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Assessing the Potential Challenges of Paid LLMs and Inequities: Assessments and Academic Integrity in Higher Education

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Title: Assessing the Potential Challenges of Paid LLMs and Inequities: Assessments and Academic Integrity in Higher Education
Language: English
Authors: Aditi Jhaveri
Source: Journal of the Scholarship of Teaching and Learning. 2025 25(3).
Availability: Indiana University. 107 South Indiana Avenue, Bryan Hall 203B, Bloomington, IN 47405. Tel: 317-274-5647; Fax: 317-278-2360; e-mail: josotl@iu.edu; Web site: https://scholarworks.iu.edu/journals/index.php/josotl
Peer Reviewed: Y
Page Count: 14
Publication Date: 2025
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Natural Language Processing, Artificial Intelligence, Technology Uses in Education, Equal Education, Evaluation Methods, Integrity, Resilience (Psychology), Writing Assignments, Writing Evaluation, Design
ISSN: 1527-9316
Abstract: This essay examines the potential impact of paid-for or premium language models, where some students may be able to afford advanced models generating superior outputs while others could face inequities due to financial constraints. It explores how this dynamic can exacerbate the digital divide, challenge traditional as well as more recent assessment methods, and discusses strategies that hold promise in navigating these complexities in the era of Generative AI within the context of higher education. The essay concludes by proposing six guiding principles for designing more AI- resilient assessments. These include: designing novel and timely questions, mirroring real-world tasks and challenges in assessments, assessing students' collaboration and communication skills as well as their adaptability and resilience, assigning tasks that test their creativity and originality, and requiring teachers to regularly update and diversify assessments.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1487508
Database: ERIC
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  Data: Assessing the Potential Challenges of Paid LLMs and Inequities: Assessments and Academic Integrity in Higher Education
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  Data: Indiana University. 107 South Indiana Avenue, Bryan Hall 203B, Bloomington, IN 47405. Tel: 317-274-5647; Fax: 317-278-2360; e-mail: josotl@iu.edu; Web site: https://scholarworks.iu.edu/journals/index.php/josotl
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  Data: This essay examines the potential impact of paid-for or premium language models, where some students may be able to afford advanced models generating superior outputs while others could face inequities due to financial constraints. It explores how this dynamic can exacerbate the digital divide, challenge traditional as well as more recent assessment methods, and discusses strategies that hold promise in navigating these complexities in the era of Generative AI within the context of higher education. The essay concludes by proposing six guiding principles for designing more AI- resilient assessments. These include: designing novel and timely questions, mirroring real-world tasks and challenges in assessments, assessing students' collaboration and communication skills as well as their adaptability and resilience, assigning tasks that test their creativity and originality, and requiring teachers to regularly update and diversify assessments.
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
    Subjects:
      – SubjectFull: Natural Language Processing
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Equal Education
        Type: general
      – SubjectFull: Evaluation Methods
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      – SubjectFull: Integrity
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      – SubjectFull: Resilience (Psychology)
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      – SubjectFull: Writing Assignments
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      – SubjectFull: Writing Evaluation
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      – SubjectFull: Design
        Type: general
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      – TitleFull: Assessing the Potential Challenges of Paid LLMs and Inequities: Assessments and Academic Integrity in Higher Education
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              Type: published
              Y: 2025
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