, learners' perception of this effectiveness and their preferences for feedback timing, particularly in the domain of Computer-Assisted Language Learning (CALL), remain underexplored. This study investigates the feasibility of providing immediate feedback and examines the impact of feedback timing on user experience and grammar learning gains in English. An in-the-wild experiment was conducted with 66 L2 English learners, who integrated chatbot sessions into their English course as an extracurricular activity over one semester. Participants were randomly assigned to two groups receiving feedback either during or after the conversation. Findings reveal no significant difference in learning gains, but immediate feedback enhanced user experience, leading to overall positive perceptions of the chatbot. Additionally, we explore users' perceptions of the chatbot's social role and personality, offering a roadmap for future enhancements. These results provide valuable insights into the potential of LLMs and chatbots for language learning.
Keywords: Chatbot, Corrective Feedback Timing, gpt, large language model (LLM), second language learning
Received: 11 Sep 2025; Accepted: 21 Jan 2026.
Copyright: © 2026 M. Kamelabad, Turano, Lundin and Skantze. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Alireza M. Kamelabad
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