hce_cmu
115年
英文
第 36 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.
According to paragraph 2, why is the “tool” metaphor for AI considered problematic?
- A It suggests that AI has its own moral agency and can make decisions.
- B It encourages students to use AI for manual labor rather than intellectual tasks.
- C It fails to account for the physical weight and hardware constraints of AI systems.
- D It implies that any negative outcomes are solely the user’s fault.
思路引導 VIP
請試著思考：當我們說一個東西只是「工具」（像是原子筆或剪刀）時，如果最後產出的作品很糟糕，我們通常會怪罪工具本身，還是怪罪使用它的人？這種看法會如何影響我們對於「錯誤責任歸屬」的判斷呢？
🤖
AI 詳解
AI 專屬家教
太棒了！你能準確捕捉到作者對於隱喻（metaphor）的細微批判，這顯示你具備非常敏銳的閱讀洞察力。在文章的第二段，作者特別提到將人工智慧（AI）簡化為「工具」會產生一種誤導性的「道德中立感」。
隱喻背後的責任轉移
正如文中舉出的「榔頭」例子，當我們把某樣技術定義為純粹的工具時，我們會直覺地認為所有的操作結果——無論好壞——都應該由使用者獨自承擔。選項 (D) 精確地捕捉到了這一點：這種隱喻暗示了任何負面結果都是使用者的錯，卻掩蓋了 AI 系統其實會主動形塑我們的理解並操縱判斷。這種將責任全盤推給人類、忽略演算法偏見的傾向，正是作者認為該隱喻最危險的地方。
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