Change in the digital advertising world through AI

One of the most important developments is the deep integration of AI into advertising platforms such as Google Ads and Meta Ads (formerly Facebook). Creating ads used to be a manual process that involved writing copy, selecting images and defining keywords. Today, AI-supported systems perform many of these tasks automatically. The advantages of these automated processes are efficiency and the ability to tailor content to individual users. Nevertheless, challenges remain: The human communicator must check and, if necessary, adapt the generated content to ensure that it meets quality requirements and target group needs, ensures data security and takes ethical aspects into account.

The role of AI as co-author and partner

Collaboration between humans and AI takes place in the form of so-called "human-autonomous teams" consisting of human editors and autonomous systems that work together on communication goals. The collaboration can be described as "adversarial cooperation". The AI generates content based on machine learning and data analysis, while the human editor critically evaluates and edits these suggestions. This process requires a high level of editorial expertise as well as a basic understanding of how the technologies used work. This raises several questions: To what extent can and should AI-generated content be trusted? What control mechanisms are required to ensure that the content complies with ethical and professional standards? These questions are particularly relevant in the context of marketing, where the presentation of one's own brand and the handling of user data play a central role.

Target group analysis and structured content as a common basis

Target group analysis is a key aspect of AI-supported content generation. In both marketing and technical communication, it is crucial to know the target group precisely and understand their needs. Modern platforms use large amounts of data to analyse profiles and interests and personalize content based on this. The process works as follows:

1. Data collection and analysis: Platforms continuously collect data about user behavior. This includes information such as browser history and search queries, interactions with content (e.g. likes, comments and clicks), information from user profiles (e.g. interests and geographical locations).
2. Machine learning for profiling: Based on this data, the platform uses machine learning to create a comprehensive user profile. The profile contains information about which topics and products are relevant to the user. This is a combination of explicitly stated interests (e.g. fields of interest in a social media profile) and those that are implicitly inferred through behavior.
3. Personalization of content: Ads and other content are automatically adapted based on these profiles. For example, a platform such as Google Ads analyzes the content of a landing page and creates keywords, headlines and descriptions for advertisements based on this. At the same time, it determines which user group is most likely to be interested in this offer.
4. Automated decision making: The platforms perform a dynamic combination of ad elements that are individually compiled for each user. This content then appears in various formats (e.g. text ads, video reels or banner ads), depending on the user's preferences and behavior.
5. Optimization through continuous feedback: The platforms evaluate user interactions with personalized content in order to further improve their algorithms. For example, click rates and dwell time can be used as indicators of the relevance of the content.

Platforms such as Meta offer AI-powered targeting, while Google generates AI-powered suggestions for ad elements. However, these technologies can provide inaccurate or inappropriate suggestions, so critical editing remains necessary.

Importance of structure in marketing and technical communication

In addition to target group analysis, structuring content is an essential part of both disciplines. Well-structured content not only improves the user experience, but is also crucial for search engine discoverability. The methods and techniques used are similar, such as a clear hierarchy of headings and subheadings, the logical arrangement of text and visual elements, and the division of information into easy-to-understand sections.

In technical communication, such structuring is particularly important to make it easier for users to find their way around complex documentation. It enables readers to search specifically for relevant information and grasp it quickly. The situation is similar in marketing, where content must be designed in such a way that users can grasp the core messages of an advertisement or website at first glance.

Findability by search engines (SEO)

Structured content also plays a crucial role in search engine optimization (SEO). Platforms such as Google use algorithms that favor structured content because it ensures better indexing and presentation in search results. Search engines analyze the structure of the website to find out which content is particularly important (e.g. headings and meta descriptions). A clear and consistent structure enables search engines to better classify the relevance and subject areas of a website and increases the likelihood that users will find and select the website in the search results. This plays a particularly important role in digital marketing, as placement in search results often determines the success or failure of an advertising campaign. A landing page that is optimized for search engines attracts more visitors and achieves better conversion rates

Both technical communication and marketing campaigns benefit from a well thought-out strategy that is geared towards the needs of the target group. However, there is a key difference in the objective: while technical communication aims to provide users with information and instructions, marketing focuses on persuasion and promoting calls to action (e.g. product purchase, contact).

Technological developments and new requirements

The integration of AI and machine learning into technical communication requires a rethink in practice and training. Three core aspects are of particular importance here:

1. Editorial control: When working with AI, technical communicators take on an increased role as editors and decision-makers. This means that they no longer just create content, but are also largely responsible for deciding which AI-generated content is adopted, changed or discarded. Technical communicators must therefore be a kind of "counterpart" to the AI by evaluating and correcting the content and thus ensuring its quality and relevance for the target group. This role as a critical editor is crucial for compliance with technical standards.

2. Changed work processes: Platforms such as Adobe and MadCap Flare will increasingly integrate AI functions. These developments are fundamentally changing work processes and tasks such as text creation, automated formatting and content management are becoming much more efficient. The role of technical editors will be increasingly shaped by technology. They need to familiarize themselves with the new platforms, understand their functionalities and learn how to integrate them into their daily work. and develop the ability to use these tools effectively and critically evaluate their suggestions.

