Jul 2026· ACM Transactions on Information Systems· Vol 44, pp. 1 - 38· 0 citations· 69 references
TL;DR
SPARK-KT (Stepwise Prerequisite-Aware Reasoning for Knowledge Tracing) is introduced, a framework that reconceptualizes knowledge tracing through the lens of cognitive dependency structures and improves predictive accuracy while providing fine-grained interpretability.
Abstract
Understanding how students think—not merely what they know—remains the central challenge of knowledge tracing. Current approaches model knowledge as a flat collection of concepts and are fundamentally unable to capture the hierarchical reasoning processes that govern human problem-solving. We introduce SPARK-KT (Stepwise Prerequisite-Aware Reasoning for Knowledge Tracing), a framework that reconceptualizes knowledge tracing through the lens of cognitive dependency structures. SPARK-KT leverages Large Language Models to automatically decompose each question into a Directed Acyclic Graph of reasoning steps, where nodes represent atomic cognitive operations, and edges encode prerequisite constraints. A novel dependency-aware mastery propagation mechanism ensures that a student’s competence on any step is bounded by their weakest prerequisite—mirroring the pedagogical reality that complex skills cannot be deployed without foundational mastery. Through a dual-stream architecture that disentangles conceptual knowledge from procedural structure, SPARK-KT improves predictive accuracy while providing fine-grained interpretability: it identifies not just whether a student will succeed, but which specific reasoning step constitutes their cognitive bottleneck. Experiments across three diverse educational domains demonstrate consistent improvements over sixteen state-of-the-art baselines, with particularly strong gains on multi-step reasoning problems. Beyond prediction, SPARK-KT enables actionable diagnostics—revealing latent potential masked by prerequisite gaps and informing precisely targeted interventions.
Knowledge tracing (KT) aims to predict students'future performance by modeling their evolving knowledge states from historical interactions. Existing KT methods usually treat the raw interaction sequence as a unified behavioral process, overlooking the phase-specific nature of learning behaviors. Our preliminary observations show that students are more likely to correctly answer previously failed knowledge concepts after sufficient practice, suggesting a transition from ability-building to proficiency-oriented learning. Motivated by this, we propose Phase-Aware Knowledge Tracing (PAKT), a KT framework that decomposes student interactions into ability and proficiency phases based on the tailored decomposition mechanism. To effectively exploit the decomposed sequences, we design a multi-branch Transformer with a type-aware readout module to jointly capture phase-specific and holistic knowledge states. We further provide a causal analysis to reveal the confounding bias caused by entangling complex learning behaviors in phase-agnostic KT models. Extensive experiments on six public benchmarks demonstrate that our method consistently outperforms representative baselines, with a maximum AUC gain of 1.33% and an average gain of 0.82%.
Duantengchuan Li, Yingqian Bi, Jinsong Chen et al.· 0 citations
It is argued that understanding AI knowledge creation is essential for bridging traditional human KM with the emerging discipline of AI Knowledge Management, and for designing governance structures that account for the inherent incompleteness and inconsistency of LLM knowledge.
T. Nguyen· European Conference on Knowl...· 0 citations
Large language models (LLMs) often produce reasoning steps that are superficially coherent yet internally inconsistent, leading to unreliable outputs. Since such failures typically arise from implicit or poorly-grounded knowledge, we introduce Grounded Reasoning in Dependency (GRiD) , a novel dependency-aware reasoning framework that explicitly grounds reasoning steps in structured knowledge. GRiD represents reasoning as a graph consisting of interconnected knowledge extraction nodes and reasoning nodes, enforcing logical consistency through explicit dependencies. Each reasoning step is validated via a lightweight, step-wise verifier that ensures logical correctness relative to its premises. Extensive experiments across diverse reasoning benchmarks—including StrategyQA, CommonsenseQA, GPQA, and TruthfulQA—demonstrate that GRiD substantially improves reasoning accuracy, consistency, and faithfulness compared to recent state-of-the-art structured reasoning methods. Notably, GRiD enhances performance even when applied purely as a lightweight verification module at inference time, underscoring its generalizability and practical utility † .
