Everything that we have covered in this class has been under the assumption that the integration of AI into daily tasks is inevitable. I wish that I did not agree with this fact, but I do. I spend many hours of my life on Adobe platforms, and I have for many years. One thing that I have noticed the most is that with every update of Adobe comes another AI tool that is shoved into your workspace. It has gotten to the point that after working in Photoshop for too long, my computer will start to crash.

While this is extremely frustrating, I have also found that many of these AI tools do help me to complete simple tasks faster. Just as we have discussed in this class, AI is never perfect and even when I am using it on a platform that is suggesting its use, I often have to go back through and fine tune the changes that AI made to any given project. This use of AI tools is not going to go away in the content creation realm any time soon. At first, it seemed like the AI tools were something fun to just play around with, but now it seems as though I cannot complete a project without using them at least once.

From this class, I have gained the knowledge to ethically pick and choose my moment of using AI. LLMs are never going to replace human creativity, but they have made it easier to navigate creative outlets that have often been too complicated for an untrained eye. In the future, I hope to continue to use LLMs and their tools to further my work and polish designs. But I never want an LLM to determine what my pieces convey or change my style.

People have raised concerns that AI will steal your job or write your homework for you. One concern that I don’t see enough people talking about is that we are outsourcing our thinking to LLMs. This has wide-ranging impacts on lots of things. You might be thinking, “Why should I be thinking about how to specifically phrase an email when Claude can hit all the key points you want to in seconds?” Some people have even started using ChatGPT as a replacement for Google, which I think shows a lack of understanding of how LLMs work. For many people, the problem-solving skills that forced our brains to figure things out are being smoothed away by AI.

This matters because human relationships all run on the ability to reason independently and to change your mind based on your own thinking. LLMs don’t even have the capacity to “think” for themselves. The moments when we don’t understand aren’t meant to be optimized by LLMs. If everyone delegated their capacity to think based on models trained on yesterday’s consensus, we wouldn’t form the unique opinions that make humans special. So, I say, write that boring email, and do it in your own voice.

Topic: How do you plan to integrate Al in your life going forward – whether personally
or professionally? Do you feel you have a choice here, for example is deciding not to
use Al an option?

Al will be a big part of my day-to-day life from here on out. On a personal level I do not enjoy
interacting with Al. especially emotionally; I also do not agree with the ecological costs of building
massive and powerful Als. However as a chemistry major going into industry, there is not a piece
of data I will ever collect that I won’t be putting into Al. My field requires me to know how to use Al
and its really become a non-negotiable so regardless of any concerns I have, I would not have a
choice in my Al use.”

This topic is important because research and development is an extremely important step in the development of new technologies. In chemistry research there are so many necessary steps and checks that AI would not be able to pass.

https://www.cas.org/resources/cas-insights/ai-models-for-chemistry-charting-the-landscape-in-materials-and-life-sciences

This course has been very interesting and has definitely shaped my views on AI and how I intend to use AI moving forward. When we talked about why it is important to cite AI, I realized then that not only does that ensure credibility and accountability, it also ensures that we give credit where it’s due. I think it’s easy to cite sources from literature, but I think we must extend that same attitude when working with AI because it is also a source of information in some sense.

Personally, I will continue to use AI for tasks including complementing my studying, generating quiz ideas and topics, comparing ideas, and for feedback on various projects and work. Professionally, I plan on using AI to cross-check my work, give feedback, and find sources (which I will double-check for accuracy). I do not think I have a choice on whether or not to use AI because a lot of fields are embracing AI, and especially in scientific research. For example, many authors are using AI to find sources, improve readability, and ensure reproducibility of their papers and experiments. If anything, it might hurt to not use AI or know how to navigate it, and I like that we are learning those skills in this class and in other school activities.

Nevertheless, it is important to recognize that AI is not always accurate. For example, we explored the Alzheimer’s hypotheses and how AI gestures to use the old hypothesis just because of how frequent it is in the literature. That being said, AI is still very useful in scientific research and needs to be verified and cited to ensure accuracy.

Overall, we should continue paying attention and updating how we use AI and when it’s okay or not okay. Especially as students and pre-professionals, AI has various uses that we can benefit from while being conscious of its limitations. We should also rely on our personal ideas and thoughts often, in order not to over-outsource everything to AI. We can do this by creating specific policies on AI use whenever needed, creating trusts and cooperations to represent us and share our thoughts and sentiments on AI use, etc.

The single most important GenAI issue I wish the general public knew was that LLM’s do not think for themselves. This is a common misconception that even I believed coming into this class. If you were to ask a lot of people, they would also believe that LLM’s think at extremely high speeds and come up with logical answers based on its thinking. However, now we know that it is trained on a ton of data and based on this data, it predicts the next word and spits out a response. Knowing this, users need to understand that they need to have prior knowledge on what they’re searching, check for other sources, and also refine their prompts if they want better answers. One specific example of people using GenAI incorrectly is when they use GenAI in place of Google or other search engines. Quickly typing in a prompt and skimming the answer that an LLM produces and trusting it as law is harmful to one’s knowledge, but the general public do not understand that because they do not know the single most important GenAI related issue that I mentioned above. In conclusion, LLM’s are useful, but in the right context and when you believe that it is a magic genie that produces all the right answers in rapid speed, you will become reliant on it, when it can easily hallucinate based on its training data or provide biases that you do not want. Not only can it be incorrect, but it can serve as a mental crutch that we as a society do not need as creative beings.

A simple way to think about “too much” AI use is whether students are still doing the thinking.

Dinsmore and Fryer warn that “some of those calling for or directly introducing genAI into formal education fail to fully understand… how humans learn in any given domain of knowledge” (Dinsmore & Fryer, 2026). This suggests the risk is using AI in ways that replace the mental effort needed for learning.

So AI use is “too much” when it does the key thinking for students, like planning answers, explaining ideas, or solving problems, and students just accept the result. That may improve work in the short term, but it reduces learning.

AI use is more acceptable when it supports learning instead. For example, it can give feedback, examples, or help students improve their own ideas, as long as they still make decisions and explain their thinking.

In both classrooms and professional life, the boundary is the same: AI should help people think better, not think for them.