@inproceedings{aiba-etal-2026-incorporating,
title = "Incorporating Respect into {LLM}-Based Academic Feedback: A {BI}-{R} Framework for Instructing Students after {Q}{\&}{A} Sessions",
author = "Aiba, Mayuko and
Saito, Daisuke and
Minematsu, Nobuaki",
editor = "Riccardi, Giuseppe and
Mousavi, Seyed Mahed and
Torres, Maria Ines and
Yoshino, Koichiro and
Callejas, Zoraida and
Chowdhury, Shammur Absar and
Chen, Yun-Nung and
Bechet, Frederic and
Gustafson, Joakim and
Damnati, G{\'e}raldine and
Papangelis, Alex and
D{'}Haro, Luis Fernando and
Mendon{\c{c}}a, John and
Bernardi, Raffaella and
Hakkani-Tur, Dilek and
Di Fabbrizio, Giuseppe {''}Pino{''} and
Kawahara, Tatsuya and
Alam, Firoj and
Tur, Gokhan and
Johnston, Michael",
booktitle = "Proceedings of the 16th International Workshop on Spoken Dialogue System Technology",
month = feb,
year = "2026",
address = "Trento, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.iwsds-1.29/",
pages = "288--301",
abstract = "In academic research, post-presentation {Q}{\&}{A} sessions are crucial for deepening understanding and shaping research directions. Supervisors' comments are particularly valuable when they highlight perspectives that students have not yet fully considered. Such comments typically arise from careful reasoning within dialogue, yet large language models ({LLM}s) still struggle to reason precisely about dialogue context and communicative intentions. Building on {LLM}s, this study proposes a feedback generation framework based on the Belief{--}Desire{--}Intention ({BDI}) model, which conceptualizes {Q}{\&}{A} sessions as cognitive interactions between presenters and questioners. We further extend this framework into {BI}-{R} by introducing Respect as an explicit dimension, ensuring that generated feedback is not only accurate but also pedagogically constructive. We evaluated the proposed framework ({BDI} and {BI}-{R}) through comparative experiments with master{'}s students and field experiments with doctoral students during pre-defense presentations. Results showed that while the {BDI} prompt did not outperform the baseline, the {BI}-{R} prompt was particularly effective when students did not fully grasp the broader context or background of the questions. When comparing {BDI} and {BI}-{R}, the inclusion of Respect improved the tone and pedagogical appropriateness of feedback. These findings highlight the potential of the proposed framework as a supportive tool for training students and early-career researchers."
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<abstract>In academic research, post-presentation Q&A sessions are crucial for deepening understanding and shaping research directions. Supervisors’ comments are particularly valuable when they highlight perspectives that students have not yet fully considered. Such comments typically arise from careful reasoning within dialogue, yet large language models (LLMs) still struggle to reason precisely about dialogue context and communicative intentions. Building on LLMs, this study proposes a feedback generation framework based on the Belief–Desire–Intention (BDI) model, which conceptualizes Q&A sessions as cognitive interactions between presenters and questioners. We further extend this framework into BI-R by introducing Respect as an explicit dimension, ensuring that generated feedback is not only accurate but also pedagogically constructive. We evaluated the proposed framework (BDI and BI-R) through comparative experiments with master’s students and field experiments with doctoral students during pre-defense presentations. Results showed that while the BDI prompt did not outperform the baseline, the BI-R prompt was particularly effective when students did not fully grasp the broader context or background of the questions. When comparing BDI and BI-R, the inclusion of Respect improved the tone and pedagogical appropriateness of feedback. These findings highlight the potential of the proposed framework as a supportive tool for training students and early-career researchers.</abstract>
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%0 Conference Proceedings
%T Incorporating Respect into LLM-Based Academic Feedback: A BI-R Framework for Instructing Students after Q&A Sessions
%A Aiba, Mayuko
%A Saito, Daisuke
%A Minematsu, Nobuaki
%Y Riccardi, Giuseppe
%Y Mousavi, Seyed Mahed
%Y Torres, Maria Ines
%Y Yoshino, Koichiro
%Y Callejas, Zoraida
%Y Chowdhury, Shammur Absar
%Y Chen, Yun-Nung
%Y Bechet, Frederic
%Y Gustafson, Joakim
%Y Damnati, Géraldine
%Y Papangelis, Alex
%Y D’Haro, Luis Fernando
%Y Mendonça, John
%Y Bernardi, Raffaella
%Y Hakkani-Tur, Dilek
%Y Di Fabbrizio, Giuseppe ”Pino”
%Y Kawahara, Tatsuya
%Y Alam, Firoj
%Y Tur, Gokhan
%Y Johnston, Michael
%S Proceedings of the 16th International Workshop on Spoken Dialogue System Technology
%D 2026
%8 February
%I Association for Computational Linguistics
%C Trento, Italy
%F aiba-etal-2026-incorporating
%X In academic research, post-presentation Q&A sessions are crucial for deepening understanding and shaping research directions. Supervisors’ comments are particularly valuable when they highlight perspectives that students have not yet fully considered. Such comments typically arise from careful reasoning within dialogue, yet large language models (LLMs) still struggle to reason precisely about dialogue context and communicative intentions. Building on LLMs, this study proposes a feedback generation framework based on the Belief–Desire–Intention (BDI) model, which conceptualizes Q&A sessions as cognitive interactions between presenters and questioners. We further extend this framework into BI-R by introducing Respect as an explicit dimension, ensuring that generated feedback is not only accurate but also pedagogically constructive. We evaluated the proposed framework (BDI and BI-R) through comparative experiments with master’s students and field experiments with doctoral students during pre-defense presentations. Results showed that while the BDI prompt did not outperform the baseline, the BI-R prompt was particularly effective when students did not fully grasp the broader context or background of the questions. When comparing BDI and BI-R, the inclusion of Respect improved the tone and pedagogical appropriateness of feedback. These findings highlight the potential of the proposed framework as a supportive tool for training students and early-career researchers.
%U https://aclanthology.org/2026.iwsds-1.29/
%P 288-301
Markdown (Informal)
[Incorporating Respect into LLM-Based Academic Feedback: A BI-R Framework for Instructing Students after Q&A Sessions](https://aclanthology.org/2026.iwsds-1.29/) (Aiba et al., IWSDS 2026)
ACL