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Why you should still write yourself at university

Artificial intelligence means that previous teaching and examination methods as well as competence expectations at universities need to be renegotiated. Why should students even bother with a laborious writing process when the AI can generate texts on command? We shed light on writing at universities as a learning object and tool for thinking and demonstrate its immense value for educational processes – also in combination with artificial intelligence.

The importance of writing in university teaching

What functions does writing fulfill for the acquisition of competences at university?

Texts are the central means of communication in science. At the same time, writing texts is a thinking tool that should not be underestimated, as it can help to generate new ideas, network knowledge, and train critical thinking. This means that writing not only transports knowledge and serves information, but also fulfills an epistemic-heuristic function. Writing – including calculations, creating drawings, CAD, and other visualizations – is therefore important for all subjects in order to simultaneously develop subject-specific content and train and deepen technical thinking.

In addition to term papers and theses, which train academic writing, other types of texts such as portfolios, seminar and experimental protocols, internship, practical and reflection reports, documentation, and other practical written examination forms are also important at TH Cologne. Text types are patterns that writers use as a guide when designing the form and content of a text and that readers apply to a text as an expectation of its form and content. By reading and writing different types of texts, students learn how questions are asked and problems solved in their subject area. Your knowledge of text types will also enable you to adapt more quickly to new communication situations in both academic and professional practice. Writing in the university context in particular is characterized by a complex cognitive process that requires students to develop individual (writing) strategies. Academic work and writing require an interplay of different sub-skills (information, reading, and writing skills, but also generic sub-skills such as self-regulation or digital skills) as well as knowledge about content, language, or addressees (see figure). Thus, scientific writing aims at higher levels of learning such as analysis, synthesis, and evaluation and must be learned and taught step by step, without and in the future also with AI.T From our point of view, both should be integrated into teaching, not as additional teaching material, but as a tool in teaching practice, which specifically trains technical and metacognitive skills and thus represents a benefit for all parties.

Competences in the academic writing process (Hoksch et al. 2022: 47)
Fig.: Competences in the academic writing process (Hoksch et al. 2022: 47)

To what extent does the use of AI in writing change the acquisition of competences?

The use of large language models (LLM) and AI-generated texts shifts the skills required for academic writing: Writers no longer write texts from scratch, but instead instruct language-processing AI to generate text and check, assess, and revise the AI-generated text elements. This phenomenon is already known in the sociology of technology (Rammert, Schulz-Schäfer, Latour, and others) and cybernetics as a shift from executive actions to human-machine interaction, as illustrated by the use of navigation devices: the machine calculates a route specified by a human. The person checks whether the planned route is realistic and meets their needs and then follows the machine’s suggestions. There is potential in this shift of competences, but it also harbors uncertainties and risks. The purpose of academic writing – even with AI – remains a person’s cognitive interest, i.e. the epistemology of the writing process. For writing at university and subsequently in professional life, the use of the THKI GPT-Lab can open up new opportunities for competence development, also concerning future skills (such as technological, transformative, or ethical competences), but this must also be practiced and reflected on together with students.

How can AI be used beneficially and responsibly for academic writing?

The writing process is shaped by academic conventions, individual strategies, and previous writing experience, but also by the tools and technologies available to create text: Notebooks, computers, and now machine intelligence can influence and relieve cognitive processes, freeing up capacity for other mental tasks. It can therefore be assumed that AI tools will influence writing and thus also scientific practices and vice versa. The extent to which academic practices and concepts of authorship will change cannot yet be predicted. The basis for scientific texts continues to be the principles of good scientific practice: The author is responsible for their own text and must deal transparently and critically with their own and others’ research and with the way the results are achieved. This makes it all the more important for you as a teacher to have students practice technical writing in your teaching so that, for example, the importance of complete and correct citation of sources, the correctness of calculations, the conformity of technical drawings and CAD representations to standards, etc. becomes clear to them. It is up to you to convince students that this is not a boring compulsory exercise or a superficial phenomenon to be neglected: Users are not only responsible for the formal correctness of the text generated by the machine. Of course, people also remain the responsible authority, for example to check,

  • whether a research question in a term paper is suitable,
  • whether the connection between several working hypotheses is correct,
  • whether the chosen method is suitable for answering the research question,
  • whether individual statements in the generated text are correct or incorrect – this also applies to visualizations–,
  • whether generated statements are comprehensible for the audience and meet the expectations of the respective readers, i.e. the text type,
  • whether the structure and arguments are coherent,
  • whether sources specified by the machine exist at all,
  • whether the sources relevant to the topic are included in the text, and
  • whether the sources cited meet scientific and possibly even ethical standards.

As things currently stand, it is still essential for students to be able to master all components of the writing process without AI tools in order to use them in a targeted and reflective manner. AI tools can make the writing process easier or more efficient in many areas. Students can explore the familiar AI tools under your guidance concerning the following: Obtaining suggestions for finding topics/generating ideas, ideas for structuring texts, feedback on the self-written text in terms of spelling, punctuation, layout, citation rules, possibly expression, or even content.

One risk could be that students are overwhelmed and not trained in academic writing, especially when writing their first academic paper during their studies (in some subjects in the first semester, in others only with the final thesis). You could then have entire texts generated by ChatGPT and copy them without critically checking the formal and content-related aspects mentioned above. This raises the question for lecturers as to whether students who are now enrolled in a degree course, just like their fellow students in higher semesters, still need to be able to write a scientific text without generative AI. Alternatively, the question arises as to whether teaching and examination methods need to change fundamentally, for example by incorporating an oral examination or defense of written work. This could remain the basis for checking other people’s texts for their scientific accuracy and ultimately being able to critically assess and evaluate the information provided. This competence remains one of the essential skills that students acquire during their studies and need for their later professional life, especially as citizens of a democratic society. What does this then mean for teaching and learning at the university?

