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<p>This article presents an integrative literature review and a Bloom-informed model of human–generative-AI interaction in university economics education. Using the revised Bloom’s taxonomy as an analytical lens, the study examines how interaction with large language models redistributes learning activities across cognitive levels. Empirical and conceptual sources on generative AI in higher education (2016–2025) from international and Russian databases and policy reports were analysed using qualitative content analysis. At lower cognitive levels, generative AI mainly acts as an adaptive reference and tutoring environment, improving access to explanations and practice but encouraging superficial learning when detached from primary sources and fact-checking. At the apply level, effects depend on assessment design: where process evidence and reflection on AI use are required, generative tools scaffold procedural skills; where only final products are graded, they tend to substitute rather than support learning. At higher-order levels, AI broadens opportunities for analysis, evaluation and co-creation, yet increases risks of over-standardised reasoning, erosion of originality and opaque authorship. The article proposes “Bloom’s taxonomy with generative technologies”, a three-dimensional matrix linking cognitive levels, pedagogically acceptable AI functions and meta-competences (AI literacy, self-regulated learning, digital responsibility), and derives programme- and course-level implications for AI-rich economics curricula.</p>

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Human–GenAI interaction across Bloom’s levels in university economics education

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月20日

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  • Vera Makarova,
  • Elizaveta Ogloblina

摘要

This article presents an integrative literature review and a Bloom-informed model of human–generative-AI interaction in university economics education. Using the revised Bloom’s taxonomy as an analytical lens, the study examines how interaction with large language models redistributes learning activities across cognitive levels. Empirical and conceptual sources on generative AI in higher education (2016–2025) from international and Russian databases and policy reports were analysed using qualitative content analysis. At lower cognitive levels, generative AI mainly acts as an adaptive reference and tutoring environment, improving access to explanations and practice but encouraging superficial learning when detached from primary sources and fact-checking. At the apply level, effects depend on assessment design: where process evidence and reflection on AI use are required, generative tools scaffold procedural skills; where only final products are graded, they tend to substitute rather than support learning. At higher-order levels, AI broadens opportunities for analysis, evaluation and co-creation, yet increases risks of over-standardised reasoning, erosion of originality and opaque authorship. The article proposes “Bloom’s taxonomy with generative technologies”, a three-dimensional matrix linking cognitive levels, pedagogically acceptable AI functions and meta-competences (AI literacy, self-regulated learning, digital responsibility), and derives programme- and course-level implications for AI-rich economics curricula.