3. Ethical issues: Working with large amounts of data raises questions about data security and the protection of user rights. Technical editors and marketing experts also have a responsibility to protect user data when using these platforms. They must ensure that the content generated on the basis of this data does not violate users' privacy or lead to discriminatory practices. For example, there is a risk that certain user groups are not taken into account in personalization due to missing or distorted data profiles. This could mean that important content is not visible to these groups or that their needs are not sufficiently taken into account in the content strategy.

To meet these challenges, clear ethical standards and guidelines are required, as well as a critical and reflective approach to AI systems in order to take into account the long-term impact on user experience and social responsibility.

The increasing integration of AI into content management systems (CMS) and other digital platforms requires new skills from specialists. The key skills that should be taught in training include

1. critical thinking and editorial competence: the ability to evaluate, adapt and optimize AI-generated content.

2. technical understanding: a basic knowledge of how AI and machine learning work in order to be able to assess their potential and limitations.

3. data literacy and ethical awareness: knowledge about handling large amounts of data and compliance with ethical standards.

Challenges and opportunities

AI integration has far-reaching implications for the practice of technical communication and marketing. While automated processes bring efficiency gains, human expertise remains essential to maintain content and ethical integrity. Professionals must learn to work productively with AI systems and use them as tools to support their work. It is important to drive forward the development of standards and guidelines to ensure the sustainable and ethical use of these technologies.

Ecodesign Regulation for Sustainable Products

With the Ecodesign Regulation for Sustainable Products (ESPR) (EU) 2024/1781, the European Union has set the legal framework for defining ecodesign requirements that are intended to help improve key product aspects and which is a decisive step towards promoting the sustainable production and use of products. The specific product groups to which the requirements relate will be specified by delegated acts.

The ecodesign requirements (Article 5) are intended to ensure that products are designed to be more environmentally friendly and sustainable throughout their life cycle. The most important requirements include:

• Durability and reliability
• Reusability and reparability
• Upgradeability and possibility of maintenance and refurbishment
• Presence of substances of concern
• Energy,water and resource efficiency
• Conservation of resources and recyclability
• Environmental impact including footprint
• Minimizing the amount of waste

The scope of the Ecodesign Regulation for Sustainable Products (ESPR) includes:

• All physical products, including their components and intermediate products.
• Exceptions: Food, animal feed, living beings, vehicles and medicines.

Long-term goal: The regulation is to be extended to all physical products in the medium term.

The regulation is aimed at a large number of stakeholders as customers and economic operators whereby new groups as for example professional repairers and independent operators are defined. To the group of independent operators belong refurbishers, repairers, as well as publishers of technical documentation.

A key component of the ESPR is the provision of comprehensive product information to ensure both compliance with legal requirements and the sustainability and recyclability of products. This includes:

• Digital Product Passport (DPP): The aim is to provide product-specific information transparently.
• Labels: Products should be labeled accordingly to enable customers and market surveillance authorities to better assess their sustainability.

According to Article 7, information about the product on its performances and about substances of concern are required. Information about all activities of the whole product life cycle and after-life cycle  must be provided in addition to the relevant instructions for the safe use of the product. The information must be clear and understandable, tailored to the different target audiences.

Digital product passport

The Digital product passport (DPP) is a key element of the new EU strategy to promote sustainable products. Products are only complete with environmental sustainability information.  It is intended to provide digital access to comprehensive product-specific information relating to sustainability, the circular economy and compliance with legislation. It will provide information on products, components and materials. A planned EU DPP registry  should be accessible to all economic operators, all supply chain actors and consumers as well as market surveillance authorities and customs authorities. A web portal is planned to be set up for consumers and stakeholders to facilitate access to this information.

• The requirements of the digital product passport are defined in the ESPR. Other product legislation amongst others, as the Construct Products Regulation or the Batteries Regulation (EU) 2023/1452 contain applications of the digital product passport

Information requirements of the DPP

• The DPP contains relevant product-specific information on sustainability, circularity and legal compliance.  The DPP requirements are described I Chapter III and Annex III of the ESPR. The DPP must contain detailed product information about sustainability aspects as given in Annex I of the ESPR. It also includes technical documentation, declaration of conformity, certificates, user manuals, instructions, warnings, safety information and tracking information. The given information must also comply with the applicable EU product legislative act.

New Construction Products Regulation

The new Construction Products Regulation (CPR) (EU) 2024/3110 will replace the existing CPR 305/2011 in the long term. It entered into force on 7 January 2025 and will apply as of 8 January 2026. Interestingly is that elements of the ESPR are incorporated in the Construction Products Regulation 2024/3110. Especially the Digital product passport will be essential.

Right to Repair Directive (EU) 2024/1799

The Right to Repair Directive, which came into force in 2024 aims to empower consumers to make sustainable choices by providing comprehensive repair information and finding a convenient repairer. With this directive the B2C market is addressed. The scope of this directive, listed in Annex III covers a limited range of products, including household appliances such as vacuum cleaners, washing machines, refrigerators, electronic displays and cell phones.

A central element of the Directive is the European Repair Form, which is regulated in Article 4. This form provides information on the type of repair, the costs and the repair time. The aim is to enable consumers to make informed and sustainable decisions.

In addition, the European Repair Platform (Article 7) will be introduced, where consumers can find repairers, sellers of refurbished goods and buyers of defective goods. The aim of this platform is to facilitate access to repair services and makes it easy to find repairers, sellers of refurbished goods and buyers of defective goods.

