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Designing experiential learning activities with generative artificial intelligence tools for authentic assessment

体验式学习 生成语法 人工智能 计算机科学 教育技术 数学教育 心理学 认知科学
作者
David Ernesto Salinas-Navarro,Eliseo Luis Vilalta-perdomo,Rosario Michel‐Villarreal,Luis Montesinos
出处
期刊:Interactive Technology and Smart Education [Emerald (MCB UP)]
卷期号:21 (4): 708-734 被引量:8
标识
DOI:10.1108/itse-12-2023-0236
摘要

Purpose This article investigates the application of generative artificial intelligence (GenAI) in experiential learning for authentic assessment in higher education. Recognized for its human-like content generation, GenAI has garnered widespread interest, raising concerns regarding its reliability, ethical considerations and overall impact. The purpose of this study is to explore the transformative capabilities and limitations of GenAI for experiential learning. Design/methodology/approach The study uses “thing ethnography” and “incremental prompting” to delve into the perspectives of ChatGPT 3.5, a prominent GenAI model. Through semi-structured interviews, the research prompts ChatGPT 3.5 on critical aspects such as conceptual clarity, integration of GenAI in educational settings and practical applications within the context of authentic assessment. The design examines GenAI’s potential contributions to reflective thinking, hands-on learning and genuine assessments, emphasizing the importance of responsible use. Findings The findings underscore GenAI’s potential to enhance experiential learning in higher education. Specifically, the research highlights GenAI’s capacity to contribute to reflective thinking, hands-on learning experiences and the facilitation of genuine assessments. Notably, the study emphasizes the significance of responsible use in harnessing the capabilities of GenAI for educational purposes. Originality/value This research showcases the application of GenAI in operations management education, specifically within lean health care. The study offers insights into its capabilities by exploring the practical implications of GenAI in a specific educational domain through thing ethnography and incremental prompting. Additionally, the article proposes future research directions, contributing to the originality of the work and opening avenues for further exploration in the integration of GenAI in education.
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