MIT's Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training

This is a bunch of fluff. I hope at least the snacks and lunches during the discussions were good.

"Guiding principles: Be bold. Be humble. Put humanity front and center. Lean into learning. Teach with intentionality. No one size fits all."

"Recommendations: Adapt educational processes for an AI-aware world. Center people, community, and the residential experience. Build processes, teams and tools for continuous reflection, iteration, and improvement"

I think you can tell who still has the ability to read long form content by whether they thought this was full of fluff or decently actionable. I think the bolded parts of section 3 are definitely not fluff. I found the document pretty interesting.

I think this has been going on well before AI or LLMs: "A fundamental danger, as we’ve discussed, is that AI can allow students to bypass learning. Equally concerning is that students may internalize a transactional model in which assignments are outputs, teachers are evaluators, peers are optional, and knowledge (or an MIT degree) is an optimizable commodity to be acquired or produced as efficiently as possible."

I'm surprised at the negative comments here. I'm about halfway done reading the report - I think it's clear, well written, and quite frankly the opposite of fluff.

A document like this isn't going to magically "solve" the use of AI in higher education. The purpose is to define a shared understanding of the situation across a large, complex organization, and set an initial direction and general shape for actions to take.

It contains clear, specific observations of how AI is organically changing the reality of education. And, in my opinion, fairly clear high-level guidance on what MIT as an entity wants to do about AI, and what individual departments and faculty should decide on their own.

A document like this doesn't need to be revolutionary, it may feel like fluff because no specific item in it is particularly surprising or groundbreaking, but the value is in having the entire document as a whole. And it is a lot more comprehensive and well thought out than what most companies can put out.

You can think what you like about MIT, but at least they're giving it some thought, however perfunctory it may seem. Many German universities prefer to try and ignore the issue and hope for the best.

I do like that they're open to reconsidering the grading paradigm wholesale. I taught at a boot camp for a while and our highest-placed cohorts were the ones to whom we gave neither grades nor certificates. They plainly understood the game was between them and an eventual interviewer, not them and the school or instructor.

- 2. Guiding Principles

- 2.1. Be humble

- 2.2. Be bold

- 2.3. Put humanity front and center

- 2.4. Lean into learning

- 2.5. Teach with intentionality

- 2.6. No one size fits all

- 2.7. Augmentation not automation

- 2.8. Think beyond the classroom and the campus

- 3. Recommendations

- 3.1. Adapt educational processes for an AI-aware world

- 3.1.1. Revisit course goals

- 3.1.2. Ensure durable learning through new course policies, structures, and forms of assessment

- 3.1.3. Emphasize experiential and project-based learning

- 3.1.4. Build structured in-person social learning into subjects

- 3.1.5. Preserve and expand out-of-class research and career experiences

- 3.1.6. Reconsider grades and incentives

- 3.1.7. Expand in-person spaces for labs and in-person evaluation

- 3.1.8. Provide AI use policies, with justification

- 3.1.9. Exercise caution with AI detectors and online exam platforms

- 3.1.10. Support responsible experimentation in the curriculum

- 3.2. Center people, community, and the residential experience

- 3.2.1. Define and communicate the value of residential education

- 3.2.2. Strengthen social connection and personal wellbeing

- 3.2.3. Encourage instructor disclosure around their own AI use

- 3.2.4. Teach effective, responsible, and ethical use of AI

- 3.2.5. Recognize and mitigate negative impacts of AI

- 3.2.6. Acknowledge AI use in theses and other research work

- 3.3. Build processes, teams, and tools for continuous reflection, iteration, and improvement

- 3.3.1. Establish an ongoing AI and education committee

- 3.3.2. Create school/college- or department-level AI Leads

- 3.3.3. Fund AI Fellows and an AI Implementation Team

- 3.3.4. Create an AI Pilot Fund

- 3.3.5. Provide ongoing training and instructor support

- 3.3.6. Develop metrics

- 3.3.7. Ensure equitable technology access

- 3.3.8. Protect sensitive data and preserve model choice

- 3.3.9. Establish privacy, logging, and auditing policies

- 3.3.10. Monitor AI costs and environmental impact

- 4. Conclusion

"Be bold" by saying nothing of any interest. I have to agree that this is basically just fluff.

Anyone put it into pangram yet?

Was expecting something more concrete with a set of deliverables that can actually be acted on.

Education is so cooked. In 10 years, people arent going to find it useful financially. The AI will be better at economically relevant thinking.

And there aren't enough hobbyist learners to sustain education at its current level.

I'm sick and tired of hearing about how LLMs are somehow going to cultivate a society of "creative people full of ideas" when increasingly the people who are using it the most are accepting the LLMs ideas without question.

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