Instructions for practice

The further development of AI is very dynamic and it is therefore advisable to stay up to date. At the end of the article, you will find recommendations for current publications and videos on the topic. At the moment, a lot of research projects and their results are still pending in order to be able to make well-founded recommendations for practical application. As things currently stand (March 2024), however, we would like to give you the following five practical suggestions.

The dynamic technological, social and scientific developments make it impossible for teachers to acquire specific AI knowledge before implementing it in their own courses. The dynamic technological, social, and scientific developments make it impossible for teachers to acquire specific AI knowledge before implementing it in their own courses. For example, ask which AI tools your students use and what they use them for. Report on your experiences and test together with your students what AI tools could be helpful for and where weaknesses or serious problems lie. It could also be helpful to provide students with information about LLM training so that they understand why AI is not suitable for acquiring specialist knowledge. In this context, you could also address data protection, copyright, and ecological problems (such as the enormous computing power) as well as ethical issues such as opaque training data and precarious low-wage work for tech companies. On this basis, students can make reasonable decisions and learn how to use technology responsibly.

It is often difficult for students to understand why they should learn academic writing. This effect can be intensified if you do not integrate writing during the semester but outsource it to examinations during lecture-free periods or self-study phases. Students are then often cognitively overwhelmed and perceive writing as something independent of the content of their studies. You can convince students of the importance of learning to write, especially if you introduce them to the benefits of epistemic-heuristic writing at an early stage. As a teacher, you can guide this effectively by using writing as a means of understanding specialist ways of thinking and working in your courses.

Smaller writing exercises, such as the One-Minute Paper, can be helpful to activate prior knowledge, summarize learning units, or generate questions and thus make it possible to experience writing as a thinking tool. Targeted writing exercises such as the Blitz-Exposé or larger writing assignments that productively integrate aspects such as a specific situation, a relevant text type, and the reference to the addressee can contribute significantly to dealing with subject-relevant problems and thus make it possible for students to experience the link between content and ways of thinking (calculation, visualization and writing methods) in a meaningful way. Such a task could be prepared within the session through free writing (with pen and paper) in order to then consider together how and for what purpose AI tools could be used, for example, to obtain feedback on the text and to revise one’s own text on this basis before submission.

Discuss how you yourself proceed when reading and writing specialist texts. This can help students to understand that the writing process is complex, that a good text takes time, that writing needs to be practiced and that one’s own approach needs to be reflected upon. This can help students to understand that the writing process is complex, that a good text takes time, that writing needs to be practiced, and that one’s own approach needs to be reflected upon. Of course, not only successful prompt engineering, but also the aforementioned aspects of methodology, research, source checking, research focus, argumentation, etc. must be taken into account here – in some cases, these aspects can be considered in prompt engineering. Together with your students, analyze the style of texts from your subject so that students become more aware of technical writing styles and do not just scan texts for content.

The basis for academic writing should continue to be good academic practice. It is currently to be clarified by the respective examiners (as of March 2024 at TH Cologne) and also to be made transparent whether the use of AI is a permissible aid for a particular examination. If it is permitted, the use of AI must be included in the declaration of originality. It must be made transparent in which way and for which work steps AI was used, and it must be emphasized that the author of a text is still responsible for the text to be submitted within the framework of the respective examination regulations. It is also your responsibility to clarify how digital exams are handled on TH PCs with different clearance for tools: A) without aids, B) with limited aids, C) with all possible aids. In addition to the epistemological dimension, a scientific text and every other form of examination in a degree course always has a legal dimension.

The Center for Academic Development (ZLE) and the Writing Center each offer (and also collaborate on) various training courses on the topic of writing and AI or AI in university teaching. The Writing Center also offers individual consultations for teachers on writing-intensive teaching. In addition, students can attend workshops at the Writing Center, benefit from writing consultations, and we will soon be launching writing groups in which students can try out writing scenarios and AI tools together with other students and are accompanied by trained peer writing tutors. You could refer your students to the services offered by the Writing Center, the Competence Workshop, the Language Learning Center, and the library (e.g., for literature research) or use cooperation with these institutions for your teaching.

Links & Literature

Header-Image: © memorystockphoto/Adobe Stock

  • Mirela Husić

    Mirela Husić is a writing consultant at the Schreibzentrum at TH Köln. She supports students and doctoral candidates in their writing through consultations and workshops. She also offers further training and individual consultations for lecturers on writing-intensive teaching.

  • Prof. Dr. Alexander Holste

    Alexander Holste (ORCID 0000-0001-5908-1587) is Professor of Multilingual Technical/IT Communication at TH Köln. Venia Legendi "Applied Linguistics" (University of Hildesheim, 2023), Dr. phil. (2019), studied at the University of Duisburg-Essen and Università degli Studi di Torino. As a specialist communication scientist and knowledge communication researcher, he investigates specialist multimodality, specialist language-based human-machine interaction and multilingualism in specialist contexts. He was a board member of the Gesellschaft für Schreibdidaktik und Schreibforschung e. V., publishes the series "Wissenskommunikation: maschinell - mehrsprachig - multimodal" and cooperates in practical projects with companies and administrations.

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