NotesFAQContact Us
Collection
Advanced
Search Tips
Back to results
Peer reviewed Peer reviewed
PDF on ERIC Download full text
ERIC Number: EJ1461880
Record Type: Journal
Publication Date: 2024
Pages: 13
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: EISSN-2688-7207
Available Date: 0000-00-00
Theory to Practice: A Framework for Generative AI
Eric Page; Gretchen Meyers; Eve Krahe Billings
Intersection: A Journal at the Intersection of Assessment and Learning, v5 n4 p114-126 2024
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.
Association for the Assessment of Learning in Higher Education. 6844 Bardstown Road #910, Louisville, KY 40291. Tel: 502-406-8012; e-mail: info@aalhe.org; Web site: https://www.aalhe.org/intersection
Publication Type: Journal Articles; Reports - Evaluative
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A