<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">EST</journal-id><journal-title-group><journal-title>Educational Science Theory</journal-title></journal-title-group><issn>2995-4835</issn><eissn>2995-4843</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/EST.2026070035</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>人工智能时代商科教育高阶思维能力培养的理论内涵、现实困境与改革方向</title><url>https://artdesignp.com/journal/EST/4/7/10.61369/EST.2026070035</url><author>段佩佩,王梦梦,刘艳彬</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>4</volume><issue>7</issue><history><date date-type="pub"><published-time>2026-07-20</published-time></date></history><abstract>数字经济与人工智能的深度融合推动商科教育转型，高阶思维已成为新时代商科人才的核心素养。当前AI 与商科教学融合仍处于浅层阶段，难以充分赋能批判性分析、创新决策等能力培养。本文以建构主义、认知负荷、人机协同三大理论为支撑，界定了AI 赋能下商科个性化教学与高阶思维的内涵，梳理出智能诊断、对话辅导、自适应测评、沉浸仿真四大实践路径。研究发现，现阶段商科教育存在隐性素养难以数字化、算法伦理隐患、技术融合不足、评价体系滞后四大困境。据此，本文提出生成式AI 赋能、三元协同教学、多模态评价优化、全周期终身学习四大改革方向，以期为人工智能时代商科高阶思维培养提供参考，助力复合型商业人才培育。</abstract><keywords>人工智能,商科教育,高阶思维,人机协同</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1]Camelia S, Gradinaru C, Surugiu M R. Artificial Intelligence in Business Education: Benefits and Tools[J/OL]. Amfiteatru Economic, 2024, 26: 241.[2]Kyambade M, Nkurunziza G, Namatovu A . Enhancing critical thinking and ethical decision-making through learner-centered strategies in business education[J/OL]. Cogent Education, 2025, 12(1): 2588420.[3]Anthony G, Hunter J, Hunter R. Prospective teachers development of adaptive expertise[J/OL]. Teaching and Teacher Education, 2015, 49: 108-117.[4] 李盼盼, 薛培琼. 人工智能赋能新商科拔尖人才继续教育的内涵、困境与实践[J/OL]. Development Pedagogy(Chinese Version), 2026, 7(2): 95-98.[5] 吴南中, 袁夏梦, 胡振川. 大规模个性化教学的课堂形态寻绎及其构型[J]. 电化教育研究, 2025, 46(10): 80-88.[6]Sweller J. Cognitive load theory and educational technology[J/OL]. Educational Technology Research and Development, 2020, 68(1): 1-16.[7] 王一岩, 郑永和. 智能时代的人机协同学习：价值内涵、表征形态与实践进路[J]. 中国电化教育, 2022(9): 90-97.[8] 涂屹潇. 知识图谱与大语言模型协同驱动的个性化学习推荐：框架构建、应用验证与未来趋势[J]. 信息与电脑, 2025, 37(17): 27-29.[9] 蒋一丹, 冯小燕, 宁欣. 对话教学理论视角下生成式人工智能重塑课堂教学路径研究[J]. 河南科技学院学报, 2026, 46(4): 31-41.[10] 苏光. AI 自适应驱动的高中Python 编程个性化学习系统设计[J]. 信息与电脑, 2025, 37(24): 162-164.[11]Zhao C. AI-assisted assessment in higher education: A systematic review[J/OL]. Journal of Educational Technology and Innovation, 2025, 6(4).[12]Obed Boateng, Bright Boateng. Algorithmic bias in educational systems: Examining the impact of AI-driven decision making in modern education[J/OL]. World Journal of Advanced Research and Reviews, 2025, 25(1): 2012-2017.[13]Maclean K D S, Bayley T. That&amp;rsquo;s Incorrect and Let Me Tell You Why: A Scalable Assessment to Evaluate Higher Order Thinking Skills[J/OL]. INFORMS Transactions on Education, 2024, 25(1): 23-34.[14] 易凯谕, 韩锡斌. 从混合教学到人智协同教学：生成式人工智能技术变革下的教学新形态[J/OL]. 中国远程教育, 2025, 45(4): 85-98.[15] 周进, 叶俊民, 李超. 多模态学习情感计算：动因、框架与建议[J/OL]. 电化教育研究, 2021, 42(7): 26-32.[16] 顾小清, 尹欢欢. &amp;ldquo;师&amp;mdash; 智&amp;mdash; 生&amp;rdquo;三元教学生态中何以坚守育人初心?[J]. 中国远程教育, 2026, 46(4): 27-49.[17] 吴遵民, 蒋贵友. 数字化时代终身学习体系的现实挑战与生态构建[J/OL]. 远程教育杂志, 2022, 40(5): 3-11.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
