Our new research has found that AINI is devastating to studies. This is how we should answer

Artificial Intelligence (AI) is devastating the studies and exams of the University.

Generative AI tools, such as Chat GPT, can now produce articles and diagnostic answers in seconds. As we noted in a study earlier this year, universities have infiltrated works, updating policies, and adopting a new system of fraud.

But the technology changes as it works. Students have permanently reported fraud through their degrees.

The problem of AI and diagnosis has put a lot of pressure on the institutions and teachers. Today’s students need diagnostic works to complete, and also confidence that they are doing matters what they are doing. Community and employers have to assure that the university degree is worthwhile.

In our latest research, we argue that the problem of AI and diagnosis is much more difficult than that, even as a result of media discussions.

This is not something that can be determined only once we find the “right solution”. Instead, this sector needs to identify the AI ​​in the diagnosis is a complex “wicked” problem, and respond accordingly.

What is a wicked problem?

The term “Anti -Issue” was made in the 1970s by Theverst Horst Ratal and Melon Weber. It describes the issues that deny clear solutions.

Well -known examples include climate change, urban planning and health care reforms.

Unlike “tim” issues, which can be resolved with a lot of time and resources, there is no single correct answer to evil issues. In fact, there is no “truth” or “wrong” answer, just better or worse.

The evil problems are dirty, resistant to being interconnected and closed. There is no way to examine a solution to a evil problem. Efforts to “fix” the issue inevitably produce new tensions, trade relations and unannounced results.

However, there are no “correct” solutions to admit, which does not mean that they are not better and worse. Rather, it allows us to appreciate the nature and need of the trade relationship contained in it.

Our research

In our latest research, we interviewed 20 university teachers at Australian universities.

We recruited the participants by seeking references in four faculty at a major Australian University.

We wanted to talk to teachers who changed their studies because of Generative AI. Our goal was to better understand what the diagnosis was being chosen, and what challenges the teachers faced.

When we were setting our research, we did not necessarily think of AI and diagnosis as a “evil problem”. But the interview has come to light.

Our results

Interviewers described matters with AI as an impossible situation, which features commercial relations. As a teacher explained: “We can make the diagnosis more AI proof, but if we make them very hard, we test compliance rather than creativity.”

In other words, the solution to the problem was not “true or wrong”, just better or worse.

Or as another teacher asked: “Have I hit the right balance? I don’t know.”

There were other examples of incomplete trade relations. Should studies allow students to use AI (as if they were in the real world)? Or completely delete to ensure that they demonstrate freely?

Should teachers have more oral exams – which appears more AI resistant than other studies – even if it increases the workload and increases the losses to some groups?

As a teacher explained, “250 students by […] 10 minutes […] It’s like 2,500 minutes, and then how many days do it work to manage just one diagnosis? “

Teachers can also personally determine hand -written exams, but it is not necessary not to test other skills that students need for the real world. Nor can it be done for every diagnosis in a course.

The problem keeps changing

Meanwhile, teachers are expected to re -design the reviews immediately, while this technology itself changes. Geni tools such as chat GPT are releasing new models as well as new models, while new AI learning tools (such as AI Text Summarizers for Unit Reading) are rapidly commonplace.

At the same time, teachers need to maintain all their usual teaching responsibilities (where we know they are already stressed and increased).

This is a sign of a dirty problem, which does not have a closure or closing point. Or as an interviewer explained: “We do not just have the resources to be able to detect everything and then write a violation.”

What do we need to do instead?

The first step is to stop the AI ​​in the diagnosis is a simple, “solving” problem.

Not only does it fail to understand what is happening, it can also cause stroke, stress, burnout and trauma among teachers, and after the policy Mandor, the institutions continue to try a “solution” after the next.

Instead, AI and diagnosis must be considered as something to be permanently interactive rather than resolved.

This identity can carry a burden from teachers. Instead of pursuing a perfect fix, the companies and teachers can focus on the construction process that are flexible and transparent about the trade involved.

Our studies show that universities allow the teaching staff to “permit” AI to better solve.

It also includes the ability to compromise to find the best approach to the unit and a group of students. All potential solutions will have commercial offices – oral exams can be better assured to learn, but some can also be biased against groups, for example, those whose second language is English.

Perhaps this also means that teachers do not have time for other course components and it can be fine.

But, like the many commercial office included in this issue, the responsibility of calling will remain on the shoulders of the teachers. They need our help to ensure that weight does not crush them.

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