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The proliferation of artificial intelligence (AI) and large language models (LLMs) presents multifaceted challenges to contemporary pedagogical practices, particularly within the domain of English writing teaching. This phenomenon is largely attributable to the capacity of numerous LLM platforms to either assist in or autonomously generate entire passages of text based on user-specified themes. Moreover, scholarly inquiry has identified a critical deficit in the coherence and inherent logical structure of AI-generated discourse, underscoring the imperative for a systematic pedagogical methodology to effectively integrate these technologies into educational contexts and guide students in their utilization. This paper posits that a macro-thinking approach can serve as a valuable pedagogical tool for navigating the complexities of English writing instruction in the AI era. This framework can empower educators to effectively discriminate between AI-generated and human-crafted text, while simultaneously facilitating the provision of constructive feedback. Macro-thinking, a cognitive approach characterized by the human propensity to apprehend the overarching structure of complex phenomena, offers a versatile analytical lens applicable not only to everyday experiences but also to scholarly inquiry and pedagogical practice. By fostering a holistic understanding of textual production, this framework can equip students with the critical thinking skills necessary to discern, evaluate, and engage with both AI and human-generated discourse, ultimately fostering a more nuanced and sophisticated approach to English writing in an increasingly technologically-mediated world.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |