在商业研究教育中以人为中心使用生成式人工智能,促进社会情感学习:内部案例研究

Patrick Aure, Oriana Cuenca
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引用次数: 0

摘要

目的这项探索性研究在以人为本的人工智能、社会情感学习和真实性原则的指导下,通过整合生成式人工智能(GAI)工具,对本科生商业研究课程的教学方法进行了创新。研究结果学生利用 GAI 工具作为头脑风暴伙伴、共同写作者和共同阅读者,提高了研究效率和理解能力。他们在参与 GAI 的过程中表现出了真实性和以人为本的人工智能原则。研究局限/影响未来的研究可以探索团队如何与 GAI 工具进行集体互动。社会影响围绕 GAI 的使用,有必要对社会认知和情感反应进行公开讨论。教育工作者可以营造一个培养学生全面发展的学习环境,让他们为迎接技术挑战做好准备,同时保护人类的学习和成长。原创性/价值本研究填补了探索人工智能融入本科教育的实施和成果方面的空白,优先考虑学生的观点,而不是普遍关注的教育工作者的观点。此外,本研究还探讨了人工智能在本科生研究中的教学和应用,有别于当前主要关注学术研究应用的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fostering social-emotional learning through human-centered use of generative AI in business research education: an insider case study
PurposeThis exploratory study innovates the pedagogy of undergraduate business research courses by integrating Generative Artificial Intelligence (GAI) tools, guided by human-centered artificial intelligence, social-emotional learning, and authenticity principles.Design/methodology/approachAn insider case study approach was employed to examine an undergraduate business research course where 72 students utilized GAI for coursework. Thematic analysis was applied to their meta-reflective journals.FindingsStudents leverage GAI tools as brainstorming partners, co-writers, and co-readers, enhancing research efficiency and comprehension. They exhibit authenticity and human-centered AI principles in their GAI engagement. GAI integration imparts relevant AI skills to students.Research limitations/implicationsFuture research could explore how teams collectively interact with GAI tools.Practical implicationsIncorporating meta-reflections can promote responsible GAI usage and develop students' self-awareness, critical thinking, and ethical engagement.Social implicationsOpen discussions about social perceptions and emotional responses surrounding GAI use are necessary. Educators can foster a learning environment that nurtures students' holistic development, preparing them for technological challenges while preserving human learning and growth.Originality/valueThis study fills a gap in exploring the delivery and outcomes of AI-integrated undergraduate education, prioritizing student perspectives over the prevalent focus on educators' viewpoints. Additionally, it examines the teaching and application of AI for undergraduate research, diverging from current studies that primarily focus on research applications for academics.
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