Artificial Intelligence and Digital Learning: Architecture, Hallucinations, Information, Findability, The First Rung, and The Arts of Inquiry.
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| Title: | Artificial Intelligence and Digital Learning: Architecture, Hallucinations, Information, Findability, The First Rung, and The Arts of Inquiry. |
|---|---|
| Authors: | Campbell, Gardner1, Dziuban, Charles2, Howlin, Colm, Smith, Mark3 |
| Source: | Online Learning. Jun2026, Vol. 30 Issue 2, p1-30. 30p. |
| Subject Terms: | *Artificial intelligence, *Digital learning, *Career development, *Information retrieval, *Creative ability, *Educational outcomes, Artificial neural networks, Language models |
| Abstract: | The authors address Artificial Intelligence (AI) powered by Large Language Models (LLM) and its relationship to learning in contemporary education. Initially they explain the underlying functionality of AI using transformer architecture, embedding, and tokenization to create language symbolism. Next, they discuss the transformed search concept and how scale-free networks and power law distributions portray information sources dominated by AI hubs that couple and decouple digital learning resources. They contend that Artificial Intelligence will replace bottom- and entry-level jobs by removing a foundational rung of new graduates’ career development. This shift, termed the answer machine, will impact graduates, industry, and education, creating an urgent need to mitigate the risks and leverage the opportunities AI presents. Finally, they consider potential consequences to human creativity, insight, wisdom-related knowledge, and the arts of inquiry in an AI-driven world where we face the danger of losing the ability to think about thinking. Artificial Intelligence can lead to a remarkably improved culture of education. At this time, however, with our seriously limited understanding of its long-term implications, AI presents more questions than answers, so the immediate need is to suggest a context for the best-informed and most important questions to emerge and receive robust consideration at all levels of society. [ABSTRACT FROM AUTHOR] |
| Copyright of Online Learning is the property of Online Learning Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 194761183 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Artificial Intelligence and Digital Learning: Architecture, Hallucinations, Information, Findability, The First Rung, and The Arts of Inquiry. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Campbell%2C+Gardner%22">Campbell, Gardner</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Dziuban%2C+Charles%22">Dziuban, Charles</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Howlin%2C+Colm%22">Howlin, Colm</searchLink><br /><searchLink fieldCode="AR" term="%22Smith%2C+Mark%22">Smith, Mark</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Online+Learning%22">Online Learning</searchLink>. Jun2026, Vol. 30 Issue 2, p1-30. 30p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Digital+learning%22">Digital learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Career+development%22">Career development</searchLink><br />*<searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br />*<searchLink fieldCode="DE" term="%22Creative+ability%22">Creative ability</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+outcomes%22">Educational outcomes</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The authors address Artificial Intelligence (AI) powered by Large Language Models (LLM) and its relationship to learning in contemporary education. Initially they explain the underlying functionality of AI using transformer architecture, embedding, and tokenization to create language symbolism. Next, they discuss the transformed search concept and how scale-free networks and power law distributions portray information sources dominated by AI hubs that couple and decouple digital learning resources. They contend that Artificial Intelligence will replace bottom- and entry-level jobs by removing a foundational rung of new graduates’ career development. This shift, termed the answer machine, will impact graduates, industry, and education, creating an urgent need to mitigate the risks and leverage the opportunities AI presents. Finally, they consider potential consequences to human creativity, insight, wisdom-related knowledge, and the arts of inquiry in an AI-driven world where we face the danger of losing the ability to think about thinking. Artificial Intelligence can lead to a remarkably improved culture of education. At this time, however, with our seriously limited understanding of its long-term implications, AI presents more questions than answers, so the immediate need is to suggest a context for the best-informed and most important questions to emerge and receive robust consideration at all levels of society. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Online Learning is the property of Online Learning Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=194761183 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 1 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Digital learning Type: general – SubjectFull: Career development Type: general – SubjectFull: Information retrieval Type: general – SubjectFull: Creative ability Type: general – SubjectFull: Educational outcomes Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Language models Type: general Titles: – TitleFull: Artificial Intelligence and Digital Learning: Architecture, Hallucinations, Information, Findability, The First Rung, and The Arts of Inquiry. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Campbell, Gardner – PersonEntity: Name: NameFull: Dziuban, Charles – PersonEntity: Name: NameFull: Howlin, Colm – PersonEntity: Name: NameFull: Smith, Mark IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 24725749 Numbering: – Type: volume Value: 30 – Type: issue Value: 2 Titles: – TitleFull: Online Learning Type: main |
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