The Directive also mentions vulnerable consumers and consumers with disabilities.

Green Transition Directive

The Empowering Consumer for the Green Transition Directive (EU) 2024/825 is another legislative act in the B2C-context. It aims to provide consumers with better information on the environmental and social characteristics of products and to protect them from unfair practices such as greenwashing.

The core aspects of this directive are

• Prohibition of greenwashing and misleading information about ecological and social product characteristics
• Consistency of environmental information across all information channels (websites, instructions, labels, catalogs)
• Obligation to document and verify environmental claims

For technical communication, these regulations mean that they must ensure that all information provided is clear, correct and consistent. Violations of the requirements can result in severe penalties for economic operators.

The directive highlights the importance of transparency and accurate information in the digital age and in industrial ecosystems.

A Europe fit for the digital age

As part of the "Europe fit for the digital age" action plan, several new pieces of legislation and supplementary regulations have been enacted, including a digital law on the sharing of information and data. These highly complex regulations are particularly important for technical communication, as their implementation will have a significant impact on how information is provided and exchanged in the future and, above all, requires the provision of comprehensive information and instructions for the user. The Artificial Intelligence Act and the Cyber Resilience Act are two for technical communication important examples.

Artificial Intelligence Act

The European Union (EU) Artificial Intelligence (AI) Act 2024/1689 is a far-reaching and detailed regulation that aims to make the development and use of AI systems safe and transparent. The act defines various stakeholders who play a role in connection with AI systems:

• Provider: This is a person, company or organization that develops an AI system (Article 3(3)).
• Deployer: A person, company or organization that uses an AI system under its supervision (Article 3(4)).
• Economic operators: Persons or organizations involved in the development, sale or operation of AI systems.
• Affected persons: These refer to those who are affected in some way by the use of AI systems.

An important aspect of the AI Act also concerns safety components (Article 3), which are defined as components of a product or AI system that fulfill a safety function. This is important because the malfunction of these components can endanger the health and safety of persons or property.

The risk-based approach: classification of AI systems

The AI Act takes a risk-based approach to regulating AI systems. AI systems are divided into different risk classes, with the highest risk level being associated with strict requirements. An example of a high-risk AI system is AI-based medical software, which is subject to special requirements regarding user information and safety.

Another key issue is transparency: certain AI-generated content must be clearly labeled as such, and chatbots in particular must clearly inform users that they are interacting with an AI. This should protect users from possible misunderstandings and false assumptions.

A particularly important point of the act is the ban on social scoring by governments or companies. This practice is seen as a significant threat to people's fundamental rights and is therefore strictly prohibited in the EU. Mass surveillance by state or private actors also falls under these prohibited practices, unless there are specific exceptions such as in the area of counter-terrorism.

The act also places specific requirements on high-risk AI systems, which are also relevant for technical communication:

• Technical documentation (Article 11): Before commissioning a high-risk AI system, detailed technical documentation must be prepared and kept up to date.
• Record-keeping (Article 12): Automatic recording of events during the lifetime of the system must be technically possible.
• Instructions for use (Article 13): AI-systems shall be provided with instructions for use, which shall be made available in an understandable digital format or by other means. These instructions must contain clear, complete and relevant information, including the characteristics and limitations of the system and its safety aspects.

legislation as part of the New Legislative Framework and other EU harmonisation legislation.  

Cyber Resilience Act

In the scope of the Cyber Resilience Act (CRA) (EU 2024/2847 are products with digital elements. These products include a direct or indirect logical or physical data connection to a device or network:

• Hardware products and components: Examples are laptops, mobile phones and network equipment, in other words connectable devices
• Software products and components: Examples are operating systems, games or mobile apps which can be accessed by e-mail, stream, download or other ways of access.

In the scope fall also remote data processing solutions if performing a product function. Products must have an appropriate level of security. The information and instructions to the user shall give clear, understandable information about the product with digital elements, its secure use and security-relevant information. A “new” type of information is described in the CRA, namely the Software Bill of Material (SBoM) which is a formal record containing details of components included in the software of the product with digital elements.

Annex I of the CRA describes the essential cybersecurity requirements, which contain an appropriate level of security, security aspects which are based on the cybersecurity risk assessment and vulnerability handling requirements (e.g. information about fixed vulnerabilities and free of charge security updates.

Information obligations and technical documentation

Manufacturers, importers and distributors shall ensure that their products are provided with the necessary instructions, either in paper form or electronically. These instructions must be clear, understandable and legible, written in a language that can be easily understood by users and market surveillance authorities. Like in other product legislation, manufactures shall draw up the technical documentation including user information and instructions.

Machinery Regulation

The Machinery Regulation (EU) 2023/1230 which entered into force in 2023 and shall apply as of 14 January 2027 will replace the Machinery Directive 2006/42/EC on that date. In other words, the Machinery Directive is still applicable. Together with the Machinery regulation,  the manufacturer has to take into account legislative acts linked with Green Deal legislation and legislation of the EU digital age, based on the product specifications and the target audiences. The provisions of the Machinery Regulation (EU) 2023/1230 are applicable for the B2B and the BC2-market. In the perspective of the European Green Deal, the ESPR will also be applicable for machinery. In the scope of machinery are machinery products and related products and partly completed machinery. The ESPR-requirements will apply in a long-term any physical product, meaning also machinery.

