What to Do About AI in Colleges?

What to Do About AI in Colleges?

There is a growing perception among students that ChatGPT can answer any question they have. This feeling extends to young professionals as well. Faculty must address this significant issue, perhaps even more urgently than traditional political problems. Let’s explore this further.

Mathematics

With the advent of tools like Maple and Mathematica*, students quickly realized these tools could solve many well-defined problems. These specialized AI tools are continually improving. Students understand this but accept that they must learn the techniques to pass their courses.

Engineering

Until recently, AI did not perform well on some engineering exams, but its capabilities are improving. Despite this, students believe AI will be available when needed in the future. As with math classes, students still comply and learn the material.

Practical Application

What students often fail to see is that in practice, problems are not simply presented; they must be created from underlying situations with contingencies. Students need to see examples of this by working through complex situations where they must formulate a problem-solving strategy. AI does not yet do this.

Writing

At many colleges, students are not required to take a composition course; instead, writing courses are taught according to department-specific criteria. This is unfortunate. However, AI can assist here. For example, you can supply a draft of your written work to ChatGPT and ask it to rewrite it. You might find it smooths out your writing, cleans up the grammar, and reorganizes the material better. I regularly use this feature, and it is helpful. These Large Language Models (LLMs) are totally impressive.

Softer Sciences

In the softer sciences, students are often assigned topics or papers. What’s to prevent students from simply asking AI to compose a paper on the topic? AI has such a vast information base that it often generates papers beyond the scope of most students. The question is: Why shouldn’t students use these AI tools, at least to get started? The significant disadvantage is that they never learn to assemble disparate information sources into a coherent and consistent product. They never learn where to look or how to gather information. What can be done? Students must now be confronted with situations where they must create a problem to be solved and then write about it. This should become the principal part of the assignment. AI cannot do this — yet (I think).

Verification

You cannot check the output for AI intervention. For example, one can easily switch between AI tools, asking for multiple rewrites. This makes it difficult to verify originality.

This is just a brief introduction to the problems AI brings to learning, and they may not be receiving the attention they need. AI is changing everything. Students also need to learn what AI cannot do and base their lifelong learning on this new reality.

*Maple and Mathematica are specialized software tools that do symbolic mathematics, statistics, and high-level programming. They are essential tools for many scientists and mathematicians.

P.S. This version is the AI-generated rewrite of my original rough draft.

Behavioral Science and Problem-Solving

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Where is AI (Artificial Intelligence) Going?

How to view Artificial Intelligence (AI). Imagine you go to the store to buy a TV, but all they have are 1950s models, black and white, circular screens, picture rolls, and picture imperfect, no remote. You’d say no thanks. Back in the day, they sold wildly. The TV was a must-have for everyone with $250 to spend* (about $3000 today). Compared to where AI is today, this is more or less where TVs were 70 years ago. In only a few decades AI will be advanced beyond comprehension, just like TVs today are from the 50s viewpoint. Just like we could not imagine where the video concept was going back then, we cannot really imagine where AI is going. Buckle up. But it will be spectacular. *Back then minimum wage was $0.75/hr. Thus, a TV cost more than eight weeks' wages. -------------------------

Fake News

If you've been following the news the last couple of days, you will note the flurry of copy devoted to fake news. Both sides are blaming whatever has befallen them the consequence of fake news. Let's look at this phenomenon a bit. When I was a student years ago, a friend climbed some mountain in Peru. A article was written in the local newspaper about the event. In only three column inches, the newspaper made about six errors. An easy article to write you say? Just interview and reproduce. Yet so many errors? The question is this: was this fake news or bad reporting? The idea here is that fake news comes in various flavors. Bad reporting – errors made by the author or editor Opinion presented as news Deliberate creation of falsehoods to favor a point of view The reporting of selected truths to favor a particular point of view Now we have the big social media ...

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