Xiangyu Wen, Min Li, Junhua Huang et al.· Neural Information Processin...· 2 citations
Knowledge Tracing (KT) aims to assess students'dynamic knowledge states from their learning histories. While most existing KT methods focus on single-domain learning with notable success, real-world learning scenarios often involve multiple domains simultaneously, introducing two critical factors: 1) Cognitive load, arising from managing learning across domains in both temporal and knowledge dimensions. 2) Knowledge transfer, where knowledge states in one domain influence related states both within and across domains. In this paper, we focus on exploring these factors to improve students'knowledge state assessment in multi-domain learning scenarios and propose a novel method incorporating cognitive Load and knowledge Transfer for Multi-domain Knowledge Tracing (LT-MKT). Specifically, to bridge isolated domains, LT-MKT first integrates textual information from questions and their associated concepts to construct a Multi-domain Hierarchical Graph, leveraging the advanced representational capabilities of large language models (LLMs). Then, cross-domain features in both the temporal and knowledge dimensions are explicitly modeled to capture the effects of cognitive load. Additionally, a knowledge transfer module is designed to model the propagation of knowledge states within and across domains. By jointly modeling these factors, LT-MKT enables more accurate prediction of students'future performance. Finally, extensive experiments on real-world datasets demonstrate that our method achieves state-of-the-art performance.
Haotian Zhang, Shucun Wang, Jinze Wu et al.· 0 citations
Organisations hold significant knowledge capital that their AI systems cannot access as knowledge. The knowledge bases most organisations maintain — manuals, onboarding documents, process documentation, and accumulated expertise — are structurally incompatible with AI reasoning: there are no dependency structures to traverse, no role classifications to honour, no relational context to surface. This paper introduces meta-books as a knowledge management design response to this structural problem. A meta-book transforms a conventional organisational document into a typed, dependency-linked knowledge graph. Source content is decomposed into typed knowledge objects — concepts, procedures, examples, controversies, and reflective insights — connected by semantically meaningful edges such as prerequisite-of, illustrates, contradicts, and applies-to. Every node carries pedagogical metadata: depth layer, role classification, engagement time, and prerequisite links. Unlike retrieval-augmented generation, meta-books restructure source content itself, making dependency, context, and role visible to AI at the point of reasoning, and support cross-source integration allowing knowledge nodes to migrate across individual, team, and organisational graphs without losing relational context. Meta-books reposition organisational knowledge as a compound, AI-traversable asset with implications across the full employee lifecycle — from recruitment and adaptive onboarding to knowledge retention beyond individual tenure. The paper positions meta-books against learning object metadata, adaptive hypermedia, semantic web standards, and enterprise knowledge graphs, demonstrating that no existing standard simultaneously occupies the intersection of typed knowledge structure, pedagogical metadata, and AI queryability. Five design principles are derived using design science research methodology and evaluated through illustrative decomposition of a representative organisational document. The paper’s most distinctive argument is that meta-books are the first organisational KM framework explicitly aligned with the cognitive architecture of human learning — integrating schema theory, cognitive load theory, dual coding, retrieval practice, the spacing effect, meaningful learning, and connectionist neuroscience — with implications for universities, professional certification, and all structured knowledge delivery systems.
Sima Fatemipour· European Conference on Knowl...· 0 citations
Recent advances in generative AI, particularly large language models and multimodal foundation models, have renewed interest in whether tacit knowledge can be learned or reproduced by machines. While prior studies emphasize the growing ability of generative AI to approximate patterns of human reasoning, judgment, and action, comparatively little attention has been paid to what aspects of tacit knowledge are excluded by design. This paper addresses this gap by asking a conceptual question: which aspects of tacit knowledge are structurally excluded from contemporary generative AI research? Rather than treating tacit knowledge as a single implicit capability, this study reorganizes prior research into three analytical perspectives: mediation, structure, and representation. From this viewpoint, tacit knowledge is sustained by processes that translate practice into communicable forms, by social and cultural structures that stabilize judgment and action, and by representational practices that constitute tacit knowledge as an object of analysis. These perspectives are then used to examine recent developments in generative AI. The analysis shows that current generative AI systems primarily engage with the externalized outcomes of tacit knowledge, such as observable reasoning patterns or action trajectories, while leaving its formative conditions unaddressed. Processes of mediation, social and institutional structures, and reflexive representational practices remain outside model design. These limitations are not merely technical but reflect structural design choices embedded in contemporary AI research. By clarifying these boundaries, this paper provides a conceptual framework for reconsidering the division of roles between human practice and generative AI in future socio-technical systems.
