Title: AIE - AI in education
Course: AI Guide Academy > Section: AI in Education (AIE)

Let's get our hands dirty. We try to solve a few educational problems and start your own portfolio.

Creating learning goals from content

How to create learning goals using GenAI

Ebru’s Two Cents

    - Sometimes when thinking in German, it’s easy to put the verb at the end of the sentence. In English, however, the verb should come first, followed by the knowledge. So instead of writing “students should be able to [knowledge] [verb]”, the correct order is “students should be able to [verb] [knowledge].”
    - It’s interesting that ChatGPT was referred to as “he”. Assigning a gender to an AI can reinforce unconscious biases and anthropomorphize a tool that isn’t human. A more neutral choice is to use “it” or simply “ChatGPT”.

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Written Version:

The core of this tutorial is the development of effective learning goals, which form the backbone of all educational tasks, content, and assessments. As budding scholars, we often face the challenge of formulating these learning goals from scratch or tailoring existing material to match our intended outcomes.

To simplify this process, I'll demonstrate a method of creating learning goals based on pre-existing content ('our own stuff') from an article I wrote earlier. Download the article here or use a source of your own. However, the approach I am about to show you isn't a one-size-fits-all solution and should serve as a guide. Please work along, but modify the process to your liking. The goal is to create a prompt that allows me to throw text-based material in and get learning goals out.

We begin by defining the context: "I need learning goals for a seminar I'm conducting as a university lecturer." Specifying the format is crucial. In this case, the format is a list of bullet points with content-specific components for each entry.

Let's dive into the process with an exploration of my earlier article. For demonstration, I used ChatGPT to parse my article and generate associated learning goals. It’s okay if the tool makes mistakes. For example, let's examine the first learning goal proposed by our AI: "Understand the role of gamification in motivation." While this appears comprehensive, it's rather vague. It could use some specificity and context.

With this in mind, I revised the learning goal format to "After the seminar, students should be able to ___", with a blank space for the requisite knowledge and an appropriate verb from the learning taxonomy. Keep in mind that working with AI is a process of iteration. I encountered situations where the AI tool needed explicit instructions in certain areas, like language preference or specification of learning content and goals. But once cleared, it churned out more pinpointed learning goals like: "After the seminar, students should be able to explain the basic principles of gamification."

Once the AI tool generates a list of learning goals, it is the lecturers' task to sift through these, cherry-pick the most relevant ones, and further refine them. For instance, in this case, I classified '...explain how reinforcement mechanisms influence behavior in gamified environments' as a good, specific, and effective learning goal. Remember to iterate and improve your process continuously. Over time, a task that could take upward of 30 minutes only takes about five minutes with an efficient system in place.

In conclusion, we can turn basically anything into learning goals. Do you have a dream job? Collect a few job offers and write a prompt to summarize what learning goals you have to hit to land your dream job! Here is the "rough process" of how we got to our final prompt.

Process steps:

sort-paragraphs.h5p

Well done! Last but not least, remember: Always have refinement in mind.

- Using genAI tools means: Iterating until you are satisfied
- Accept that AI-generated content may still need refinement.
- The goal is to save time, not perfection.

Now it's your Turn: When you are finished, tell us about your experience in the forum. We don't need to know your learning goals, just your process for creating them. Respond to at least two others. How does your experience compare with that of your peers?

Get Trustworthy Materials

Navigating the Sea of Online Resources

Welcome to this chapter, where we aim to get knowledge concerning a topic FAST!

The key focus is on specific strategies that help to gather reliable data quickly. In the video, I guide you through effective techniques using prominent online tools and services, focusing primarily on their applications within the education field.

Below are some tools for gaining knowledge mentioned in the video, each varying in reliability and purpose. Can you match them correctly?

drag-the-words.h5p

Your turn: Now that you've used AI to design some presentation materials, tell us about your experience in the forum. Respond to your peers about what they did, as well.

Slides

Creating Slides with GenAI

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Ebru’s Two Cents

    - As you may notice in the video, the slide deck does not appear on screen, even though Ben is referring to it in his presentation. This is a technical issue, and our team is already working on a resolution.

Written Version:

Welcome! We are diving into the world of PowerPoint presentations, slides, and the complexity of mastering these tools. The crux of the problem lies within the density of information present in many slide-based presentations. Overflowing text, scanty use of imagery, all while trying to listen and grasp the subject, can make listening quite challenging... But whom am I telling that? So how do we not make bad presentations?

I personally use three in my presentations: Pictures, single words, and sometimes my desktop (just as it is now).

But let's consider, for instance, the Gamma app, an AI tool that claims to generate engaging slides on the spot. When we try to create a presentation on AI and climate change risks using this tool, the outcome is a versatile set of editable cards. Not only can you customize these cards, adjusting the topic as you wish, but you can also select your style preference, image AI availability, and model choice for the pictures. Voila, a rapid generation of gorgeous slides! It may be gorgeous, but it could be better.

Though stunning, these slides pose a challenge to readability, cramming extensive information in limited space. This can hinder the primary function of a slide – to prompt what the presenter needs to say alongside aiding the audience in "getting back on track" if they let their thoughts wander for a bit. It isn't that these AI-generated slides can't be worked with. But they might not always suit every presenter's unique style, particularly if the focus should be more on the speaker and the story they are telling.

The crux of the issue of AI-generated slides is the overwhelming text, which, while good-looking, distracts the audience, who end up straddling between reading and listening. But we could definitely just reduce it, so maybe just use the style and do the text yourself?

