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Taxonomy-Aligned-Conversational-Tutor
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Model Details
Model Provider
Taxonomy-Aligned-Conversational-Tutor
Model Tree
Qwen/Qwen3.5-4B-Base
Input Modalities
Output Modalities
Supported Functionality
TACTutor generates the next teacher response in an ongoing English-learning conversation. It is intended for research on pedagogically adaptive dialogue, including feedback, guided revision, clarification, and learner-supportive conversation management.
The model is a research artifact, not a replacement for a qualified teacher. Outputs should be reviewed before use in high-stakes educational settings.
python
from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "Taxonomy-Aligned-Conversational-Tutor/TACTutor"tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype="auto", device_map="auto", trust_remote_code=True,)One prompt used in our evaluation is:
text
You are an expert ESL tutoring teacher. Your job is to write the next teacher response in an ongoing teacher-student chat.Respond naturally and pedagogically. Do not explain your reasoning. Do not mentiontaxonomy labels, rubrics, or evaluation criteria.Pass this instruction as the system message, followed by the dialogue history, and use the tokenizer's chat template to construct the model input.
On the 78-item TACTBench diagnostic benchmark, the selected checkpoint achieved:
| Metric | Score |
|---|---|
| TACT Overall | 0.832051 |
| Accept | 0.871795 |
| Leak | 0.025641 |
| Off-task | 0.025641 |
The scores above were measured before five representative benchmark examples were released. Those public examples are demonstration data and should be excluded from future hidden-set scoring.
The post-training data are derived from authentic English tutoring dialogue and taxonomy-guided augmentation. The complete training corpus and hidden benchmark are not included in this repository. Five short full-context demonstration examples are available in the companion dataset repository.
The source dialogue is derived from the Teacher-Student Chatroom Corpus version 2 (TSCC v2), which is governed by its own user agreement. This model may produce incorrect, overly explicit, or contextually inappropriate tutoring responses, and its behavior outside English-language tutoring has not been established.
Please cite the following paper when using TACTutor:
bibtex
@article{yang2026tact, title = {TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring}, author = {Yang, Dongjie and Lin, Siyan and Shen, Leixian and Sheng, Rui and Qu, Huamin and Chen, Zixin}, year = {2026}}The model weights are released under the Apache License 2.0. The TSCC-derived source data remain subject to the TSCC user agreement and are not redistributed with the model weights.
from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "Taxonomy-Aligned-Conversational-Tutor/TACTutor"tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype="auto", device_map="auto", trust_remote_code=True,)You are an expert ESL tutoring teacher.Your job is to write the next teacher response in an ongoing teacher-student chat.Respond naturally and pedagogically. Do not explain your reasoning. Do not mentiontaxonomy labels, rubrics, or evaluation criteria.@article{yang2026tact, title = {TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring}, author = {Yang, Dongjie and Lin, Siyan and Shen, Leixian and Sheng, Rui and Qu, Huamin and Chen, Zixin}, year = {2026}}