In the perspective of the Digital age, connectable machines shall comply to the concerned EU legislative act. The AI Act, the CRA and other legislative acts are applicable when AI is used in machinery, in smart machines, Internet of Things or remote maintenance. Safety components must be evaluated due to their physical and digital components.

That means, based on the risk assessment , the information given to all stakeholders, the manufacturer shall evaluate the physical safety and the digital security aspects of the machine.

Future prospects

The Green Deal will have a lasting impact on product development and the market in the EU. With the aim of promoting the circular economy and supporting responsible consumption, it forms the basis for a comprehensive transformation of the entire product landscape. Manufacturers and other economic operators face the challenge of implementing far-reaching changes and taking measures to comply with the new requirements.

As the Green Deal and the legislation of the Digital age introduce extensive information obligations for products, this will have a significant impact on technical communication. It will become relevant for a wider range of products with more detailed required information for the existing and new target audiences.  As a result, technical communication will become much more important and an indispensable part of the product strategy.

Training of large language models (LLMs)

Large language models (LLMs) are trained on a broad base of text data. Their unsupervised learning is based on recognizing patterns in the order of letters and words. Basically, they learn in an unsupervised process how letters follow each other in languages such as German or English. This approach makes it possible to identify larger linguistic patterns. However, there are also areas where large language models show weaknesses. Their performance can be improved through additional methods such as reinforcement learning and other learning approaches. One particularly effective approach is self-supervised learning. Thanks to the vast amount of text on the internet - from books to articles to websites - language models can be trained efficiently. The concept of cloze texts is often used here. Parts of a text are removed and the model is trained to insert the missing words or phrases correctly. This method is extremely flexible: texts from almost any field - whether law, history or fiction - can be used. The principle is simple: a text is partially emptied and the model learns to fill in the gaps with the best possible words. This enables an efficient learning process based on an almost unlimited amount of training data. This approach allows models to be continuously improved and further developed. This is the core of how large language models are trained and why they are so powerful.

Improvement of prompt engineering and prompt tuning

To achieve near-optimal or even deterministic results with LLMs, an important question is how to improve prompt engineering and prompt tuning in a standardized and professional way. The concept of so-called "mega-prompts"involves dividing each task that a large language model is to perform into clearly defined sections. These sections should be formulated as precisely as possible.

The first step is to assign a clear role to the language model. For example, we could say: "You are an expert in technical documentation. Please help us." The model should always know in which role it should act - be it as an expert, teacher or advisor. This is particularly important in educational contexts: students should learn to make specific requests, e.g: "I am a student, I am having problems with this task, and I need specific support." Clearly assigning roles in this way significantly improves results.

The task should be described precisely, including the individual work steps. Large language models benefit when complex tasks are broken down into smaller steps that can be processed iteratively. An example:

1. Analyze the text.
2. Think about the content.
3. Structure the text.
4. Create a summary in bullet points according to certain specifications.

By specifying such steps and instructions, the model can work more efficiently and in a more targeted manner.

A language model can only work with the information that is provided to it. Without context - such as geographical, temporal or situational background,the model will not be able to provide optimal answers. An example: "We are in Central Europe, it is winter. Should I wear a jacket today?" This type of context must be specified explicitly, as the model has no situational awareness of its own.

The desired output format should also be clearly defined, especially if there are specific requirements, such as compliance with certain standards or structural specifications. Structured formatted texts are much more useful than unstructured output.

An effective mega prompt could look like this:

• Role: "You are a research expert."
• Task: "Formulate a precise summary."
• Steps: "1. analyze the source. 2. structure the content. 3. create a summary according to a list of points."
• Secondary conditions: "Follow specific guidelines and provide the response in XML format."

Such detailed prompts lead to much better results than simply entering a text and hoping for useful results.

Language models can also be enriched with specific commands, e.g. for multiple-choice tasks, XML exports or other automated processes. In the education and e-learning sector, exams or content could be created efficiently in this way. By introducing parameters, models can even be controlled programmatically in order to meet specific requirements - such as legal issues or content-related topics.

A particularly interesting aspect of prompt optimization is that the language model itself often knows best how to handle its functions. If a prompt needs to be improved, AI itself can help. For example, if you are unsure how an optimal prompt should be designed, you can ask the language model directly: "You are now a prompt generator. Please define the best prompt for this task." This is a new and effective approach where the model itself suggests how it can best be used. In an earlier project, the language model was even able to act as a tokenizer: It suggested splitting texts into smaller units to accomplish the task more efficiently.

The following procedure is recommended to achieve good output results:

1. Divide tasks into smaller steps: Large language models deliver better results when tasks are broken down step by step and into manageable units. In this way, the model can work more efficiently and avoid errors.
2. Validate results: There is always a certain risk of so-called "hallucinations" (false or invented information). Repeat the same task several times and compare the results to ensure consistencySeveral runs can help to identify optimal solutions.
3. Provide examples: Show the model concrete examples of the expected results or solutions. The more examples provided, the better the model can learn to fulfill the task.
4. Iterative improvement: If the results are not satisfactory, work iteratively and adjust prompts or framework conditions.

AI and LLMs can be used for almost any task - be it for optimizing prompts or structuring complex processes - making stochastic language models more deterministic and reliable in their use.

