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REVIEW 4 major objections 5 minor 96 references

AGGA: A Dataset of Academic Guidelines for Generative AI and Large Language Models

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The AGGA dataset packages 80 official university guidelines on generative AI and large language models into an openly licensed, multi-format corpus built for NLP research.

desk verdict A useful, openly archived dataset whose central scope claim is undercut by its own table; fixable with a careful re-audit. read the letter →

arxiv 2501.02063 v3 pith:2P2QB2QV submitted 2025-01-03 cs.CL cs.CY

Junfeng Jiao , Saleh Afroogh , Kevin Chen , David Atkinson , Amit Dhurandhar This is my paper · ORCID
classification cs.CLcs.CY
keywords generativeAIlargelanguagemodelsacademicguidelinesNLPbenchmarkrequirementsengineeringtextmininghighereducationpolicydataset
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

AGGA is a curated collection of 80 official university guidelines on the use of generative AI and large language models in academic settings, drawn from institutions across six continents and totaling 188,674 words. The paper's central claim is that this openly licensed, multi-format corpus gives NLP researchers a reproducible foundation for tasks common in requirements engineering, such as synthesizing policy models, detecting ambiguity, categorizing requirements, and assessing document structure. The authors also argue the dataset can be annotated into a benchmark for comparing how different institutions regulate AI use. If the claim holds, AGGA fills a documented gap: fewer than 10 percent of schools and universities have formal AI-use policies, and scattered proprietary documents have made experiments hard to replicate.

What carries the argument

The central object is the AGGA dataset, a structured compilation in which each guideline is tagged by continent, country, university, document name, and page count, with full text stored in DOCX, PDF, and XLSX files. The supporting machinery is a text-processing pipeline that tokenizes, removes stopwords, stems, and lemmatizes the documents, then runs frequency and network analyses to display continent-level keyword patterns. This pipeline demonstrates the dataset's usefulness for text mining and serves as a template for downstream NLP tasks.

What would settle it

Open Table 1 and count how many of the 80 listed sources are issued directly by a university's own central administration, faculty, or department; any substantial number of entries that turn out to be national strategies, ministry reports, or consortium statements would falsify the paper's description of 'academic guidelines collected from official university websites' and would shrink the effective scope of the corpus.

Watch

Extended reading notes

Core claim

The core discovery is the AGGA dataset itself: 80 academic guidelines for generative AI and large language models, collected from official university websites, normalized into a consistent structure, and released in Word, PDF, and Excel formats under a public-domain license. The paper reports that the corpus contains 188,674 words and spans six continents, and it validates the collection through text-mining analyses that show distinct regional keyword emphases. The intended contribution is a shared resource that lets researchers apply NLP methods to academic AI policy, with the dataset structured so that model synthesis, abstraction identification, ambiguity detection, and requirements categorization can be performed and compared across institutions.

Load-bearing premise

The dataset's value depends on the assumption that all 80 documents are truly official academic guidelines issued by universities, not government reports, ministry strategies, or third-party principles.

Editorial extensions

If this is right

  • Researchers can use AGGA as a shared benchmark for ambiguity detection, requirements categorization, and equivalent-requirement identification in AI policy documents.
  • The multi-format release lets one team do close qualitative reading in Word and PDF while another runs quantitative scraping and modeling on the structured table.
  • Because the corpus spans six continents, comparative studies of regional differences in AI governance language become possible with a single accessible dataset.
  • The public-domain license means institutions can reuse or adapt the guidelines, and researchers can redistribute derived annotations without restriction.
  • The planned expansion on an open code repository lets the corpus track newly issued university policies as they appear.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The dataset could be turned into a longitudinal resource: re-crawling the same universities' pages at later dates would reveal how AI-use policies change as the technology and regulatory climate evolve.
  • A user should filter the 80 entries by issuing body before benchmarking; several rows are national strategies, ministry reports, or consortium principles rather than university-issued academic guidelines, so a stricter university-only subset may give different results.
  • The continent-level keyword differences suggest that an NLP model trained on one region's guidelines may transfer less cleanly to another; a cross-validation experiment could test that directly.
  • Pairing AGGA with a comparable corpus of industry or government AI guidelines would allow direct comparison of how academic and non-academic sectors frame responsible AI use.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces AGGA, a dataset claimed to contain 80 academic guidelines for the use of Generative AIs and Large Language Models, collected from official university websites across six continents. It describes the collection and exclusion criteria, the dataset's file formats (DOCX, PDF, XLSX) on Harvard Dataverse under a CC0 license, and two descriptive analyses: continental keyword-frequency charts and a keyword-continent network graph. The authors position the dataset as a resource for NLP and requirements-engineering tasks such as ambiguity detection, categorization, and structure assessment, and they provide usage notes and a GitHub repository with processing code.

