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

Then I hope you have better memories of our country.

To explain briefly:

Broadly speaking, there are these types of schools:

- Hauptschule (lower secondary school) - Realschule (intermediate secondary school) - Gymnasium (grammar school)

in various organizational forms. At a Gymnasium, you can obtain a full Abitur (university entrance qualification), which qualifies you to study at - Universities - Universities of Applied Sciences

For studies at Universities of Applied Sciences, a Fachabitur (subject-specific university entrance qualification) is necessary, which can be obtained in various ways. Previously, diplomas were usually awarded there, an academic degree between a B.A. and M.A. Since the Bologna reforms, the system has been changed to B.A./M.A.

At Universities of Applied Sciences, studies are more practically oriented (with practical phases); however, the biggest difference is that you cannot pursue a doctorate there, which is only possible at a "proper" university.

Besides social informatics, I also teach academic writing, which is why the topic of AI is important to me, because it's not easy for teachers to deal with. But I also don't think the strategy of simply looking away is effective.

To clarify, I am pro-university. I'm just saying that institutions do not always behave in ways that are purely for educational benefit, such as with legacy admissions or grade inflation. I don't think the blame falls solely on students for viewing university degrees as a necessary step for something else. I would sure bet admissions committees will use AI to screen applicants, if they aren't doing so already.

It will only get harder. From the trenches in tech it’s hard enough to get people who should know better to put in the work to understand the ai output.

For education I can’t imagine. You might have to resort to actual discussion in classrooms instead of homework, with the accompanying need for both more educators and higher quality therein. Which isn’t going to be easy when education is fully under regulatory capture (at least for K-12 in the USA)

- 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

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.