In conclusion, the process of forming effective slides for a lecture is a bit of a tightrope walk, made seemingly simpler by AI but not necessarily more efficient. With AI, iteration might be faster, but mastery in delivering engaging presentations, it appears, still lies in the human hands of the presenter.
But enough talking, it's time to put your AI tool evaluation skills to proof:

You can use the Gamma App we talked about here, or you can pick a different tool if you prefer. To do this task

1. Pick a topic you know very well and use an AI tool to help you create a presentation about it.
2. Design your evaluation criteria. What should a good presentation on this topic be like? How detailed should it be? What are the main points? What kind of images should it show?
3. Write a good prompt on the tool you chose to evaluate.
4. And finally, evaluate your results and share your evaluation with us here. Did the tool meet your criteria? How much editing would you need to do to improve your presentation? Is this tool worth it overall? Would you use it again?

Activities

Designing Discussion Activities with GenAI

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Written Version:

"The best way to learn is by struggling against the world and succeeding." - Ben Lenk-Ostendorf
Cite it whenever you like; it sounds great, and who needs reliable scientific sources these days?

But on a more serious note, here’s what you need to understand:

1. The most valuable moments occur when learning is a challenge.
2. Creating problems that facilitate this learning process can be daunting and time-consuming.
3. Despite this, obtaining high-quality and stimulating tasks is paramount in fostering effective learning.

The question is: Can we let AI support us with that? We certainly think so. Let's create a workshop to have an intense discussion about a topic. Preparing a discussion might not be particularly challenging for an expert; however, creating a document that initiates meaningful discussions among students is significantly more demanding. Yet, this is where our solution shines - taking often time-consuming preparation and condensing it down to a simple process.

Remember this:

- The complexity of creating a prompt is not the main issue; it's the task of carefully crafting the correct format, selecting quality criteria, and defining what a successful activity looks like.
- The solution here is to find and utilize prior didactical knowledge and bake it into the prompt

Knowledge is still key, but genAI can execute on it. Try it out and enjoy exploring this innovative approach to learning.

Your turn: Let's put this into practice by creating an activity that supports your learning goals, share your activity in the forum. Remember to give feedback to your peers!

Assessment

How to design tests using GenAI

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Written Version:

In this module, we delve into the significance of testing during and at the end of a semester. We aim at assessing the effectiveness of the lectures and whether the students comprehend the information being relayed. It's critical to ensure that tests are a fair measure of knowledge and skills, extending the same chance to every student... but it's also a test for the lecturer: Did you succeed in teaching these students?

One of the ways to achieve this entails basing our test questions on our learning goals. For instance, after a seminar, if the learning goal was for students to critically assess the role of generative AI's energy consumption on climate change, our tests should validate whether this goal has been met.

Here are a few different ideas for generating test questions:

1. Creating a Quiz Question based on the learning goal.
2. Framing a comprehensive Essay Question related to our learning goal.
3. Providing interpretation-centered questions where students analyze datasets pertinent to the topic.
4. Hosting a Debate for an oral examination.
5. Assigning a Policy Brief wherein the students must compile a written assignment or a case study analysis.

The confines of the exam situation can greatly influence the approach to testing. For instance, if the exam is being administered in a lecture hall, the options might be more restrictive. Therefore, the exam design should be adaptative to the real-world scenario. The first information we need is the context of the exam. After this restriction, we can aim to verify our learning goals. You created a workshop, so how would you test your learning goals?

Here is a little exercise for you: The quiz below was developed by an LLM, and it contains some of the classic errors LLMs can make when creating a quiz. Your task is to find the errors. After that, share in the forum below how you can prevent these kinds of errors.

single-choice.h5p

Your turn: Once you have successfully created an assessment using AI, share your experience in the forum. What went well? What didn't? What would you do differently next time?

Assessment for humans

How to design tests for humans

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Written Version:

AI is a Horror for Professors – But Maybe It Shouldn’t Be

Let’s be honest — generative AI has completely disrupted how we design and think about exams. In the old world, students had to do the cognitive heavy lifting: understanding, researching, analyzing. But now, AI can do most of that — often better, faster, and definitely cheaper than a human fresh out of university. That’s a problem.

So what’s left for us to teach? If students learn skills that AI can perform effortlessly, what exactly is their edge when they graduate?

The knowledge itself isn’t the issue — it's widely accessible, just like it’s always been with Wikipedia and academic papers. But now, even processing that information — analyzing, contextualizing, making sense of it — is being handled by AI. So we need to shift our focus.

What I believe we’re seeing is a new type of work. Not just doing a task, but orchestrating it. That means knowing where the information lives, defining the process, using AI to do the grunt work, then coming in to refine and apply judgment.

To help rethink how we teach, I developed a framework called ELITE — it’s not perfect, but it gets the ball rolling. We start with existing exam questions and check: can AI answer this? If it can, maybe it's not testing what we really care about.

Take an example: a multiple-choice question about AI's impact on climate change. AI can answer that. So we ask: what's the real learning goal here? Maybe it’s not just recalling facts, but being able to critically evaluate claims about AI and energy use. That’s a much richer goal — and something we can structure differently.

The ELITE Framework

- Experiment – Can AI already solve the question?
- Learning Goal – What should students actually learn?
- Iterate – Do we need to shift the task or the learning goal?
- Test – Try new questions and see if AI can still solve them.
- Expose – Show students the task, compare their answers with AI, and see where the human value lies.

Sometimes, the right solution is going back to supervised, in-person, paper-based exams for core knowledge. Other times, we need to redefine tasks to reflect this new orchestrator role — where students are evaluating, adapting, and applying AI-generated content in meaningful ways.

The point is not to resist AI — it’s to embrace it smartly. Let’s not fight generative AI but collaborate with it. We should teach students how to use these tools effectively, thoughtfully, and ethically. That’s how we create value, and frankly, that’s what gets me excited again about teaching in this new world.

So yeah — AI is a horror… but it’s also an opportunity. Let’s build something better.

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