Computer-Assisted Language Learning

Latest papers

11 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Beyond Aggregate WER: In-domain Adaptation, Learner Speech, Code-Switching, and Speaker Attribution in Online Spanish Learning Classrooms
Oct 27, 2026Olga Kellert, Samhitha Harish, Johnatan E Bonilla +3ASR Evaluation Domain Adaptation for ASR
Arizona State University · Freie Universität Berlin
SLA-LENS: From Classroom Transcripts to CSLA-Informed and Evidence-Linked L2 Interaction Analytics
Oct 16, 2026Nemika Tyagi, Olga Kellert, Phanos Kareen +1Second Language Acquisition LLM-Assisted Annotation
Arizona State University
Grammar Concept Annotation at Scale: Deployed Fine-Tuned Small Language Models Outperform Prompted Frontier Models
Oct 7, 2026Marjan Celikik, Ana Peleteiro Ramallo, Javier MoralesSecond Language Acquisition Small Language Models
Preply
LLMersion: A Local-First AI Agent Framework for Low-Cost Home Language Learning toward Educational Equity
Sep 12, 2026Qiming Guo, Jinwen Tang, Xingran Huang +3Computer-Assisted Language Learning AI in Education
Dept. of Computing Sciences Texas A&M University–Corpus Christi Corpus Christi, TX, USA · Dept. of Electrical Engineering and Computer Science University of Missouri Columbia, MO, USA · Dept. of Computer Science and Engineering University of California, Riverside Riverside, CA, USA +2
Structure and Implementation of New Practical English Textbooks Driven by Artificial Intelligence
Sep 2, 2026Ya Wang, Lei Zhang, Xueguang Yang +1Intelligent Tutoring Systems AI in Education
Air Force Logistics Academy, Xuzhou, 221000,China
Question Type, Cognitive Load, and CEFR Alignment: Evaluating LLM-Generated EFL Grammar Drill Exercises
Jun 1, 2026Steve Woollaston, Brendan Flanagan, Yuko Toyokawa +1LLM Evaluation Computer-Assisted Language Learning
Kyoto University, Japan · Ritsumeikan University, Japan
Classification of non-analyzable word types in web documents to implement an effective Korean e-learning system
May 28, 2026Sang-Taek Park, Ae-Lim Ahn, Eric Laporte +1Corpus Linguistics Computer-Assisted Language Learning
DICORA, Hankuk University of Foreign Studies, Korea · LIGM, Université Paris-Est, France
Controllable Spoken Dialogue Generation: An LLM-Driven Grading System for K-12 Non-Native English Learners
Apr 24, 2026Haidong Yuan, Haokun Zhao, Wanshi Xu +4RL for Language Models LLM Alignment
Peking University · Fudan University · Tencent +2
AFRILANGTUTOR: Advancing Language Tutoring and Culture Education in Low-Resource Languages with Large Language Models
Apr 22, 2026Tadesse Destaw Belay, Shahriar Kabir Nahin, Israel Abebe Azime +6Multilingual Language Models Intelligent Tutoring Systems
Instituto Politécnico Nacional, Mexico · University of South Florida, FL, USA · Saarland University, Germany +2
Listen, Correct, and Feed Back: Spoken Pedagogical Feedback Generation
Mar 28, 2026Junhong Liang, Yifan Lu, Ekaterina Kochmar +1Computer-Assisted Language Learning Educational Feedback Generation
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
Profiling learners' affective engagement: Emotion AI, intercultural pragmatics, and language learning
Mar 20, 2026Robert Godwin-JonesComputer-Assisted Language Learning Affective Computing
Virginia Commonwealth University

Oct 27, 2026J/K move · Enter open · S save

Beyond Aggregate WER: In-domain Adaptation, Learner Speech, Code-Switching, and Speaker Attribution in Online Spanish Learning Classrooms

Olga Kellert, Samhitha Harish, Johnatan E Bonilla, Nemika Tyagi+2

Arizona State University · Freie Universität Berlin

ASR Evaluation Domain Adaptation for ASR Code-Switching Speech Recognition Speaker-Attributed ASR Computer-Assisted Language Learning

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Automatic speech recognition (ASR) increasingly supplies transcripts for educational NLP, but aggregate word error rate (WER) can conceal errors concentrated in learner speech, multilingual turns, and speaker attribution. We compare platform-integrated, open-weight, adapted, and commercial ASR configurations on held-out sessions of online L2 Spanish interaction between L1 English learners and their instructors and tutors. Because English is the learner's shared first language, the interactions contain Spanish-only, English-only, and code-switched turns. Evaluation combines overall WER and character error rate (CER), WER disaggregated by speaker role and language context, and diarization error rate (DER). We also adapt Whisper large-v3 and MOSS-Transcribe-Diarize with LoRA using human-corrected in-domain classroom data. MOSS adaptation produced consistent improvements across speaker roles and language contexts, whereas Whisper showed smaller gains. Domain match proved more important than data volume: Larger out-of-domain bilingual and learner corpora did not outperform the smaller classroom corpus, where learner speech and code-switching are embedded in instructor--learner interaction. The open-weight MOSS adapter was competitive with commercial Muse Voice Transcribe on WER and CER and achieved lower system-provided DER on the recordings eligible for each system. Learner, English-only, and code-switched speech remained more difficult than instructor and Spanish-only speech. The findings demonstrate the value of condition-aware evaluation and interactionally matched data for bilingual educational ASR.