In education, efficiency often prioritizes outputs—grades, test scores, word counts—while undervaluing subtler aspects of learning like curiosity, creativity, or ethical reflection. Lin is warning, that not everything of value such as the joy of learning fits into measurable categories.

This raises an important question about shifting boundaries in technology use: where do we draw the line between permissible assistance and impermissible outsourcing? Grammarly functioned as a tool, polishing grammar, correcting mechanics, and smoothing awkward / lengthy sentences without replacing a student's intellectual labour.

If most workers rely on AI for content, then originality, voice, and unique perspective become scarce—and therefore valuable. The warning is that AI tends to reproduce “average” responses, so individuals who cultivate their own ideas and styles will stand out in a crowded job market. This places education in a new light: the classroom becomes less about mastering efficiency and more about nurturing individuality, creativity, and critical perspective. There is a true advantage of imaginative contributions.

Memorization or familiarity with facts is not about stockpiling trivia, but about building a mental library that allows students to make unexpected connections. AI can provide quick answers, but it cannot replace the deeper cognitive skill of synthesis—linking disparate ideas into something original.

What once seemed like a reasonable restriction now looks outdated, since calculators became universally accessible. The comparison is while banning calculators seemed absurd to our generation (avg university student age in 2025), the rationale for banning or limiting AI is not the same. Calculators automate mechanical processes, while AI risks automating cognitive and creative processes which are central purposes of education. Thus, there is a necessary skill(s) that must be built before technology is introduced.

The vulnerability of personal data and intellectual property should be a major consideration for students' use of AI. It may not feel relevant or real and perhaps can be easily ignored, but every query leaves a digital trace that reveals sensitive information about a person's values, beliefs, and thought processes, which then can be commodified, surveilled or even weaponized against you. These tools can often profit from exploiting user data - its a matter of digital safety and civic responsibility.

There is an intrinsic reward inherent in the effort and mastery of a skill, reminding us that learning is not only about external outcomes, getting a higher grade, but also about internal fulfillment. The value is not just in the final product, but in the act of honing a skill and overall experiencing growth. By constantly outsourcing outputs, the achievements then result in hollow victories.

One of the most deceptive qualities of AI: its rhetorical confidence. AI's fluent prose can easily persuade readers even when the content is inaccurate or fabricated. This lack of confidence can blur the line between trustworthy information and misinformation, making it difficult for students to exercise critical judgement about what to verify.

There is a subtle but serious intellectual risk: the narrowing of thought. By design, large language models produce outputs that reflect the most statistically probable or average responses which discourages originality and nuance. This endangers diverse perspectives and flattens them into predictable patterns shaped by prior date or algorithms.

This metaphor frames education as a kind of mental training ground, where the workout is quite simply, doing the reading, writing, and critical thinking and as a result, building intellectual strength. Just like any other muscle in our body, our mind weakens, cognitively speaking, with the overall reliance on AI. Extending this concern to doctors, and other professionals which may connect to a larger debate in educational philosophy about resilience, discipline, and long-term costs of outsourcing efforts to machines. I see Lin's point as preserving mental fitness necessary for intellectual and professional life.

University is not costless - it involves financial investment, by raising this point, Lin is highlighting that education is a scarce and valuable resource, not something to be treated lightly or bypassed with shortcuts like AI. It reframes AI misuse as not just a violation of rules, but as a kind of self-sabotage that undermines the very reason for being at university, essentially pushing students to reconsider whether leaning on AI aligns with their educational goals or wastes the financial sacrifice being made to be afforded this opportunity.

This gestures to the almost utopian promises often associated with AI. By listing global crises like climate change, disease, and scarcity - though, the scale of these claims contrasts sharply with the classroom context: while AI may one day help solve humanity's hardest problems, it does not necessarily follow that it should solve a student's homework struggles. Framing AI as some saviour of civilization is inappropriate because this obscures its limitations, risks, and unintended consequences. What might be losing in human intellectual development?

This is both appealing, and a danger of AI in education. On the one hand, it democratizes access to polished language and complex information, ultimately giving students who struggle with writing mechanics or academic conventions a powerful tool. This acknowledges the real barriers that less experiences or less confident learners face which may feel liberating for these students. However, the bottom line is that while AI masks those struggles, it does not resolve them. Students skip over the process of learning how to write and think for themselves, leaving them entirely dependent on a system that performs the task for them - so there is disempowerment through dependence.

This statement speaks to the disciplinary role of education. Lin is arguing that part of a teacher’s responsibility is to create conditions where students stretch beyond comfort, engaging in struggles they might otherwise avoid. Discipline here isn’t about punishment but about guidance: helping students practice perseverance, delayed gratification, and critical thinking. In a culture that often prizes convenience and instant results, AI tempts students to bypass the very difficulties that cultivate growth. Teachers step in to enforce boundaries—not to limit freedom, but to protect the long-term benefits of wrestling with hard ideas. Intellectual resilience is worth the challenge and it is not possible without structured discipline.

The contrast between doing, and producing. In particular, this emphasizes the difference between active engagement and passive output. Lin highlights that philosophy - and by extension, education, is about human activity, not just polished results. He makes sure students don't miss the key concepts. There is a hint of a Marxist thought in this passage, even if Lin doesn't frame it in those terms. The doing philosophy = authentic, lived, human activity similar to unalienated labour. While the producing philosophy introduces a commodified product detached from natural human engagement.

By openly acknowledging the usefulness of AI, Lin demonstrates fairness and credibility. He shows that is not entirely rejecting AI out of fear or ignorance, but out of principle, which makes his case stronger. These benefits -speed, reduce stress, and help with mechanics - are genuine and many students find these benefits attractive. However, Lin reframes them as secondary to the real goals of education which are deep thinking, meaningful engagement, and intellectual growth. So in this way, he distinguishes between surface-level advantages, and deeper values. Arguing overall, efficiency should not override learning. Very persuasive point.

This point shifts the conversation from individual integrity to collective fairness. When a student uses AI dishonestly, they aren't only misrepresenting their own learning but also undermining the efforts of classmates who struggled authentically through the assignment - creating an uneven playing field where students benefit from shortcuts while others invest time and energy into genuine work.

This phrase captures Lin's ethical stance. By comparing AI use to a pyramid scheme, he implies that AI's apparent polish hides a hollow foundation - borrowed knowledge without genuine understanding. The idea of "fraud" underscores how misrepresenting AI's work as your own undermines honesty in education. On this thought, students are invited to consider not just whether AI can produce work, but whether presenting it as theirs is intellectually or morally defensible. So does convenience justify compromising authorship?