ALP Guide: Academic writing in the age of AI

Tips and tricks

• Podcast ‘Nooit meer schrijven?’: conversations about AI and writing

  In 2023, as part of a project funded with a SoTL grant (Scholarship of Teaching and Learning) from the VU's Centre for Teaching and Learning we created a podcast series: Nooit meer schrijven? In each of the seven episodes, Gea Dreschler, director of the ALP, talks to a guest about one of the many topics related to AI and writing skills. Guests include Piek Vossen, professor of Computational Lexicology, who explains the Large Language Models that ChatGPT is based on; Christine Moser, Associate Professor of Organization Theory, who reflects on the role of writing in education; and Maaike van den Haak, who talks about the opportunities and threats that machine translation presents. Episodes are in Dutch (with a translated transcript) or English.

• The 'Writing Pie': mapping writing skills

  Writing an academic text is a complex cognitive task that requires not only the ability to produce complex and accurate sentences, but also a sophisticated knowledge of audience and genre conventions. And at the same time as thinking about the text itself, the writer needs to develop ideas, understand concepts, and engage with source materials.

  As part of the SoTL-funded project, Gea Dreschler, Abby Gambrel and Jens Branum conceptualised the writing process in a 'Writing Pie', with the different skills required to write an academic text displayed in each slice of the pie. You can find a handout version of the pie here.

  Mapping writing skills this way gives a broader perspective on the value of writing: writing is not just about the end product, but it is also a tool for thinking, for understanding, and for putting thoughts into words. Another benefit of the Writing Pie is that it allows for a more detailed look at the potential issues that GenAI writing tools present to writing assignments. For this purpose, we added the outer ring, which indicate our assessment of the ability of AI to replicate these skills (at least on a surface level).

  The Writing Pie, and a case study of how the writing pie can be used to rethink and redesign writing assignments and in-class exercises, was published in the Journal of Academic Writing.

• Key takeaway: three things everyone should know about GenAI

  We have learned a lot in our explorations of Generative AI and the writing tools that have become available. In our conversations with teachers and students, we’ve come to the conclusion that there are three fundamental aspects of Generative AI tools that everyone should know in order to use the tools responsibly.

  1. Input = output. Language models are trained on existing texts. This means that GenAI tools don’t just make something up, but 'learn' from a huge database of texts. The more people have written about a topic, the better GenAI tools work. It also means that it is important to know what is in that database of texts, but this is precisely something that, for instance, OpenAI does not disclose.
  2. Large language models are based on probability. They calculate what the most likely next letter (and word) is based on what is already there. This means that GenAI tools are especially good at common combinations of words and phrases. But it also means that their ability is very superficial: they produce combinations of words and sentences that are commonly used about a topic or in a specific type of text, but the tool doesn’t actually understand what it writes.
  3. Programming is human work. Even though GenAI tools may seem like autonomous intelligent computers, there is a lot of human programming involved behind the scenes. For instance, the interactive part, so this sense that a chatbot like ChatGPT actually talks to you, is programmed as a sort of extra layer on top of the language models output. It’s good to realise that this is a deliberate choice by the programmers.

• For teachers: spotting AI writing 

  In 2025, we received money from the VU's language policy funds to further develop our materials for teachers and students about GenAI tools and writing. One topic that we examined was the question of how teachers can spot texts that are generated or heavily influenced by AI.

  There is in fact no guaranteed method to detect whether a text was generated entirely, or largely, by an AI tool, and that includes the use of AI detectors. Indeed, the VU strictly forbids the use of AI detectors because they are unreliable and do not meet the VU's privacy requirements. But knowing some of the common traits of AI-generated texts can help you spot them. These tendencies aren’t inherently bad and can also be present in human writing, so they aren’t a way to detect AI fraud or say that a text is bad writing. And as the technology is continuing to change, so will the features of AI writing. But thinking about these features can help you think about what you value in writing and what kind of writers you want your students to become.

  So, we have looked into the research on tendencies of AI writing and created a list of common clues and traits. This list doesn’t represent VU policy. It’s only intended as a helpful aid for teachers. You should always check the university and programme guidelines when thinking about potential AI misuse.

  You can find out more about these in our in-depth guide to spotting AI use in student writing here. We are also working on a Canvas course with activities to practice spotting AI writing. Send us an email at alp.sgw@vu.nl if you would like to be added to that course when it is ready.

  Here are some clues that a text might have been produced by an AI writing tool:

  Linguistic clues

  • Repetitive sentence structures
  • Repetitive vocabulary
  • AI-favoured vocabulary
  • A more confident and less cautious tone   
  • No or very few errors

  Structural clues

  • Headers
  • Uniform text patterns
  • Fewer different communicative purposes per text

  Content Clues

  • Hallucinated sources
  • Unexpected sources
  • Lower citation frequency
  • Emphasis on information instead of argumentation

  Qualitative clues

  The following clues were identified through more qualitative research and are based on expert and instructor opinion rather than empirical evidence.

  • Claims beyond likely student knowledge
  • Broad statements
  • Inaccurate or fake information
  • Not meeting assignment requirements
  • Omitting class content
  • Bland writing style
  • Overly poetic language 

• For teachers: reviewing your writing assignments

  A common question that is raised by teachers is whether they can still set writing assignments for their students; and if so, how they can best redesign their assignments. In 2025, we looked into this question as part of a project funded by money from the VU's language policy funds.

  Here's a quick road map to reviewing existing writing assignments:

  1. Take a writing assignment that you currently are using in one of your courses. Which of the subskills from the 'Writing Pie' are part of the learning goals of the assignment?
  2. Consider to what extent GenAI tools are a threat to assessing this skill through writing. Can a student have a GenAI tools carry out the assignment very easily? Will it be difficult for you to see if the student has done it themselves?
  3. Take the information from steps 1 and 2 and use it to revise your assignment.
     • If the goal for the student is to gain knowledge and you assess this through a writing assignment, then given the ease with which GenAI can mimic such an assignment, a writing assignment is not a wise choice. An oral examination or a written exam on location can be a good solution here.
     • Is learning to write part of the learning objectives? Then see if you can make the assignment less vulnerable to AI. For example, you might make it more specific, so that it can be imitated less easily by GenAI. Or you could think about assessing subskills: for example, a student can show through an argumentation scheme or text plan that they can divide a text logically. You can find more in-depth of explanations of these tips on our page about assessing and developing student writing skills in the age of AI. We also cover the following tips:
       • Break longer projects into smaller steps
       • Use in-class hand-written assignments
       • Create authentic assignments
       • Check that assignment instructions are accessible and engaging
       • Include personalised/reflective elements in the assignment
       • Relate the assessment closely to the classroom content
       • Incorporate group work
  4. In revising writing assignments, it is important not only to go through these steps for specific courses, but also to consider them at curriculum level. If a student has to write a thesis, writing skills should be addressed somewhere in the curriculum.