Significance. If the dataset matched its description, AGGA would be a useful open resource for studying institutional AI-use policies, with the practical merit of being openly licensed, multi-format, and geographically broad. The authors also provide a data DOI and a code repository, which are appropriate for a dataset paper. However, the central value of the corpus depends on each item being an authentic, university-issued academic guideline, and the manuscript's own table contradicts that premise: several rows are national strategies, government reports, multi-institution principles, or non-guideline web pages. Since the word-frequency and network analyses are computed over this mixed collection, the descriptive conclusions inherit the composition error. The dataset is potentially salvageable through a systematic re-audit and re-release, but the current version overstates its scope.

major comments (4)
  1. [Supplementary Table 1] Rows 30, 36, 37, 58, 59, and 80 are not university-issued academic guidelines: they are a U.S. Department of Education report, Peru's national AI strategy, Chile's Ministry of Science guidelines, China's MOST research-conduct guidelines, the Australian Framework for Generative AI in Schools, and the Russell Group principles. Row 10 is also not a university but the African Observatory on Responsible AI. These entries directly contradict the abstract and Methods, which define AGGA as '80 academic guidelines' collected from 'official university websites' and describe excluding universities without official guidelines. The '80' count is therefore misleading, and all quantitative descriptions of the corpus (188,674 words, per-continent statistics) are computed over an out-of-scope set. A complete re-audit is needed, with either removal of non-conforming items or a revised, explicit statement of broader inclusion criteria, followed by recomputation of corpus statistics.
  2. [Supplementary Table 1, rows 32, 42, 43, 47, 73, 28] Several entries are not academic guidelines at all: row 32 is guidance for ChatGPT use in the justice sector, row 42 is an editorial policy of a university press, row 43 is a report on judicial-sector readiness in Latin America, row 47 is a Tsinghua forum page, row 73 is a research-group page on artificial intelligence at the University of Padua, and row 28 is the Montreal Declaration on Responsible AI rather than a University of Montreal guideline. Labeling these documents as 'academic guidelines' overstates the dataset's scope and can distort the continental keyword profiles, such as the 'justice' emphasis reported for South America. The authors should either remove such non-guideline entries or clearly re-scope the dataset's title and description.
  3. [Supplementary Table 1, rows 51, 53, 54; references [10], [60], [86]] Provenance errors undermine the claim of meticulous collection from official websites. Row 51 is labeled University of Hong Kong but links to a CUHK document; row 54 lists the same CUHK guide under The Chinese University of Hong Kong, making a duplicate or misattribution; row 53 (National Taiwan University) reuses reference [60], which points to National Tsing Hua University; reference [10] gives 'https://chat.openai.com/chat' rather than the Cairo University policy document; and reference [86] is a local file path on the author's computer. Each row should be verified against the named institution's official web presence, and every reference should be replaced with a stable, publicly resolvable URL or DOI.
  4. [Technical Validation, Figures 2 and 3] The keyword-frequency and network analyses are descriptive summaries of the dataset as currently composed. Because the dataset includes government documents, multi-institution frameworks, and non-guideline web pages, the reported continental themes (e.g., 'justice' for South America, 'frameworks' for Oceania) cannot be attributed to university academic guidelines. If the dataset composition is corrected, these analyses and visualizations must be regenerated on the verified subset; as presented, they do not validate the inclusiveness of a corpus of university guidelines specifically.
minor comments (5)
  1. [Supplementary Table 1, rows 22, 46, 48, 51] There are several typos and naming errors that should be fixed: row 22 'Gudelines' should be 'Guidelines'; row 46 'Weseda' and 'Arificial' should be 'Waseda' and 'Artificial'; row 48 'SUM' should be 'SMU'; and row 51 'Nayang' and 'NUT' should be 'Nanyang' and 'NTU'.
  2. [Figure 1 and Data Records] Figure 1 lists 'Applying an XML Schema (XSD) to standardize the documents' as a processing stage, but the Data Records section describes only DOCX, PDF, and XLSX files and never mentions an XML instance or schema. Clarify whether an XML version exists or remove that step from the workflow diagram.
  3. [Data Records, licensing sentence] The text says the dataset is licensed under CC0 1.0 'provided proper credit is given,' which is inconsistent with CC0, as CC0 does not require attribution. Rephrase to say that attribution is appreciated but not required, or state the actual license terms precisely.
  4. [Table 1 caption] The caption 'Analytics Workflow of the analysis of GAI/LLM guidelines' does not match the table's content, which lists the 80 guideline entries rather than an analytics workflow. Rename the caption to describe the dataset inventory or the selection results.
  5. [Supplementary Table 1, rows 35 and 46] Institution names are inconsistently capitalized and formatted (e.g., 'Universidad de san andres' and 'pontificia universidad católica de chile'). Standardize institution names and country spellings to avoid ambiguity in downstream use of the Excel file.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the dataset is self-contained, and the descriptive text analyses are summaries of the data, not predictions.

