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    <abstract>In-car AI assistants enhance driving by enabling hands-free interactions, yet they often struggle with multi-turn conversations and fail to handle cognitively complex follow-up questions. This limits their effectiveness in real-world deployment. To address this limitation, we propose a framework that leverages Bloom’s Taxonomy to systematically generate follow-up questions with increasing cognitive complexity and a Gricean-inspired evaluation framework to assess their Logical Consistency, Informativeness, Relevance, and Clarity. We introduce a dataset comprising 750 human-annotated seed questions and 3750 follow-up questions, with human evaluation confirming that 96.68% of the generated questions adhere to the intended Bloom’s Taxonomy levels. Our approach, validated through both LLM-based and human assessments, also identifies the specific cognitive complexity level at which in-car AI assistants begin to falter information that can help developers measure and optimize key cognitive aspects of conversational performance.</abstract>
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%0 Conference Proceedings
%T From Recall to Creation: Generating Follow-Up Questions Using Bloom’s Taxonomy and Grice’s Maxims
%A Yadav, Archana
%A Kashid, Harshvivek
%A Sruthi, Medchalimi
%A JayaPrakash, B.
%A Kullayappa, Chintalapalli Raja
%A Reddy, Mandala Jagadeesh
%A Bhattacharyya, Pushpak
%Y Rehm, Georg
%Y Li, Yunyao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-288-6
%F yadav-etal-2025-recall
%X In-car AI assistants enhance driving by enabling hands-free interactions, yet they often struggle with multi-turn conversations and fail to handle cognitively complex follow-up questions. This limits their effectiveness in real-world deployment. To address this limitation, we propose a framework that leverages Bloom’s Taxonomy to systematically generate follow-up questions with increasing cognitive complexity and a Gricean-inspired evaluation framework to assess their Logical Consistency, Informativeness, Relevance, and Clarity. We introduce a dataset comprising 750 human-annotated seed questions and 3750 follow-up questions, with human evaluation confirming that 96.68% of the generated questions adhere to the intended Bloom’s Taxonomy levels. Our approach, validated through both LLM-based and human assessments, also identifies the specific cognitive complexity level at which in-car AI assistants begin to falter information that can help developers measure and optimize key cognitive aspects of conversational performance.
%R 10.18653/v1/2025.acl-industry.93
%U https://aclanthology.org/2025.acl-industry.93/
%U https://doi.org/10.18653/v1/2025.acl-industry.93
%P 1322-1338

Markdown (Informal)

[From Recall to Creation: Generating Follow-Up Questions Using Bloom’s Taxonomy and Grice’s Maxims](https://aclanthology.org/2025.acl-industry.93/) (Yadav et al., ACL 2025)

• From Recall to Creation: Generating Follow-Up Questions Using Bloom’s Taxonomy and Grice’s Maxims (Yadav et al., ACL 2025)

ACL

• Archana Yadav, Harshvivek Kashid, Medchalimi Sruthi, B JayaPrakash, Chintalapalli Raja Kullayappa, Mandala Jagadeesh Reddy, and Pushpak Bhattacharyya. 2025. From Recall to Creation: Generating Follow-Up Questions Using Bloom’s Taxonomy and Grice’s Maxims. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 1322–1338, Vienna, Austria. Association for Computational Linguistics.