Testing knowledge and skills with AI in e-learning


In this blog, we list some of the pros and cons of AI: how to use AI to test knowledge and skills.

In the “educational country”, there is a lot of discussion about Artificial Intelligence (AI). Teachers and students/students see both advantages and disadvantages. Many teachers run into the fact that “AI is taking over the writing of essays or other assignments from students”. They type in, in ChatGPT, what they want to write an essay about and voila: a reasonable quality essay rolls out in seconds. So how can you still test what students actually know?

Of course, this is an interesting development. But doesn't this just require us to assess need to approach knowledge and skills in a more creative way? Is it really necessary to write an essay to test knowledge? Or are there other ways that are not easy to produce by ChatGPT? Because the fact remains: AI is here to stay. Where we well being able to change something is how we deal with it. In fact, it also offers huge opportunities to simplify teachers' work, especially when it comes to testing knowledge and skills!

In this blog, we choose to focus on the benefits of AI in testing knowledge to discuss. Of course, we do make a critical comment if, in our opinion, this is appropriate.

Automated assessments in e-learning

The possibilities of AI in automated grading systems are revolutionary! AI makes teachers' work a lot less repetitive and time-consuming because it offers a more efficient and objective way to assess students' or students' work.

For example, AI can evaluate tests and essays on a large scale and provide immediate feedback. AI's ability to analyze text, detect plagiarism, and assess answers is impressive! Teachers can now focus more on other critical aspects of teaching and teaching, such as planning lessons and providing personalised feedback to students.

Another key benefit of using AI to assess assessments is its consistency. It does not fatigue and is not biased, which ensures that each student is assessed in a fair manner. Nevertheless, it remains important to balance automation with the human factor. AI can handle routine assessment tasks well, but assessing complex or creative assignments really still requires people.

Personal follow-up after assessment in e-learning

Adaptive learning systems use AI to continuously assess student progress and adjust content and pace. If a student excels in one area but struggles with another, the system can provide additional support on the challenging subject, while allowing faster progress in the areas that the student is strong in. This personalisation ensures that students receive the right level of challenge and support, preventing boredom or frustration (and possible dropout).

In addition, AI can also find out where the student's interest lies. AI can then adapt follow-up assignments or, for example, the teaching material to the topic that appeals to the student. Of course, this also increases intrinsic motivation.

Detecting plagiarism and cheating in e-learning

Maintaining academic integrity is becoming increasingly important with the growth of online education and the creation of tools such as ChatGPT. Although we previously wrote that teachers often experience that students make it very easy for themselves or even “cheat” by letting ChatGPT write their essay, AI can also contribute to detecting plagiarism, for example. AI can identify similarities in writing style and content online, making it easier for teachers to recognise plagiarism.

AI tools can also evaluate how a student types and interacts with a test or exam. This includes, for example, typing speed, the use of keyboard combinations, pauses between keystrokes, and other aspects of the student's keyboard behavior.

This analysis can help detect irregularities or suspicious activity during an online test, such as using unauthorised tools or copying answers from other sources (such as ChatGPT).

How do we see the future?

To use the full potential of AI in assessment, continuous research is essential. For example, this research could focus on refining algorithms, addressing ethical issues, and ensuring that AI continues to evolve to meet the dynamic needs of education.

To make the most of AI's capabilities in e-learning assessments, ongoing research and development is key - improving algorithms, addressing ethical issues, and ensuring that these tools continue to evolve to meet the changing needs of education. In addition, we should strive for user-friendly interfaces so that teachers and students can seamlessly collaborate with AI-supported assessment tools.

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