LearnOpt: Recovering the Latent Cognitive Structure of Standardized Examinations via Knowledge Graphs and Constrained Optimization
arXiv:2606.15349v1 Announce Type: cross Abstract: Standardized examinations are typically treated as uniform syllabus coverage problems. We argue they are better understood as adversarial systems with stable latent cognitive structures diverging systematically from official syllabi. We introduce LearnOpt, which recovers this structure from historical question papers and generates personalized, time-bounded study plans. Applied to nine years of NEET questions (2016-2024, n=1,496), LearnOpt builds an exam knowledge graph from LLM-tagged questions, extracts a five-category latent skill distributi
The proliferation of advanced AI models, particularly LLMs, enables new approaches to analyzing complex data, making this method of disaggregating educational structures feasible and scalable now.
This development allows for a more granular and adaptive understanding of educational systems, potentially disrupting traditional assessment methodologies and creating highly personalized learning pathways.
Standardized tests can now be seen not just as syllabus coverage but as dynamic, adversarial systems with latent cognitive structures, enabling targeted educational interventions and study plan generation.
- · Students
- · Ed-tech companies
- · Educational researchers
- · AI developers
Personalized learning platforms will become significantly more effective at identifying and addressing individual learning gaps.
The design and validity of future standardized examinations will need to adapt to AI systems capable of inferring their latent structures.
This could lead to a broader reconceptualization of educational pedagogy, shifting from broad syllabus coverage to mastery of specific cognitive skills identified by AI.
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Read at arXiv cs.AI