full rationale

AGGA is presented as a dataset artifact, not as a derived quantity. The paper contains no equations that map inputs to outputs, and the word-frequency and network analyses are descriptive summaries computed from the dataset itself rather than predictions of external facts. The 80-document collection is documented item-by-item in Supplementary Table 1 and archived independently at Harvard Dataverse (doi:10.7910/DVN/XZZHA5), providing external, checkable evidence of the dataset's content. References [4] and [5] are companion papers by overlapping authors, but they are cited only as general context about the global guideline landscape and are not used to justify AGGA's composition, to exclude alternative sources, or to generate any of the reported results; they are therefore not load-bearing. The paper's own validation section describes its analyses as a 'descriptive overview,' so there is no claim that these analyses independently confirm the dataset. One noted limitation, that some Supplementary Table 1 entries (e.g., rows 30, 36, 37, 58, 59, 80) are national or multi-institution documents rather than university-specific guidelines, is a scope and composition concern about the dataset's stated inclusion criteria, not a circularity issue, because no prediction is being derived from those entries. Accordingly, no circular step can be exhibited with quotation, and the appropriate score is low.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No numerical parameters are fitted; the only hand-tuned artifact is the added stopword list used in frequency analysis, which affects descriptive keyword rankings but not the dataset content.

assumptions (3)
  • domain assumption Official university websites are authoritative sources for institutional AI guidelines.
    Stated in Methods: data collected from official university websites; a central assumption for dataset authenticity.
  • domain assumption The selected 80 documents are representative of academic guidelines across six continents.
    The paper claims diverse global representation, but selection is non-random and any claim of representativeness depends on this assumption.
  • domain assumption NLTK stopwords plus the added list adequately remove non-semantic words for frequency analysis.
    Descriptive keyword findings depend on preprocessing choices, including the manually added stopword list.

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Cite this review

Pith. "Pith review of AGGA: A Dataset of Academic Guidelines for Generative AI and Large Language Models." pith.science (2026). https://pith.science/paper/2P2QB2QV

@misc{pith2026250102063,
  author       = {Pith},
  title        = {Pith review of: AGGA: A Dataset of Academic Guidelines for Generative AI and Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2P2QB2QV}},
  note         = {Machine review of arXiv:2501.02063}
}
read the original abstract

This study introduces AGGA, a dataset comprising 80 academic guidelines for the use of Generative AIs (GAIs) and Large Language Models (LLMs) in academic settings, meticulously collected from official university websites. The dataset contains 188,674 words and serves as a valuable resource for natural language processing tasks commonly applied in requirements engineering, such as model synthesis, abstraction identification, and document structure assessment. Additionally, AGGA can be further annotated to function as a benchmark for various tasks, including ambiguity detection, requirements categorization, and the identification of equivalent requirements. Our methodologically rigorous approach ensured a thorough examination, with a selection of universities that represent a diverse range of global institutions, including top-ranked universities across six continents. The dataset captures perspectives from a variety of academic fields, including humanities, technology, and both public and private institutions, offering a broad spectrum of insights into the integration of GAIs and LLMs in academia.

Figures

Figures reproduced from arXiv: 2501.02063 by the authors.

Figure 1
Figure 1. Schematic Overview of the AGGA Study Design: Data Collection, Structuring, Analysis, and Utilization [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

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Reference graph

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.