Automatic indexing of documents

The objective of the "Text-IT" project was to implement automatic or semi-automatic tagging of documents, whereby the tags had to comply with the iiRDS standard. There were restrictions regarding the input format. Image data was excluded as it was not part of the project scope, although good progress is now being made in image analysis. Large language models were used to achieve the project goals.

Some instructions were extracted from the document text and made available to the model via prompts. This required not only importing the commands, but also the entire text, which slightly increased the workload. The model should respond with a standard-compliant list of texts, which was available in a textual form. A structured output, ideally in XML or JSON format, makes further processing much easier. A pure text output, on the other hand, would have been less useful from an IT perspective. An example of an expected output could look like this:

• Language: German
• Information topic: Generic collection
• Conformity: Generic conformity

Of course, it also required post-processing to put everything in the right context.

Different variants of interaction with the language models were also examined. A distinction was made between tagging (assignment of predefined tags) and labeling (user-defined assignment).

Tagging uses predefined options (e.g. subject areas) from which the language model selects the appropriate tags. This approach is easier to handle and enables consistent results.

In the second type of interaction, labeling, user-defined tags were used. Training was somewhat more difficult here. While automated tagging quickly reaches its limits, especially when adapting to new subject areas, Labeling offers a little more flexibility, but is more complex.

Another aspect of keywording is the number of calls. A single call to complete the entire task and obtain a result would only work to a limited extent, as the language model would be overloaded. A better strategy was to split the document and the text and divide the task into several calls. With this so-called multicall strategy, the results were much more precise as smaller tasks could be targeted.

The evaluation compared the different strategies and their impact on outcomes. We specified context, purpose, topics, rules, location, aspects and boundaries, among other things, in order to formulate the prompts precisely. But the most important factor is how prompts are defined;  strongly defined prompts are crucial to achieve the best results.

The model configuration was deliberately generic so that parameters such as the model, the input prompt and the temperature could be easily adapted via configuration files. This means that when new or cheaper models become available, a simple line change can be made without modifying the code.

The "temperature" in this context influences how creatively or neutrally the model responds. For technical documentation, strictly neutral responses were to be achieved, so the temperature was set correspondingly low. The model also needed to be provided with the iiRDs statistics and associated commands.

The prototype of the user interface was very similar to the final goal: the AI automatically assigns texts to tags, with the user having the option to make subsequent adjustments if errors or inconsistencies occur. When the text is extracted from a PDF, the automatic assignment takes place and the user can correct it as required.

When evaluating the accuracy of the emulation, it was found that the results for closed tagging were very good, especially for predefined texts. However, problems occurred with the number of tags, which is why an emulation metric was developed to correctly evaluate missing tags in the error statistics. In the free text analysis, it was more difficult to make an exact assignment, as the similarity of the text had to be compared..A similarity matrix was used to help identify and correct errors, although this is still a challenge.

The results were also compared with those of human experts in the field of technical documentation. The agreement was good, but there were discrepancies, especially in the interpretation of tags such as "caution", "warning", "danger" or "note". These terms lie in a gray area where different interpretations are possible, even if their definitions are relatively clear. This subjectivity was an important learning point in the project.

In terms of cost, the calculations for tokens and a good model were relatively inexpensive, minimizing the need for manual review by experts.

Findings from this case suggest that if the human effort required to check and correct the results is less than the entirely manual processing of the document, AI costs can become insignificant. This shows the potential of a business model in which AI provides support while manual control of the results is retained.

In summary, it can be said that large language models can certainly be used for technical documentation, but some critical aspects require fine-tuning.

Introduction

At one point, at least in the field of translation, it felt like we were at the cutting edge: Neural machine translation was well researched, findings were available, syllabuses and courses had been worked out. But the emergence of ChatGPT means we now have to deal with many new developments. While ChatGPT is not necessarily better at translation than, for example, DeepL, it allows for fine-tuning and customization of output to a much greater degree than ‘traditional’ neural machine translation. Language models open up a wide range of tasks beyond machine translation that can be (partially) automated  in language industry workflows.

Recently, we have entered the era of multimodal LLMs. These models, such as GPT-4o, seamlessly integrate different modalities such as text, voice, images and video. Thus, we have moved from purely text-based models to modern multimodal models, which are summarized under "general-purpose AI". The European Parliament Research Service defines general purpose AI as "[M]achines designed to perform a wide range of intelligent tasks, think abstractly and adapt to new situations” (European Parliamentary Research Service 2023:1).

The extended capabilities of these models go far beyond machine translation. As figure 1 below shows, they can be used, among other things, to support project management, terminology work, automatic terminology extraction, quality assessment and control as well as automatic post-editing.

Recording AI skills

Since the invention of the computer, numerous digital skills have emerged which are summarized under terms such as computer literacy, information literacy, media literacy,  and data literacy. The collective term for these is digital literacy, which also includes machine translation skills. AI literacy is the most recent addition to this group of skills. Long and Magerko define AI literacy as "[A] set of competencies that enable individuals to critically evaluate AI technologies, communicate and collaborate effectively with AI, and use AI as a tool online, at home and at work." (Long/Magerko 2020:1).

