Large Language Models (LLMs) hold significant promise for accelerating academic ideation but face critical challenges in grounding ideas and mitigating confirmation bias during refinement. To address these limitations, we propose MotivGraph-SoIQ, a novel framework that enhances LLM ideation by integrating a Motivational Knowledge Graph (MotivGraph), which provides essential grounding from research literature, with a Q-Driven Socratic Ideator. The Ideator, a dual-agent system utilizing Socratic questioning, facilitates a rigorous refinement process that mitigates confirmation bias and significantly improves idea quality across dimensions of novelty, experimental feasibility, and motivation. Our experimental results demonstrate MotivGraph-SoIQ’s effectiveness. Comparative studies show significant quantitative improvements over SOTA methods across LLM-based scoring, ELO ranking, and human evaluation. Ablation studies further validate the crucial contributions of both the MotivGraph for enhancing idea novelty and practicality, and the Socratic dialogue with the teacher agent for substantial quality improvement. This work underscores the potential of combining structured knowledge with interactive, critique-based refinement for robust LLM ideation.