Syed Billah

Imran Kabir, Md Alimoor Reza, Syed Billah

Large multimodal models (LMMs) are increasingly integrated into autonomous driving systems for user interaction. However, their limitations in fine-grained spatial reasoning pose challenges for system interpretability and user trust. We introduce Logic-RAG, a novel Retrieval-Augmented Generation (RAG) framework that improves LMMs' spatial understanding in driving scenarios. Logic-RAG constructs a dynamic knowledge base (KB) about object-object relationships in first-order logic (FOL) using a perception module, a query-to-logic embedder, and a logical inference engine. We evaluated Logic-RAG on visual-spatial queries using both synthetic and real-world driving videos. When using popular LMMs (GPT-4V, Claude 3.5) as proxies for an autonomous driving system, these models achieved only 55% accuracy on synthetic driving scenes and under 75% on real-world driving scenes. Augmenting them with Logic-RAG increased their accuracies to over 80% and 90%, respectively. An ablation study showed that even without logical inference, the fact-based context constructed by Logic-RAG alone improved accuracy by 15%. Logic-RAG is extensible: it allows seamless replacement of individual components with improved versions and enables domain experts to compose new knowledge in both FOL and natural language. In sum, Logic-RAG addresses critical spatial reasoning deficiencies in LMMs for autonomous driving applications. Code and data are available at https://github.com/Imran2205/LogicRAG.

Md Touhidul Islam, Mahir Akgun, Syed Billah

Generative AI (GenAI) is increasingly used as a knowledge partner in higher education, raising the need for instructional designs that emphasize AI literacy practices such as evaluating output credibility and maintaining human accountability. Existing AI literacy frameworks focus more on what learners should do than on how these practices are enacted in routine student-GenAI collaboration. We address this gap by framing student-GenAI interaction as a transactive memory partnership, where credibility regulates reliance and verification. To make this process visible during coursework, we used a weaker large language model (LLM): small enough to run on most students' computers during class, helpful enough to support learning, but not so capable that it removes the need for verification. In an undergraduate STEM course, students were randomly assigned to one of three conditions across repeated activities: reflection-first (think first, then consult AI), verification-required (use AI, then evaluate the output), or control (unrestricted use). Students completed a transactive memory survey at three time points (N = 42). Weighted credibility diverged by condition over time. ANCOVA controlling for baseline credibility showed a condition effect at mid-semester, F(2, 38) = 4.02, p = .026, partial eta squared = .175, and a stronger effect at post-intervention, F(2, 38) = 5.48, p = .008, partial eta squared = .224; adjusted means were lowest in reflection-first, intermediate in verification-required, and highest in control. Parallel analyses of specialization and coordination were not significant. These findings suggest that workflow sequencing, deliberate use of weaker LLMs, and accountability cues embedded in assignment instructions can recalibrate students' credibility judgments in GenAI use, with reflection-first producing the strongest downward shift in reliance.