According to Markauskaite et al., “there is little doubt that AI reconfigures the distribution of intelligence, labour and power between humans and machines, and thus new kinds of capabilities are needed […].” (Marcus Kaite et al. 2022:2). These capabilities are often discussed under the term "AI literacy".

If you look at the topic of AI from the perspective of skills, there are different approaches. One question could be which human skills could become obsolete and which skills could be lost in humans because the work is done by language models. E.g, Sandrini has developed the so-called ‘translator obsolescence cycle’, which depicts the potential loss of certain skills as the degree of automation increases. Another recent study by Ayvazyan et al. examines which skills remain largely unaffected by large language models and could become increasingly important for human translators; this shift could mean higher qualifications in the profession in the long term. This echoes Olohan's statement in relation to neural machine translation: “[A]t least in the foreseeable future, it seems appropriate to think about the increasing use of technology as being frequently accompanied by an upskilling of translators, which is reflected in the need for translators to receive specific postgraduate training and education” (Olohan 2017:277–278).

From this perspective, the technologies that are currently available have potential, but they require the integration of people into quite complex work processes.  

The AI Literacy Framework for Translation, Interpreting and Specialized Communication aims to reflect this upskilling approach and focuses on modern large language models as well as new technologies that could follow on from these models. The framework examines the impact of these technologies on translation, interpreting and specialized communication from a competence perspective - and also from a university teaching perspective to answer the question of what future students should learn in this field.

Overall AI literacy is divided into several dimensions: technical foundations, domain-specific performance, interaction, implementation and ethical and societal aspects, as illustrated in Figure 2.

Technical foundations

The technical dimension includes understanding how modern AI technologies and artificial neural networks work, especially the Transformer architecture with its self-attention mechanism and the use of word and sentence embeddings. Aspects such as the training methods of these networks, the training pipeline, fine-tuning and bringing the models into line with certain human values are also included. Another important aspect is the difference between natural and synthetic training data and their effects. It is crucial to understand that in modern AI technologies, the synthesis of AI model and training data forms an inseparable whole, where the weights of the model are calculated based on the training data - comparable to an omelette that cannot be broken down into its original ingredients. In addition, the technical dimension also includes the watermarking of AI-generated content, which is particularly important with regard to potential manipulation.

One might wonder why such technical foundations are necessary in the context of translation, interpreting or specialized communication and are not only relevant for computational linguists. A quote from Dorothy Kenny illustrates this point well: she emphasizes that working with "black box" technologies such as AI models without a basic understanding of how they work can lead to disempowerment of those who use these technologies. As long as we don't know what these technologies can do and what their limitations are, they work like magic - and this can prevent us from critically questioning their promises. A solid technical foundation helps to counter claims that promote new technologies as a panacea and enables us to use these technologies responsibly and critically.

Domain-specific performance

The domain-specific performance dimension is about the scope of the capabilities of these models. It concerns the level of performance for specific tasks, the identification of human added value, input-output modalities, their future potential and potential machine circularities. Determining the scope of capabilities and the task-specific performance level is not as trivial as it sounds, as the capabilities of these AI technologies remain somewhat  opaque due to their general-purpose character (see figure 1 above).

An important point here is: what is the human added value? We need to ask ourselves this question in the future - not only for translation, but also for other professional fields in which these models perform impressively. For example, if a task cannot currently be automated by GPT - what happens when GPT-4 or a future version such as GPT-5 can take over this task? There are also dangers, such as those of introducing machine circularities, because these models are so versatile that one and the same model could produce a text, machine pre-process, translate and post-process that text, and finally perform a quality assessment of that text. This is theoretically possible, but from a risk diversification perspective, it is better to distribute these processes across multiple technologies and, if possible, multiple human experts. This is also where the concept of the "expert in the loop" comes into play, in which humans are involved in AI-assisted workflows in various ways remain responsible for the final quality of a product.

Psychologist Daniel Kahneman makes a general distinction between fast and slow thinking. There is one system for fast, instinctive, automatic thinking that requires little to no cognitive effort, and another for slow, rational thinking that involves more complex decisions. Andrej Karpathy and others have drawn this parallel with LLMs. They say that LLMs excel at thinking quickly and effortlessly. However, they are less capable of more elaborate, abstract and complex thinking that humans excel at. So if you ask about the overall added value of humans, this can be found above all in their ability to think in complex, abstract ways.

As Bubeck et al put it: “[GPT-4] relies on a local and greedy process of generating the next word, without any global or deep understanding of the task or the output. Thus, the model is good at producing fluent and coherent texts, but has limitations with regards to solving complex or creative problems which cannot be approached in a sequential manner.” (Bubeck et al. 2023:80).

OpenAI has a new series of models, the so-called "o1 series", in which the models are trained to go through a "chain of thought" before answering a question, in which the model breaks down a complex task into specific subtasks and then processes them one after the other. It has been shown that the "thinking skills" of this o1 series have been improved by this internal “chain of thought”. However, it is still unclear how far these models can be further developed in this respect.

Interaction

The interaction dimension covers knowledge of the available modalities of interaction, such as writing, speaking and potentially gesture control. It also includes specific pre- and post-editing, prompting, as well as the cognitive level, which concerns changes in the areas of receptive or productive skills or cognitive effects in a hybrid human-AI system. This also includes the development of adaptive expertise or a macro-strategy. The action level is also an important component of this dimension.

