Perceptions of AI-Driven Frameworks for English Composition Assessment Strategies: A Comparison of Professional and Student Researchers

In an effort to explore the integration of Artificial Intelligence (AI) in higher education, this project focused on the evaluation of perceptions of AI-related assessment strategies between student researchers and professional researchers. With English composition as a focal point, the AI-driven framework consisted of the policy, guidelines, and rubric that were analyzed through perceptions related to cognition, attitude, and behavior. Through the creation of the Perceptions of AI-Generated Assessments Model, researchers compared and contrasted the insights from these groups and identified trends that served as new approaches to innovation in higher education assessment. Overall, this research project revealed that student researchers were more optimistic yet sensitive toward benefits for their peers, whereas professional researchers were more critical and deeper analyzers related to AI’s impact on learning and its benefit to student researchers.

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Publication Details

Journal
Intersection A Journal at the Intersection of Assessment and Learning
Published
2025-03-06
DOI
https://doi.org/10.61669/001c.131922
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00
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article

Perceptions of AI-Driven Frameworks for English Composition Assessment Strategies: A Comparison of Professional and Student Researchers

Amy J. Heston, Ling Qian, Tatiana Tolson, Madeline M. Heston
Intersection A Journal at the Intersection of Assessment and Learning
Online Learning and Analytics
article

Perceptions of AI-Driven Frameworks for English Composition Assessment Strategies: A Comparison of Professional and Student Researchers

Amy J. Heston, Ling Qian, Tatiana Tolson, Madeline M. Heston
article en

Abstract

In an effort to explore the integration of Artificial Intelligence (AI) in higher education, this project focused on the evaluation of perceptions of AI-related assessment strategies between student researchers and professional researchers. With English composition as a focal point, the AI-driven framework consisted of the policy, guidelines, and rubric that were analyzed through perceptions related to cognition, attitude, and behavior. Through the creation of the Perceptions of AI-Generated Assessments Model, researchers compared and contrasted the insights from these groups and identified trends that served as new approaches to innovation in higher education assessment. Overall, this research project revealed that student researchers were more optimistic yet sensitive toward benefits for their peers, whereas professional researchers were more critical and deeper analyzers related to AI’s impact on learning and its benefit to student researchers.

Intersection A Journal at the Intersection of Assessment and LearningVol. 6(1)
University of Akron (US), Walsh University (US)
Openalex Percentile: Top 96%
Online Learning and Analytics
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