hce_cmu
115年
英文
第 40 題
📖 題組：
In contemporary higher education, the discourse surrounding generative artificial intelligence (AI) has largely coalesced around a set of comforting metaphors. University policies and academic handbooks frequently characterize AI as a “tool,” a “tutor,” or a “helpful assistant.” While these descriptors aim to provide pragmatic clarity, they are far from neutral. Instead, these metaphors exert a profound, often invisible, influence on how educators and students conceptualize the technology’s role in the cultivation of critical thinking. The cognitive power of metaphors is well-documented. A landmark study by Stanford psychologists Paul Thibodeau and Lera Boroditsky demonstrated that framing crime as either a “beast” or a “plague” significantly altered the solutions participants proposed—steering them toward either punitive measures or social reform, respectively. Crucially, participants remained largely unaware of the metaphor’s influence, instead attributing their decisions to raw statistics. Similarly, viewing AI as a “tool” implies a degree of moral neutrality and human agency; much like a hammer, the responsibility for its effects is placed squarely on the user. This perspective, however, obscures the ways in which AI systems actively shape interpretations and manipulate judgment, potentially undermining the intellectual habits academics seek to foster. The “assistant” metaphor further complicates the pedagogical landscape by suggesting a clear hierarchy where the human remains in control. This narrative masks the emergence of a “second hidden curriculum,” wherein AI does not merely assist but actively directs learning by structuring explanations and modeling specific cognitive pathways. When AI is anthropomorphized—attributed human qualities like “going rogue” or being “racist”—it further dilutes accountability. Such language allows users to look away from the institutional and corporate biases embedded within the algorithms, leading to a drift in moral responsibility. To counteract these cognitive distortions, some scholars suggest a transition from convenient metaphors to disciplined, technical vocabulary. Rather than “brainstorming with AI,” users should recognize the process as “engaging in probabilistic text generation” under specific algorithmic constraints. Shifting from the concept of “hallucinations” to “predictive text failure” transforms verification from an optional task into a fundamental academic practice. Ultimately, by stripping away misleading metaphors and insisting on technical precision, the academic community can better maintain moral responsibility and pedagogical integrity, ensuring that the development of thought remains a distinctly human endeavor.
In contemporary higher education, the discourse surrounding generative artificial intelligence (AI) has largely coalesced around a set of comforting metaphors. University policies and academic handbooks frequently characterize AI as a “tool,” a “tutor,” or a “helpful assistant.” While these descriptors aim to provide pragmatic clarity, they are far from neutral. Instead, these metaphors exert a profound, often invisible, influence on how educators and students conceptualize the technology’s role in the cultivation of critical thinking. The cognitive power of metaphors is well-documented. A landmark study by Stanford psychologists Paul Thibodeau and Lera Boroditsky demonstrated that framing crime as either a “beast” or a “plague” significantly altered the solutions participants proposed—steering them toward either punitive measures or social reform, respectively. Crucially, participants remained largely unaware of the metaphor’s influence, instead attributing their decisions to raw statistics. Similarly, viewing AI as a “tool” implies a degree of moral neutrality and human agency; much like a hammer, the responsibility for its effects is placed squarely on the user. This perspective, however, obscures the ways in which AI systems actively shape interpretations and manipulate judgment, potentially undermining the intellectual habits academics seek to foster. The “assistant” metaphor further complicates the pedagogical landscape by suggesting a clear hierarchy where the human remains in control. This narrative masks the emergence of a “second hidden curriculum,” wherein AI does not merely assist but actively directs learning by structuring explanations and modeling specific cognitive pathways. When AI is anthropomorphized—attributed human qualities like “going rogue” or being “racist”—it further dilutes accountability. Such language allows users to look away from the institutional and corporate biases embedded within the algorithms, leading to a drift in moral responsibility. To counteract these cognitive distortions, some scholars suggest a transition from convenient metaphors to disciplined, technical vocabulary. Rather than “brainstorming with AI,” users should recognize the process as “engaging in probabilistic text generation” under specific algorithmic constraints. Shifting from the concept of “hallucinations” to “predictive text failure” transforms verification from an optional task into a fundamental academic practice. Ultimately, by stripping away misleading metaphors and insisting on technical precision, the academic community can better maintain moral responsibility and pedagogical integrity, ensuring that the development of thought remains a distinctly human endeavor.
In paragraph 4, the author suggests that shifting to technical language like “predictive text failure” would _____.
- A make the technology seem more human and approachable
- B highlight the user’s role in interpreting and evaluating the output
- C eliminate the need for students to learn experimental language design
- D allow users to stop worrying about algorithmic constraints
思路引導 VIP
請觀察第四段中，作者提到將「幻覺」這類隱喻替換為「預測失敗」等技術術語後，對於我們「驗證資訊（verification）」的態度會產生什麼樣的轉變？這項轉變如何影響我們對 AI 產出內容的信任程度？
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AI 詳解
AI 專屬家教
同學做得太棒了！你能精準選出 (B) 選項，代表你已經完全掌握了作者在文章末段想傳達的核心思想。這題考驗的是對「因果關係」與「用語轉變」背後深意的主動推論能力。
技術語言與人類責任的連結
在文章第四段中，作者強調透過使用「預測文本失敗」（predictive text failure）等精確的技術詞彙，能將驗證過程從「可有可無的任務」轉變為「核心學術實踐」。當我們撥開擬人化的迷霧，改用中立的技術詞彙時，AI 的產出就不再具有「意識」或「主動性」，而是回歸到機率運算的本質。這種語境的切換，實際上是為了將解釋與評估產出結果的重擔，重新交回人類手中，確保學術判斷保持其人類主體性。
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