The AI crisis for teaching: "Train your own neural network!"
Abstract
Everyone seems to be thinking about so-called artificial intelligence (AI) these days. As scientists, we understand that we're not about to have a conscious computer intelligence taking over the world, but we're nevertheless concerned about how "large language models" (LLMs) and AI "agents" are impacting the way that we do science. This concern has generated several recent essays about the effects of AI on research, including in my own field of astronomy and astrophysics. These opinion pieces tend to miss the biggest current concern for those whose jobs combine teaching with research - although the threat to future scientific research as we know it is probably a bit concerning, the effect on teaching is an existential crisis right now! We need to hang on to the parts of education that we think are at its core - so that students coming out of degree programmes are actually able to think for themselves, "like an astrophysicist". This means emphasising to students that they should use AI tools to support their education and not to replace it. One way of explaining the importance of this principle is to point out that students should be training their own neural networks.
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BibTeX
@misc{scott2026crisis,
title = {{The AI crisis for teaching: "Train your own neural network!"}},
author = {Douglas Scott},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.39433},
eprint = {2609.39433},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.39433},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The AI crisis for teaching: "Train your own neural network!"
AU - Douglas Scott
PY - 2026
DA - 2026/09/30
PB - arXiv
DO - 10.48550/arxiv.2609.39433
UR - https://arxiv.org/abs/2609.39433
AB - Everyone seems to be thinking about so-called artificial intelligence (AI) these days. As scientists, we understand that we're not about to have a conscious computer intelligence taking over the world, but we're nevertheless concerned about how "large language models" (LLMs) and AI "agents" are impacting the way that we do science. This concern has generated several recent essays about the effects of AI on research, including in my own field of astronomy and astrophysics. These opinion pieces tend to miss the biggest current concern for those whose jobs combine teaching with research - although the threat to future scientific research as we know it is probably a bit concerning, the effect on teaching is an existential crisis right now! We need to hang on to the parts of education that we think are at its core - so that students coming out of degree programmes are actually able to think for themselves, "like an astrophysicist". This means emphasising to students that they should use AI tools to support their education and not to replace it. One way of explaining the importance of this principle is to point out that students should be training their own neural networks.
ER -