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.