Constructing Evaluation Datasets for Procedural Reasoning: Balancing Naturalness, Grounding, and Multi-Hop Coverage

Researchers present a framework for evaluating procedural reasoning datasets in AI-supported learning systems by comparing three question-generation strategies based on Task-Method-Knowledge (TMK) models. The study demonstrates that strict TMK generation produces the most grounded and usable datasets (96.5% grounded), while transcript-based approaches sacrifice representational alignment for naturalness, highlighting the trade-off between learner-like phrasing and formal grounding in evaluation dataset construction.

This research addresses a critical challenge in AI-supported education: constructing reliable evaluation datasets that balance natural language expression with formal knowledge representation. The study's three-strategy comparison reveals fundamental tensions in procedural reasoning evaluation that extend beyond educational AI applications. Strict TMK generation prioritizes grounding over naturalness, achieving 96.5% grounded questions but potentially producing rigid or less conversational content. Conversely, transcript-first approaches sacrifice representational fidelity for more authentic learner-like questions, introducing context-dependency issues that undermine systematic evaluation. The TMK-aware hybrid approach attempts reconciliation but reveals that procedural coverage doesn't guarantee proper grounding.

This tension mirrors broader challenges in AI evaluation frameworks across domains. As machine learning systems expand into reasoning-heavy applications—from education to complex task automation—the dataset quality becomes the primary constraint on model reliability. The research demonstrates that validation frameworks must explicitly account for multiple quality dimensions rather than optimizing single metrics. The grounding validation framework introduced here, measuring answer support, question self-containment, and multi-hop reasoning coverage, provides a replicable methodology for other procedural domains requiring multi-step reasoning evaluation.

For AI developers, these findings suggest that dataset construction should prioritize representation-aware validation despite increased annotation complexity. Organizations building educational AI systems, autonomous task systems, or reasoning-dependent applications must invest in grounding frameworks before scaling. The 690 question-answer pairs tested across 23 topics provide empirical evidence that naturalness and procedural coverage are insufficient quality signals alone, requiring complementary evaluation mechanisms that validate against formal knowledge representations.

• →Strict TMK-based generation achieves 96.5% grounded questions, outperforming transcript-first and hybrid approaches in representational alignment.
• →Natural phrasing in procedural questions often conflicts with formal grounding requirements, requiring explicit representation-aware validation.
• →Procedural reasoning coverage (multi-hop questions) doesn't correlate with representational grounding, revealing distinct quality dimensions.
• →Grounding validation frameworks measuring answer support, self-containment, and multi-hop reasoning enable systematic dataset quality assessment.
• →Educational AI systems and reasoning-dependent applications require explicit knowledge representation alignment beyond naturalness metrics.