AI Academic Integrity Gap Vocabulary

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Flashcards defining key terms and perspectives regarding the integration of AI in academic environments and its impact on integrity and learning.

Last updated 5:01 PM on 7/10/26
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12 Terms

1
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The Protective/Policing Perspective

A view focused on the risks AI poses to original thought, emphasizing potential academic dishonesty, "corner-cutting," and limitations like hallucinations and bias.

<p>A view focused on the risks AI poses to original thought, emphasizing potential academic dishonesty, "corner-cutting," and limitations like hallucinations and bias.</p>
2
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Hallucinations

A phenomenon in AI where the system produces confident but false information.

<p>A phenomenon in AI where the system produces confident but false information.</p>
3
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The Integrative/Partnering Perspective

An approach that views AI as a powerful "24/7 tutor" or a "starting point" for creativity, advocating for collaboration between the student and technology.

<p>An approach that views AI as a powerful "24/7 tutor" or a "starting point" for creativity, advocating for collaboration between the student and technology.</p>
4
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Human-in-the-loop

A model where AI handles tasks like idea generation or scaffolding, while the student remains responsible for critical evaluation and final decision-making.

<p>A model where AI handles tasks like idea generation or scaffolding, while the student remains responsible for critical evaluation and final decision-making.</p>
5
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The Human-Centric Perspective

A perspective asserting that education should double down on skills AI cannot replicate, such as empathy, ethical judgment, and social-emotional intelligence.

<p>A perspective asserting that education should double down on skills AI cannot replicate, such as empathy, ethical judgment, and social-emotional intelligence.</p>
6
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The Critical Evaluation Gap

The lack of training for students on how to fact-check, verify information, and identify biases or errors in AI-generated answers.

<p>The lack of training for students on how to fact-check, verify information, and identify biases or errors in AI-generated answers.</p>
7
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Productive Struggle

The "heavy lifting" of the mind required for long-term neural development and a deep understanding of a subject.

<p>The "heavy lifting" of the mind required for long-term neural development and a deep understanding of a subject.</p>
8
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The "Productive Struggle" Gap

A gap created when AI solutions provide a shortcut that bypasses the mental effort necessary for deep learning.

<p>A gap created when AI solutions provide a shortcut that bypasses the mental effort necessary for deep learning.</p>
9
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The Pedagogical Framework Gap

The disconnect between the technical availability of AI and the existence of formal, consistent institutional guidelines for its responsible use.

<p>The disconnect between the technical availability of AI and the existence of formal, consistent institutional guidelines for its responsible use.</p>
10
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Prompt Engineering

The ability to ask the "right questions" to an AI system, identified as a critical evolving skill for the workforce.

<p>The ability to ask the "right questions" to an AI system, identified as a critical evolving skill for the workforce.</p>
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Critical Consumer

A student's role in the AI era, characterized by actively evaluating AI output as a draft to be scrutinized rather than being a passive recipient.

<p>A student's role in the AI era, characterized by actively evaluating AI output as a draft to be scrutinized rather than being a passive recipient.</p>
12
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Cognitive Offloading

The risk of letting a machine think for a person, which can potentially weaken independent critical thinking skills.

<p>The risk of letting a machine think for a person, which can potentially weaken independent critical thinking skills.</p>