D e MINT : Automated Language Debriefing for E nglish Learners via AI Chatbot Analysis of Meeting Transcripts (2025)

Abstract: The objective of the DeMINT project is to develop a conversational tutoring system aimed at enhancing non-native English speakers’ language skills through post-meeting analysis of the transcriptions of video conferences in which they have participated. This paper describes the model developed and the results obtained through a human evaluation conducted with learners of English as a second language.

## project overview demint (automated language debriefing for english learners via ai chatbot analysis of meeting transcripts) was developed by the transducens research group at universitat d'alacant from january to september 2024. it is funded under the utter project, 1 a collaborative research and innovation project under horizon europe (grant agreement id: 101070631), via financial support to third parties. conversational intelligent tutoring systems (its) are poised to revolutionize education by providing personalized, interactive, and inclusive one-on-one learning. the objective of this project is to develop an educational chatbot aimed at leveraging large language models (llms) to improve speakers' language skills through interactive error-driven conversations. a full-length description of our model is described by @xcite . although not centered on machine translation (mt), our chatbot relies on components common to many mt-related tasks, such as speech transcription or grammatical error correction. moreover, a similar its could also support translation tasks by helping translators improve text quality. the architecture of our system is described in figure 1 , which reflects the modules that interact to provide a comprehensive tutoring experience: © 2025 the authors. this article is licensed under a creative commons 4.0 licence, no derivative works, attribution, cc-by-nd. 1 https://he-utter.eu chat bot er r or annot at i on er r or ex pl anat i on ret r i ev al augment ed gener at i on col bert engl i s h book s llm1 speak er di ar i z at i on speec h r ec ogni t i on gr ammat i c al er r or cor r ec t i on t5-l i k e knowl edge t r ac i ng nex t ut t er anc e gener at i on conv er s at i on hi s t or y py annot e empat het i c t eac her llm2 lear ni ng pl an or c hes t r at or llm3 er r or pr es er v i ng w hi sper errant figure 1: main components of the demint system. 1. diarization: the pipeline starts by processing the audio recorded during an online meeting with the library pyannote.audio, foot_0 to identify the segments corresponding to each speaker. 2. speech recognition: audio fragments are processed by a speech recognition model built by fine-tuning whisper foot_1 on a custom dataset of spoken sentences with grammatical errors. foot_2 ## results of the project as the results of this project, a prototype of our its has been implemented. all the models and the datasets used to build them have been released and are available on the project repository. 8 the prototype developed has been evaluated through interactions between the chatbot and l1-spanish/l2-english students. seven students with b2/c1 english levels (according to the common european framework of reference for languages) participated in 15 video-calls. these calls were recorded and processed by our its, and students spent about 75 minutes interacting with it. students were then surveyed on overall user experience and chatbot's effectiveness as an english tutor, using a 1-5 likert scale. in response to "did you enjoy interacting with the chatbot?", all gave positive feedback (scores of 4 or 5). fluency was identified as the main area for improvement, with an average score of 3. regarding the chatbot's role as a tutor, the main concern was its accuracy in identifying speech errors (average score: 3). still, most students felt it helped improve aspects of their english, with five out of seven giving a score of 4. when asked about future use of similar tutoring tools in video conferences, all but one rated their interest as 4 or 5, showing overall enthusiasm for such tools.