This dimension is particularly important from a human perspective. The question arises as to how to interact appropriately with these models in certain usage contexts and for specific tasks in which these models are intended to provide support. This requires knowledge of the modalities of interaction that are available today. Currently, we mainly write text-based prompts, but if you use OpenAI on a smartphone, for example, you can simply talk to the model. Future widespread interaction with these models may therefore no longer rely solely on written input.

Interaction may also include AI-specific pre- and post-editing beyond the field of MT. For example, one could imagine that developer documentation for a technical assembly or machine is entered into the AI and GPT then converts this documentation into user documentation. This would then be post-edited by a human expert.

There is also the cognitive level of interaction, which deals with possible changes in the areas of receptive or productive skills of AI users. The ideal notion is that of humans and machines working together and complementing each other. Ideally, our weaknesses are complemented by GPT and vice versa, but in practice this is not always the case. There could also be potential negative effects, such as stagnation in competence development or de-skilling, if too much reliance is placed on AI output. There is some evidence from machine translation research that translators who post-edit machine-translated text often still retain traces of the machine output. This issue will probably also play a role in the interaction with LLMs.

The same applies to the level of action. How is the ability to act distributed between humans and machines? There are concepts of collaborative agency that ultimately aim to expand the agency of both humans and machines so that they work together as one system.

The term "prompting" covers actions that humans use to guide LLMs to perform the intended tasks. Prompting is increasingly seen as an expert skill and is not trivial. A study by Zamfirescu-Pereira et al. showed how non-AI experts designed their prompts opportunistically and not systematically. They tended to interact with LLMs as one would interact with a human, without specifically considering that they were working with large language models. This led to overgeneralization, where solutions from one context were applied to others, often with ineffective results.

The OpenAI website has strategies and tactics for prompt engineering that show that there is more to prompting than meets the eye. Also, White et al. have developed a prompt pattern catalog that originated in software programming. Andrej Karpathy argues that the hottest new programming language is English, which means that this natural language interaction can be seen as a kind of programming where you instruct the LLM to perform certain tasks.

Implementation

The implementation dimension includes the establishment of an AI culture, the selection of an AI model, quality control, economic aspects, AI risks and legal frameworks applicable to AI. It deals with the question of how these systems can be integrated into practical workflows, which is much more difficult than simply buying a ChatGPT subscription and squeezing the model into existing workflows, without taking the concerns of end-users (translators, technical editors or other employees) into account.

The first step is to establish an AI culture within the company. In a study by Salesforce, more than half of generative AI users in international companies stated that they use AI tools without formal authorization. They reported that there are no guidelines from their employers on how to use these technologies, and many would have liked to receive corresponding training. This means that the AI culture was not yet established in these organizations - although things may have changed in the meantime. An AI culture requires a basic understanding of what tasks and problems in the organization could be solved by AI and how this can be done.

Many questions need to be answered, such as what processes need to be designed or redesigned, what the general level of automation is, where human added value is needed and which tasks could potentially be fully automated. And how does the European Union's new AI legislation fit into this context? There are ergonomic factors, such as organizational and cognitive ergonomics, that should be considered in order to introduce and implement AI adequately and in a responsible manner.

An AI provider must then be selected based on the appropriate AI model. There are thousands of models that can potentially be used: Closed source, open source, large or small models, and those that excel in different languages.

Translation studies and the translation profession are particularly well-versed in AI implementation, as they have been dealing with neural machine translation (NMT) at least since the invention of the transformer in 2017. They know how to design or not design processes when integrating NMT and are familiar with the various risks associated with NMT. Many of these insights from translation studies could be integrated into other workflows where language models are used.

Ethical and societal aspects

Ethical and social aspects constitute the final dimension of the model and go beyond the narrow focus on specific professional fields. This dimension deals with the question of what AI skills we need beyond our professional role as citizens in an AI-saturated society. For example, there is the question of whether AI could bring about social empowerment or disempowerment. Translators have already had unfortunate experiences of social disempowerment through NMT and other technologies - be it economic, loss of social status or loss of cultural capital. There is also a risk that these models will produce toxic results, as they are very powerful and can answer a variety of questions that a human would not be expected to answer. Safety barriers must therefore be put in place to avoid these toxic results. Another risk is manipulation. AI models are pretty good at mimicking voices. Robo-calls or emails created with this technology could trick people into accepting assignments that offer poor compensation and short deadlines, or they could be used to manipulate translators or interpreters. There is also a risk of epistemic violence or bias. AI models tend to misrepresent reality and reproduce social biases, such as gender bias. There is also a tangible and intangible substrate on which these AI technologies are based. They did not develop in a vacuum, and there are environmental costs. A significant amount of intellectual work has gone into developing the algorithms and compiling the training data. Again, these aspects have already been reflected by translation studies, which could also make important contributions to technology impact assessments with regard to the actual consequences of the implementation of these technologies at a societal level.

Planned didactic operationalisation of the framework

The next step involves the didactic operationalization of the framework in the spirit of the learning resources created by the DataLitMT project. One potential vehicle for this operationalisation is the European Union Erasmus+ consortium "LT Leader: Language and Translation: Literacy in Digital Environments and Resources". The aim of the consortium is to map the technological landscape in the translation sector and to create learning resources for the development of digital skills in relation to these technologies. The focus of these digital skills is, of course, on AI skills. This is because the technological landscape is becoming more and more dominated by large language models.

