Theory to Practice: An Assessment Framework for Generative AI.

Recent advancements in generative artificial intelligence (AI) have disrupted assessment practices within the higher education sector. The efficacy of existing assessment approaches is under reexamination with the introduction of generative AI’s ability to generate human-like text. Simultaneously, there are calls to integrate generative AI into assessment design to enhance learning and prepare students for a new era of technology in their careers. This paper proposes a framework to integrate generative AI into formative and summative assessments across Bloom’s levels and Knowledge Dimensions. Its purpose is to illustrate the versatility and intricacy of generative AI’s potential applications grounded in existing learning theory while retaining a focus on authentic assessment. The goal is to support higher education professionals stimulate assessment design concepts featuring generative AI positioned within varying learning complexities.

Authors

Publication Details

Journal
Intersection A Journal at the Intersection of Assessment and Learning
Published
2024-11-04
DOI
https://doi.org/10.61669/001c.124250
Citations
5
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
1.74
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article

Theory to Practice: An Assessment Framework for Generative AI.

Gretchen Meyers, Eric Page, Eve Billings
5 citations
Intersection A Journal at the Intersection of Assessment and Learning
Intelligent Tutoring Systems and Adaptive Learning
1.74
article

Theory to Practice: An Assessment Framework for Generative AI.

Gretchen Meyers, Eric Page, Eve Billings
article en
5 citations

Abstract

Recent advancements in generative artificial intelligence (AI) have disrupted assessment practices within the higher education sector. The efficacy of existing assessment approaches is under reexamination with the introduction of generative AI’s ability to generate human-like text. Simultaneously, there are calls to integrate generative AI into assessment design to enhance learning and prepare students for a new era of technology in their careers. This paper proposes a framework to integrate generative AI into formative and summative assessments across Bloom’s levels and Knowledge Dimensions. Its purpose is to illustrate the versatility and intricacy of generative AI’s potential applications grounded in existing learning theory while retaining a focus on authentic assessment. The goal is to support higher education professionals stimulate assessment design concepts featuring generative AI positioned within varying learning complexities.

Intersection A Journal at the Intersection of Assessment and LearningVol. 5(4)
Quality Education
Openalex Percentile: Top 13%
Intelligent Tutoring Systems and Adaptive Learning
1.74
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Theory to Practice: An Assessment Framework for Generative AI. — Gretchen Meyers, Eric Page, et al. · Intersection A Journal at the Intersection of Assessment and Learning (2024) | TGRS Research Map | TGRS