Scholarlink

<p>The accelerating development of Generative Artificial Intelligence (GenAI) is redefining pedagogical practices in higher education by reshaping instructional design, learner engagement, and assessment processes. This study presents a TCCM-based systematic review of GenAI scholarship across STEM disciplines and selected non-technical fields, including business, tourism, education, and social sciences, published between 2023 and 2025. The synthesis reveals pronounced disciplinary imbalances, with STEM domains leading empirical adoption, while humanities and social sciences demonstrate emerging but comparatively limited engagement. The reviewed literature further indicates a growing orientation toward intelligence augmentation, reflected in increasing emphasis on human–AI collaboration and ethically informed pedagogical integration. This study advances a novel contribution by consolidating leading ethical AI perspectives and translating them into institution-level governance pathways that address critical trade-offs in GenAI adoption, including cognitive dependence, academic integrity risks, fairness challenges, and accountability gaps. Finally, the study proposes an integrative institutional framework that positions GenAI as a complementary cognitive resource capable of supporting pedagogically robust and ethically grounded learning ecosystems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

From adoption to augmentation: a TCCM analysis of generative AI in higher education

  • 期刊论文

  • 发表日期：

    2026年4月2日

查看全文
  • Priya Chugh,
  • Vishu Jain

摘要

The accelerating development of Generative Artificial Intelligence (GenAI) is redefining pedagogical practices in higher education by reshaping instructional design, learner engagement, and assessment processes. This study presents a TCCM-based systematic review of GenAI scholarship across STEM disciplines and selected non-technical fields, including business, tourism, education, and social sciences, published between 2023 and 2025. The synthesis reveals pronounced disciplinary imbalances, with STEM domains leading empirical adoption, while humanities and social sciences demonstrate emerging but comparatively limited engagement. The reviewed literature further indicates a growing orientation toward intelligence augmentation, reflected in increasing emphasis on human–AI collaboration and ethically informed pedagogical integration. This study advances a novel contribution by consolidating leading ethical AI perspectives and translating them into institution-level governance pathways that address critical trade-offs in GenAI adoption, including cognitive dependence, academic integrity risks, fairness challenges, and accountability gaps. Finally, the study proposes an integrative institutional framework that positions GenAI as a complementary cognitive resource capable of supporting pedagogically robust and ethically grounded learning ecosystems.