https://news.harvard.edu/gazette/story/2026/02/preserving-learning-in-the-age-of-ai-shortcuts/
Easy AI answers all the time?
How will we balance human learning against the constant temptation of “easy AI answers all the time”? Of 7000 high school students surveyed, about 40% indicated that they had failed to resist the temptation of over-using AI on their schoolwork.
Self-Regulation will be a crucial skill in our coming age of AI. OR at least, designing environments that support human self-regulation, because we are temptation-succumbing agents. At least, I am.
This is a great podcast, and you can listen, or read the transcript.
WiFi Blocking?
I like our best human thinkers as they grapple with the necessary productive cognitive friction that is needed for human brains to encode hard-earned knowledge. It is not good enough to have a super-search algorithm finding answers for you. There is something ineffable about being able to take a difficult problem, and reason through it as a human.
At the same time, we know that younglings have access to all the AI models out there. No amount of “adult protection” or “wifi blocking” or other pretend gatekeeping will keep smart kids from figuring out how to get to forbidden fruit.
So it remains, how would informaticists, physician teachers, all teachers, coaches, mentors, suggest we move forward when EVERYTHING is changing, and this new AI entity or entities are everywhere?
Idea 1: Invite our learners into the problem.
We recently struggled whether to grant our medical students access to Abridge, the ambient note solution offered at University of Colorado and UCHealth. Shouldn’t we prohibit students from using ambient notes? Don’t all of us teaching professors remember struggling to write comprehensible notes in our learning years, and now can quickly and incisively think through hard problems by writing notes that get to the root of the patient’s pathophysiology? Who hasn’t worked on writing a note, and in the process, discover an angle on the patient’s medical problem that was not obvious before starting the writing?
Now, if ambient notes are done at the end of the visit, where does that cognitive friction go? Removing this friction is perhaps a slight problem (and a big benefit) for experienced clinicians. However, removing this friction for students and residents might impair their learning just as it is most needed to form neural pathways, knowledge and … wisdom and judgement.
This year our graduate medical education leaders decided to give all students access. And then allow them to choose: how WILL you use it, knowing that it might impair your learning and there are as yet NO GUIDELINES? We must write these guidelines together. As a result, most students have chosen NOT to use ambient notes because of exactly that concern: they are in medical school for the training, NOT to simplify their work. This is a gratifying outcome.
Idea 2: Construct problems unsolvable by AI.
In this podcast, college professors describe writing problems specifically so that AI at present, cannot solve them. This is difficult work, and perhaps unsustainable if AI continues to improve at dramatic rates. It is an interim solution. Better yet…
Idea 3: Learners explore the human/AI interface.
Assume that everyone has access to AI, and then ask questions that could not be asked before. Specifically, have our learners ask questions that could not be asked before. And in answering them using AI, and critiquing each other, learning the field in a way not possible before.
In this podcast, college professors are now adjusting their curricula, instead of giving take-home exams that GPT can easily answer, they have in-class work where students design and ask mathematics problems that the AI cannot answer, and then have to figure out an answers the long-hand way. As a result, professors are seeing levels of learning and sophisticated understand that comes from exploring the space WITH an AI and all the extra reading needed to figure out what the edges of what an AI can do, and what the modern questions are in mathematics, and how to approach them. This also, is a winning approach.
This man appears sad, but is he?
We have a new entity in every conversation
How might we keep human learning, human judgement, human embodied cognition front and center, when our old teaching methods no longer work? It is both terrifying and amazing to think what comes next.
