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1) In your field of study or discipline, where do you see (or anticipate) the most significant impact of GenAI?

2) What changes might you make to this assignment and/or LOs so that

–Students will use Gen AI critically?

–It is more meaningful to students?

–Measures what it aims to measure?

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Written text carry no value

Result:

Assessment should be very old school, e.g. in class exam at the end, and support students in using AI to help learn

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(1) Lots of broken products. It has already happened in many ways, one of which is vibe coding (https://techcrunch.com/2025/03/06/a-quarter-of-startups-in-ycs-current-cohort-have-codebases-that-are-almost-entirely-ai-generated/). This is despite the sheer low quality of code generated (https://www.youtube.com/shorts/TZt6thN7AU8).

(2) I would not even encourage the use of AI, because the evidence is that students perform much more poorly if they used AI for their homework, even if they started off at about the same level as other students who don't use AI. I only trust proctored closed-book written tests for measuring objective competency.

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A quarter of startups in YC’s current cohort have codebases that are almost entirely AI-generated | TechCrunch

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With the release of new AI models that are better at coding, developers are increasingly using AI to generate code. One of the newest examples is the

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1) In my field, people are exploring AI co-scientist which generates new research hypotheses! Some high-score hypotheses have been tested true experimentally. This is going to change the way people do science, whether we like it or not. I think what I want the students to learn is how we make good use of it.

2) I think I will design some coding exercises that is complicated enough for GenAI to make mistakes. This requires the students to be able to critically thinking about GenAI output, and improve on top.

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I also think that written texts as an assessment only make sense when written in class. If the assessment is in form of a presentation then a Q&A session could help determine whether the students prepared it themselves or not and grade it accordingly. If AI wrote the presentation the students' answers will probably reflect hat - they might not be able to answer very well. Maybe we should have more oral assessments or project work with presentation followed by Q&A in general? Difficult in large groups but possible in smaller classes.

In language learning one of the obvious forms of assessment are oral tests and it's a lot more difficult to help you "cheat" using AI which is why I am not (yet) super worried about the impact of AI when it comes to assessments. However, in class the usage of AI makes students lazy because they don't try very hard e.g. to understand words from context but run everything through some translation tool. One attempt to discourage this is to have the students work in groups so they can first ask a peer when they're "lost in translation" ;)

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There is nothing wrong to use Gen AI to make our work more efficient,

However, written test/assignment could no longer be used to gauge students performance especially if it is conducted offline.

No tools can be used to reliably detect such fraud.

I have few ideas:

Ask students to submit drafts, prompts, or screenshots of how they used GenAI. You don’t need every detail—just enough to see their role in shaping the output.

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At present, I do not feel that AI interferes significantly with language learning assessment. Writing assignments are completed in class under exam conditions. Since text format is part of the evaluation criteria, students must review in advance the different conventions required for each type of text.

Oral assessments are also carried out in class and can take a variety of forms, such as one-to-one interviews, pair roleplays, oral presentations to the class, or group debates, among others.

That said, I believe AI holds great potential for language practice, particularly in preparing for oral assessments. Emerging AI platforms now allow students to interact with applications that provide opportunities to explore language structures and improve pronunciation. This can greatly enhance their communication skills, helping them go beyond the constraints and limitations of in-class practice

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1)

Within the field of visual communication, GenAI will enable capabilities and technical skills that in the past would have been out of reach of students (such as due to lack of knowledge/training, time, or resources). They may be able to better realise the vision they have for projects, which would not have been possible due to the lack of the above-mentioned.

2)

As most of our projects are continuous assessments, we already follow the process and progress of the students weekly. However, with the use of GenAI, perhaps it might make more sense to shift the weightage of evaluation more to the process than the end result. This might allow us to guide more of their capabilities in creative/design thinking and conceptualisation.

Though this reflects how the industry works (i.e. outsourcing or collaboration with others who have the necessary skills to perform specific tasks), the worry is that there would be less opportunities for students to practise and hone their executional craft.

(Reflection)

The topic of assessment brings to mind that if the grading criteria is based on outcomes that could be intangible and subjective, is it even necessary to put a score to it? I could make assessments to whether a student has met learning outcomes or performed to an acceptable level, but could grading of art & design then be simply a pass/fail? Would this lower motivation levels and risk students simply going for bare minimum? Would this be fair to the students who exceed expectations? How could grading be more equitable?