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Cited by 93Ethics and Social Impacts of AIArtificial Intelligence in EducationArtificial Intelligence in Healthcare and Education

ChatGPT and the Generation of Digitally Born “Knowledge”: How Does a Generative AI Language Model Interpret Cultural Heritage Values?

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Spennemann, Dirk H. R.·2023·Knowledge
DOI 10.3390/knowledge3030032
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Abstract

The public release of ChatGPT, a generative artificial intelligence language model, caused wide-spread public interest in its abilities but also concern about the implications of the application on academia, depending on whether it was deemed benevolent (e.g., supporting analysis and simplification of tasks) or malevolent (e.g., assignment writing and academic misconduct). While ChatGPT has been shown to provide answers of sufficient quality to pass some university exams, its capacity to write essays that require an exploration of value concepts is unknown. This paper presents the results of a study where ChatGPT-4 (released May 2023) was tasked with writing a 1500-word essay to discuss the nature of values used in the assessment of cultural heritage significance. Based on an analysis of 36 iterations, ChatGPT wrote essays of limited length with about 50% of the stipulated word count being primarily descriptive and without any depth or complexity. The concepts, which are often flawed and suffer from inverted logic, are presented in an arbitrary sequence with limited coherence and without any defined line of argument. Given that it is a generative language model, ChatGPT often splits concepts and uses one or more words to develop tangential arguments. While ChatGPT provides references as tasked, many are fictitious, albeit with plausible authors and titles. At present, ChatGPT has the ability to critique its own work but seems unable to incorporate that critique in a meaningful way to improve a previous draft. Setting aside conceptual flaws such as inverted logic, several of the essays could possibly pass as a junior high school assignment but fall short of what would be expected in senior school, let alone at a college or university level.

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  • Regions with limited data richness experience algorithmic poverty at the ecosystem level, where agents perform poorly because they lack the contextual signals required for high-quality prediction.

    “Regions with limited data richness experience what scholars describe as algorithmic poverty at the ecosystem level, where agents perform poorly because they lack the contextual signals required for high-quality prediction (Spennemann, 2023).”

    From Marketing to the Machine: Rethinking Global Marketing for the AI Era (2026) · cites this paper as Spennemann, 2023

    The Conclusions (page unavailable) say that the nature and quantity of material available to ChatGPT determine its knowledge base and responses, and that the choice of training material can introduce bias. The paper does not describe regional data scarcity as “algorithmic poverty” or establish that agents in data-poor regional ecosystems perform poorly because they lack contextual signals for prediction. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • At the representational level, generative systems actively construct category logics and evaluative frames.

    “At the representational level, generative systems actively construct category logics and evaluative frames, amplifying concerns about synthetic content, hallucination, and narrative distortion raised in emerging AI research (Campbell et al., 2022;González-Cantera & Bonacchi, 2025;Spennemann, 2023).”

    From Marketing to the Machine: Rethinking Global Marketing for the AI Era (2026) · cites this paper as Spennemann, 2023

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Cited by

Sorted by citations

Authors
Spennemann, D. H. R.
Affiliations
Charles Sturt University (AU)
Corresponding author
Spennemann, Dirk H. R.
ORCID
Spennemann, Dirk H. R.
Venue
Knowledge
Publisher
MDPI AG
Published
2023-09-18
Volume
3
Issue
3
Pages
480-512
ISSN
2673-9585 (ISSN-L)
DOI
10.3390/knowledge3030032
OpenAlex
W4386849548
Cited by
93
References
21
Citations by year
2026: 15 · 2025: 39 · 2024: 33 · 2023: 6
FWCI
23.29
Type
journal article
Language
en
Open access
Licence
Locations
  • Knowledge (publishedVersion, cc-by)
  • Preprints.org (acceptedVersion, cc-by)
  • Charles Sturt University Research Output (CRO) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
ChatGPT, generative AI, cultural heritage value, AI-generated references, self-criticism, academic integrity, artificial intelligence in higher education

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