GenAI and AI literacy for teachers: an introduction

Who it is for: teachers who want to know what AI literacy means for themselves, the way Short 1 asks it about their students

Length: 90 minutes · Group size: 8 to 24. The session turns on two conversations in pairs and a list the group makes itself; above 24 that list becomes unreadable.

Prior knowledge: none required, and the session opens by asking who has already done what.

Language: the slides are in English, the conversation need not be. Ask in the first minute; slide 3 puts the question there itself.

You need: a board or wall that lists can go on, and a laptop per pair for the closing exercise.

What participants can do afterwards

• Explain what AI literacy means for them as a teacher, and why it bears on their own subject.
• Take apart the components of a framework such as the VU-UvA Taskforce one and hold it against their own practice.
• Name the main dilemmas around the effect of generative AI on learning, teaching and assessment.
• Judge their own knowledge, attitudes and skills, and say where they want to go further.

How this differs from Short 1

Short 1 is about what students can and cannot do. This one is about the teacher. That sounds like a small difference and it is not: in Short 1 the room looks at someone else, here it looks at itself. The self-assessment halfway through is the hinge of the session, and everything before it exists to give that assessment meaning.

The two can be given independently. Anyone who follows them in order will notice that the list from Short 1 and the list from this one overlap in places and collide in others. That is usable material.

What to arrange beforehand

• Read the chapter titles of both Companions. They are on slide 10 and they are the material for the self-assessment. Know which ones are new: the student edition has three that were not there before, on environmental impact, on work and society, and on an AI-aware future.
• Test the two addresses the closing exercise uses. The Teacher Edition and the chat. Do it on the day, not a week before.
• Clear the board. The opening exercise produces a pile of lists and they have to fit side by side.
• Look through the Taskforce advice. The three categories are on slides 6 to 8 with their components. You do not have to read them out; you have to know where they are when someone presses.

Running order

• 0 to 10 minutes. Welcome, choice of language, and a survey of who has done what with generative AI. That last part is not a formality: it decides how you handle the rest.
• 10 to 25 minutes. In pairs: what would AI literacy mean for a teacher, and what would you then need to know and be able to do? Make a list, put it on the board.
• 25 to 50 minutes. Set the Taskforce advice beside it: knowing, doing, and judging.
• 50 to 70 minutes. Self-assessment against the chapters of both Companions, then a conversation in pairs about why you rated yourself that way.
• 70 to 85 minutes. At the laptop: look up the key concepts in the Teacher Edition or through the chat, and compare what you find.
• 85 to 90 minutes. Close.

The opening exercise

Let the group work out for itself what AI literacy means for a teacher, in pairs, and have each pair put its list up. Give no examples first. The value is in the list being the group's own; the moment you name the first three points, everyone writes those down.

What usually goes missing is the Taskforce's third category, the one about ethics and judgement. Groups name what they should know and what they should be able to do readily enough, and rarely when they should not do something at all. That gap is the best way into the next part, so point at it and leave it open.

The Taskforce advice beside it

The VU-UvA Taskforce divides AI literacy into three categories, and the slides follow that division.

• What, knowledge and understanding. How a language model works, what data does to it, what machine learning and algorithms are in broad terms, and which applications exist in your own field.
• How, understanding and application. Recognising what kind of application you are dealing with, weighing when use is worthwhile, going on thinking and writing yourself, choosing which source to consult, and prompting deliberately.
• Why, ethics and judgement. Being able to argue both ways, testing output against the standards of your own field, copyright and attribution, reflection within your discipline, and privacy.

Put the group's list next to it, literally. The question is not whether the group got it right, but what appears on one list and not the other, and what that says.

The self-assessment

This is the heart of the session. Participants read the chapter titles of the Student Edition and the Teacher Edition, and rate their knowledge and skill for each. Then they discuss in pairs why they rated themselves that way.

The rating does not matter, the conversation about it does. Steer towards how someone knows they can do a thing, because that is where the rating becomes concrete. And let people pick their own partner; they are more honest about what they do not know with someone they chose.

Looking up the key concepts

The closing exercise puts participants at a laptop, in pairs, with two ways in: the Teacher Edition itself, or the chat that searches the same text. Have them pick a concept they rated themselves low on and look it up.

That the two give different answers is not a fault but the lesson: searching a book and questioning a chatbot produce different things, and which you want depends on what you are after.

What goes wrong

The Taskforce section eats time. Three categories with sixteen points between them read like a list to be worked through, and that is what not to do. Pick two points per category that connect to the group's own list and leave the rest on the slide.

The self-assessment overruns if you read the chapter titles aloud. Put them on the board or leave the slide up, and give the group five minutes of quiet.

The VU CTL Shorts series

This is the second Short in a series from the VU Centre for Teaching & Learning. Slide 2 shows the whole series, in four groups.

• Knowledge and skills: students' AI literacy, this introduction for teachers, powerful prompting, and prompting for beginners.
• Core concepts: the impact of generative AI on learning, on academic integrity and on assessment.
• Designing and strengthening education: generative AI for educational design, feedback and grading, retrieving and processing information, AI roleplay, and Socratic bots, quiz bots and debate bots.
• Broad and societal impact: language models and technical applications, environmental impact, and societal impact.

Material and provenance

• Based on the slides GenAI & AI Literacy for Teachers: an introduction, VU Centre for Teaching & Learning, 13 slides.
• Download the slides (PowerPoint, 487 kB). The slides are in English and carry no speaker notes; this plan is a reconstruction of what happens between them.
• The running order is derived from the programme on slide 3 and not from a session that has been given. Once this Short has been run, a section belongs here on what went differently in practice, as there is for Short 1.
• The Taskforce advice is also in the Teacher Edition of the Companion, in section 1.2.