Pith Number

pith:BLYT3PVF

pith:2026:BLYT3PVFT6W5TJTPKBXRLSOX34

not attested not anchored not stored refs resolved

CLARA: An AI-Augmented Analytics Dashboard for Collaboration Literacy

Bookyung Shin, Chenghong Lin, Dawei Xie, Khalil Anderson, Marcelo Worsley, Tochukwu Eze

CLARA extracts concept maps and seven-dimension assessments from transcripts to create shared representations that improve both user analytics and AI retrieval over text-only baselines.

arxiv:2605.17259 v1 · 2026-05-17 · cs.HC

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\usepackage{pith}
\pithnumber{BLYT3PVFT6W5TJTPKBXRLSOX34}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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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

Evaluation results show that CLARA produces reliable collaboration quality analysis and, owing to the artifacts serving as knowledge infrastructure, improves both retrieval performance and response quality over transcript-only baselines.

C2weakest assumption

That AI models can accurately and consistently extract semantic artifacts such as concept maps and seven-dimension collaboration assessments from transcripts without introducing substantial errors or biases that would undermine the shared representations.

C3one line summary

CLARA generates semantic artifacts like concept maps and collaboration assessments from transcripts to support human analytics and improve AI retrieval and reasoning over transcript-only approaches.

References

24 extracted · 24 resolved · 0 Pith anchors

[1] Perspectives on socially shared cognition pp 1991

[2] Cukurova, M., Zhou, Q., Spikol, D., Landolfi, L.: Modelling collaborative problem- solving competence with transparent learning analytics: is video data enough? In: Proceedings of the tenth internatio 2020

[3] Computers and Education: Artificial Intelligence7, 100299 (2024) 2024

[4] Computers & Education46(1), 6–28 (2006) 2006

[5] Journal of Learning Analytics 12(1), 253–270 (Mar 2025) 2025 · doi:10.18608/jla.2025.8431

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:48.144841Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

0af13dbea59fadd9a66f506f15c9d7df04b216407debe54aee714cce3b6c9683

Aliases

arxiv: 2605.17259 · arxiv_version: 2605.17259v1 · doi: 10.48550/arxiv.2605.17259 · pith_short_12: BLYT3PVFT6W5 · pith_short_16: BLYT3PVFT6W5TJTP · pith_short_8: BLYT3PVF

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/BLYT3PVFT6W5TJTPKBXRLSOX34 \
  | 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: 0af13dbea59fadd9a66f506f15c9d7df04b216407debe54aee714cce3b6c9683

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "f5a2d331695ea51f8d6904efe1ae9850e4f058db5cb4e0ce57c92747efdbabe8",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.HC",
    "submitted_at": "2026-05-17T05:01:40Z",
    "title_canon_sha256": "3db3d4f10bb7064e1d8cf63ade794380d3f4ce8e22deedb8b195504ace56ec50"
  },
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  "source": {
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    "kind": "arxiv",
    "version": 1
  }
}