The Two-Lane Road to Hell Is Paved with Good Intentions: Why an All-or-None Approach to Generative AI, Integrity, and Assessment Is Insupportable
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| Title: | The Two-Lane Road to Hell Is Paved with Good Intentions: Why an All-or-None Approach to Generative AI, Integrity, and Assessment Is Insupportable |
|---|---|
| Language: | English |
| Authors: | Guy J. Curtis (ORCID |
| Source: | Higher Education Research and Development. 2025 44(8):2151-2158. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 8 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Evaluative |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Technology Uses in Education, Integrity, Higher Education, Educational Assessment, Cheating, Academic Achievement |
| DOI: | 10.1080/07294360.2025.2476516 |
| ISSN: | 0729-4360 1469-8366 |
| Abstract: | A 'two-lane' (All-or-None) approach to the use of generative artificial intelligence (genAI) is the idea that there should be two categories of assessments in higher education--Lane 1/None: where the use of genAI is prohibited, and Lane 2/All: where "any" use of genAI is permitted. This idea has been thoughtfully detailed and continues to be debated. Although this idea is generally well-intentioned, in this comment piece I argue that, if implemented, it will promote an impoverished approach to education and educational assessment. One argument often invoked in favour of an All-or-None approach is that genAI use may sometimes be undetectable. Contract cheating (e.g., students outsourcing assessments to ghostwriters) is sometimes undetectable, yet an argument that there should be an All-or-None approach permitting contract cheating in some assessments is clearly absurd. An All-or-None approach to genAI and assessment is also absurd. A middle lane, where genAI use in assessments is allowed with some limitations, is essential. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1503562 |
| Database: | ERIC |
| FullText | Text: Availability: 0 |
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| Header | DbId: eric DbLabel: ERIC An: EJ1503562 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: The Two-Lane Road to Hell Is Paved with Good Intentions: Why an All-or-None Approach to Generative AI, Integrity, and Assessment Is Insupportable – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Guy+J%2E+Curtis%22">Guy J. Curtis</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4174-6955">0000-0002-4174-6955</externalLink>) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Higher+Education+Research+and+Development%22"><i>Higher Education Research and Development</i></searchLink>. 2025 44(8):2151-2158. – Name: Avail Label: Availability Group: Avail Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 8 – Name: DatePubCY Label: Publication Date Group: Date Data: 2025 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Evaluative – Name: Audience Label: Education Level Group: Audnce Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink> – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Integrity%22">Integrity</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Assessment%22">Educational Assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Cheating%22">Cheating</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1080/07294360.2025.2476516 – Name: ISSN Label: ISSN Group: ISSN Data: 0729-4360<br />1469-8366 – Name: Abstract Label: Abstract Group: Ab Data: A 'two-lane' (All-or-None) approach to the use of generative artificial intelligence (genAI) is the idea that there should be two categories of assessments in higher education--Lane 1/None: where the use of genAI is prohibited, and Lane 2/All: where "any" use of genAI is permitted. This idea has been thoughtfully detailed and continues to be debated. Although this idea is generally well-intentioned, in this comment piece I argue that, if implemented, it will promote an impoverished approach to education and educational assessment. One argument often invoked in favour of an All-or-None approach is that genAI use may sometimes be undetectable. Contract cheating (e.g., students outsourcing assessments to ghostwriters) is sometimes undetectable, yet an argument that there should be an All-or-None approach permitting contract cheating in some assessments is clearly absurd. An All-or-None approach to genAI and assessment is also absurd. A middle lane, where genAI use in assessments is allowed with some limitations, is essential. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2026 – Name: AN Label: Accession Number Group: ID Data: EJ1503562 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1503562 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/07294360.2025.2476516 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 2151 Subjects: – SubjectFull: Artificial Intelligence Type: general – SubjectFull: Technology Uses in Education Type: general – SubjectFull: Integrity Type: general – SubjectFull: Higher Education Type: general – SubjectFull: Educational Assessment Type: general – SubjectFull: Cheating Type: general – SubjectFull: Academic Achievement Type: general Titles: – TitleFull: The Two-Lane Road to Hell Is Paved with Good Intentions: Why an All-or-None Approach to Generative AI, Integrity, and Assessment Is Insupportable Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Guy J. Curtis IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 0729-4360 – Type: issn-electronic Value: 1469-8366 Numbering: – Type: volume Value: 44 – Type: issue Value: 8 Titles: – TitleFull: Higher Education Research and Development Type: main |
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