Thanks for this series. As a college English teacher, I have been watching the steady increase of students using AI in my own courses and have been trying to navigate these changes. I'm wondering, though, how much agency we as instructors really have in controlling our students' use of these tools. Soon AI will be embedded in programs like Word, giving it a legitimacy that won't require any thoughtfulness about its use (remember how people worried that the spellcheck tool would hurt students' spelling, and now no one would consider not using it?). Many students already don't value the process of learning, so they will choose AI regardless of whatever conversations we have about the ethics of it. And many more will choose AI because it saves time that they could use for other projects that interest them more.
Because of this, I've been rethinking my entire approach to teaching, and now I want to include more experiential learning in my courses. I'm not sure what this will look like yet, but I do feel like the traditional research essay can no longer be the measure of student comprehension. If AI can do it for us, we need to be asking what other ways are there to teach and measure critical thinking? I don't have the answers yet, but I appreciate your posts as they are helping me think through what's at stake in my classroom going forward.
I'm concerned that poorly designed research AI will 'help' students solidify their biases, rather than telling them their thesis does not represent the scholarly consensus. One of the services I provide as a librarian is gently suggesting that it may be easier to complete their research if they use the sources that exist; will AI do that, or will it locate low-quality sources to match their initial assumptions?
Based on McMurtrie's article and my own observations I'm not at all convinced students will end up with AI-researched papers that they understand any better than the sources they skipped reading. If it could - if it helped them develop the necessary vocabulary and background, and quizzed them on the sources and the connections it made between them, such that they could present the paper and correctly answer questions about it - well, that would be great.
Have you started to think about what measures you can put in place to preserve friction in students' research process? I think this needs to be part of the conversation moving forward, if we value friction. What do you think? I honestly don't know where this conversation will lead at this point. Just putting this thought out in space.
There should be a balance between traditional and AI-assisted research.
If students can learn the beauty of both worlds, they'll realize why AI is not a substitute for hardwork but a magnifier of creativity and critical thought.
I know because I've done both... And I'm a Gen Z. If you ask me, I prefer the traditional but due to time constraints, I'll always choose to navigate with AI.
As for how other Gen Zers will appreciate this method, I'm clueless.
Definitely one of the essays I appreciated the most on the impact of ChatGPT on information seeking. What I really liked is certainly that these ideas can easily be applied, in part and with the necessary clarifications, to scientific research. How is AI also impacting the way researchers themselves 'do research'? Some ideas emerge in this case too.
Some of the writings of reports by human authors have been made deliberately difficult to understand for a variety of motives, none of the favorable to the better understanding of the subject and the sharing of the research discoveries with other students and interested parties. For this it is possible that AI summary works may be useful, but it is my impression that a researcher who is doing more than simply looking for a source of income through his/her work, would express the topics and details of this in the most simple language that others at the same level of understanding could easily follow.
I gather from some research reviewing editors that there is a lot of fraudulent writing about certain subjects and the aim of this is to make a living through impressing that money source management about how deeply involved the would-be research really is. This is a fault of poor management, because any manager who is any good should be able to see through the applicant's work from a mile away, unless of course , if he/she is being bribed. This can be a tricky subject because anyone can have more than one bank account and not need to disclose some of the income for tax purposes of investigation, when a crime is suspected. I may be wrong but it seems to me that competence and honesty of major management and associated players is at a premium and things are likely to get worse until out good knowledge about what is really significant in our better knowledge about the current state of the world is going to cease its growth.
Thanks for this series. As a college English teacher, I have been watching the steady increase of students using AI in my own courses and have been trying to navigate these changes. I'm wondering, though, how much agency we as instructors really have in controlling our students' use of these tools. Soon AI will be embedded in programs like Word, giving it a legitimacy that won't require any thoughtfulness about its use (remember how people worried that the spellcheck tool would hurt students' spelling, and now no one would consider not using it?). Many students already don't value the process of learning, so they will choose AI regardless of whatever conversations we have about the ethics of it. And many more will choose AI because it saves time that they could use for other projects that interest them more.
Because of this, I've been rethinking my entire approach to teaching, and now I want to include more experiential learning in my courses. I'm not sure what this will look like yet, but I do feel like the traditional research essay can no longer be the measure of student comprehension. If AI can do it for us, we need to be asking what other ways are there to teach and measure critical thinking? I don't have the answers yet, but I appreciate your posts as they are helping me think through what's at stake in my classroom going forward.
I'm concerned that poorly designed research AI will 'help' students solidify their biases, rather than telling them their thesis does not represent the scholarly consensus. One of the services I provide as a librarian is gently suggesting that it may be easier to complete their research if they use the sources that exist; will AI do that, or will it locate low-quality sources to match their initial assumptions?
Based on McMurtrie's article and my own observations I'm not at all convinced students will end up with AI-researched papers that they understand any better than the sources they skipped reading. If it could - if it helped them develop the necessary vocabulary and background, and quizzed them on the sources and the connections it made between them, such that they could present the paper and correctly answer questions about it - well, that would be great.
Have you started to think about what measures you can put in place to preserve friction in students' research process? I think this needs to be part of the conversation moving forward, if we value friction. What do you think? I honestly don't know where this conversation will lead at this point. Just putting this thought out in space.
Good writing. Thanks.
"we’re faced with the question of what we value in education" and I would add, what we even mean by "education".
There should be a balance between traditional and AI-assisted research.
If students can learn the beauty of both worlds, they'll realize why AI is not a substitute for hardwork but a magnifier of creativity and critical thought.
I know because I've done both... And I'm a Gen Z. If you ask me, I prefer the traditional but due to time constraints, I'll always choose to navigate with AI.
As for how other Gen Zers will appreciate this method, I'm clueless.
Definitely one of the essays I appreciated the most on the impact of ChatGPT on information seeking. What I really liked is certainly that these ideas can easily be applied, in part and with the necessary clarifications, to scientific research. How is AI also impacting the way researchers themselves 'do research'? Some ideas emerge in this case too.
Some of the writings of reports by human authors have been made deliberately difficult to understand for a variety of motives, none of the favorable to the better understanding of the subject and the sharing of the research discoveries with other students and interested parties. For this it is possible that AI summary works may be useful, but it is my impression that a researcher who is doing more than simply looking for a source of income through his/her work, would express the topics and details of this in the most simple language that others at the same level of understanding could easily follow.
I gather from some research reviewing editors that there is a lot of fraudulent writing about certain subjects and the aim of this is to make a living through impressing that money source management about how deeply involved the would-be research really is. This is a fault of poor management, because any manager who is any good should be able to see through the applicant's work from a mile away, unless of course , if he/she is being bribed. This can be a tricky subject because anyone can have more than one bank account and not need to disclose some of the income for tax purposes of investigation, when a crime is suspected. I may be wrong but it seems to me that competence and honesty of major management and associated players is at a premium and things are likely to get worse until out good knowledge about what is really significant in our better knowledge about the current state of the world is going to cease its growth.