Pith Number

pith:OOLLWAIX

pith:2026:OOLLWAIXFIMRGKAFNT43MKVVDK

not attested not anchored not stored refs resolved

Expert Cognition Dashboard: From Learning Analytics to Cognition Intelligence in AI-Driven Education

Annie Yuan

Expert Cognition Dashboards let AI systems interpret learner behaviors through expert-like cognitive structures rather than raw data.

arxiv:2605.17263 v1 · 2026-05-17 · cs.HC

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\pithnumber{OOLLWAIXFIMRGKAFNT43MKVVDK}

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

ECD models expert cognition within dashboard systems, enabling learner behaviours to be interpreted through expert-like cognitive structures rather than treated as raw behavioural signals.

C2weakest assumption

That expert cognition (including interpretation, misconception patterns, and learning tension) can be effectively captured and operationalized through the proposed three-layer dashboard architecture to support AI-driven adaptive interventions.

C3one line summary

Introduces the Expert Cognition Dashboard framework that organizes learner data into multi-level cognitive structures for AI Twin-driven personalized education.

References

54 extracted · 54 resolved · 0 Pith anchors

[1] Learning and individual differences , volume= 2023

[2] A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 , author=. Complexity , volume=. 2021 , publisher= 2010

[3] Proceedings of the 2nd international conference on learning analytics and knowledge , pages=

[4] International journal of technology enhanced learning , volume= 2012

[5] Let’s not forget: Learning analytics are about learning , author=. TechTrends , volume=. 2015 , publisher= 2015

Formal links

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Receipt and verification

First computed	2026-05-20T00:03:48.548245Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7396bb01172a191328056cf9b62ab51a804654ff5f32bcbc634300b8ee682831

Aliases

arxiv: 2605.17263 · arxiv_version: 2605.17263v1 · doi: 10.48550/arxiv.2605.17263 · pith_short_12: OOLLWAIXFIMR · pith_short_16: OOLLWAIXFIMRGKAF · pith_short_8: OOLLWAIX

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/OOLLWAIXFIMRGKAFNT43MKVVDK \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 7396bb01172a191328056cf9b62ab51a804654ff5f32bcbc634300b8ee682831

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "15a9b040b6e1bfce0aea173097923b88b5cec4767ae26d05cc4365bd93e956f1",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.HC",
    "submitted_at": "2026-05-17T05:11:31Z",
    "title_canon_sha256": "a43e1389c7ee6d25af2cef4ab7e69080a80ddfbcdd378a64118dd33f7b8e24fe"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.17263",
    "kind": "arxiv",
    "version": 1
  }
}