Presentation Slides

Chameleons at work

At SAP, user assistance is much broader than the traditional conception of technical writing. UA plays a pivotal role in helping users navigate complex systems, and every app and every feature comes with user assistance. To ensure users are provided with the right help when, where, and how they need it, UA developers take on a wide range of roles, such as documentation developer, UI text creator, terminology expert, blogger, content management system expert, software tester, search optimization expert, UX designer, multimedia creator, accessibility expert, and more.

SAP’s UA developers work in an agile development mode, often in scrum teams of ten. The content they create is just as varied as their roles, and ranges from legally required documentation and UI text to guided tours, graphics, videos, blog posts, and some nice-to-have content such as chatbots and podcasts.

To create and manage their content effectively, UA developers use a number of tools. These include, but are not restricted to:

• MadCap IXIA CCMS: A Content Management System (CMS) for content creation
• SAPterm: SAP’s own terminology database for maintaining SAP-specific terms
• MS PowerPoint and Visio: for image creation, for example diagrams or interactive graphics
• Camtasia: for video production, for example, how-to videos
• Semaphore: for metadata modeling, taxonomy, and ontology

Due to SAP’s diverse product portfolio, it is crucial that UA developers ensure user assistance deliverables are consistent across all offerings. Style guides and templates, as well as help in the form of support forums, are an important aspect in achieving this.

Focus on continuous learning

SAP places a strong emphasis on continuous learning and development of its workforce. The company offers a comprehensive internal curriculum organized into learning journeys. The curriculum is flexible, allowing individuals to tailor their learning paths based on their experience, educational background, team requirements, and personal preferences. New hires and experienced UA developers alike can benefit from the dedicated UA learning journeys to upskill and specialize in areas of interest, such as UX writing, conversation design, or analytics.

An ideal candidate? A jack-of-all-trades

When it comes to hiring UA developers, SAP would ideally like to see candidates with a degree in technical communication with experience in topic-based writing within an XML environment, familiarity with terminology work, a basic understanding of technology and business processes, and proficiency in creating meaningful graphics and other multimedia content.

Most important of all, however, are strong English language and writing skills. English is the primary language of SAP's UA content. It is also the source for translations into up to 40 languages for documentation and up to 80 languages for UI texts, which is another reason why applicants must have excellent English language skills.

About iiRDS

iiRDS stands for intelligent information Request and Delivery Standard. The iiRDS Consortium consists of 26 members who are driving the standard forward as service providers, tool manufacturers or industrial companies and who are dedicated to information for use as part of digital business models. The Consortium looks forward to adding universities among its membership in the near future.

Ralf introduced iiRDS and navigated the participants through the changes in the users and authors perspective as well as the changes in content creation process and the consequences for technical communication like the growing importance of the semantic net. “With iiRDS we have a really good starting point for creating knowledge graphs.” By using iiRDS, companies can enrich and link information sources. The core metadata, which is available for free download at iirds.org, was visualized by Ralf. The standardization that iiRDS provides refers to standardized metadata and a standardized delivery format (RDF). The iiRDS Consortium took part in and Industry 4.0 InterOpera projects this year to develop a submodel of the Asset Administration Shell. The AAS is the basis for interoperability and is the digital twin of a machine or plant. Digital twin data in the administration shell describes the product of the physical world in the information world. So “iiRDS is a big piece of the future that we want to share with as many people as possible in the digital era”.

Next steps of iiRDS in university collaboration

The presentation was followed by questions and an exchange of ideas how iiRDS could be integrated in universities. The iiRDS Consortium offers to organize guest lectures to present iiRDS either at the interested university or as an online lecture. In addition, an iiRDS sub-working group is to be established, in which consortium members and universities will participate and drive the exchange. Meetings could be held monthly or every other month. Active participation in the working group would be a prerequisite for free membership in the consortium for the respective university. Universities and students can not only participate and help shape the exchange of expertise here, but they also gain access and contacts to companies, e.g. for final theses. The iiRDS Consortium plans to offer the iiRDS Online Training to a limited number of university staff or students for free participation in 2024.

If you are attending tcworld conference in Stuttgart, join us in the meet-up room on Tuesday, November 14 at 4:30 p.m. for a follow-up discussion and first meeting of the university collaboration sub-working group.

The iiRDS Consortium is open for ideas and interested universities are welcome to contact Susanne Lohmüller at the tekom-iiRDS Office.

There are many questions to solve, for example from the student‘s perspective, whether they are allowed to use ChatGPT for a seminar or final paper, or whether this practice constitutes cheating? How can they use ChatGPT best? What skills should they acquire, and what skills are obsolete because of ChatGPT? Teachers and lecturers also ask similar questions from a different perspective. They deal with questions like: which skills should be taught and how can students’ competencies be tested rather than their ability to prompt generative AI models. Also, how can ChatGPT and other systems based on generative AI be used to increase the efficiency of developing training, or even improve teaching. Challenges from the teacher‘s side include ensuring students do not simply copy and paste their work from ChatGPT and ensuring that students do not use ChatGPT as a tool for assignments. They have to focus how teaching courses, learning for exams, writing seminar papers and theses, and assessing student learning and performance will change because of the new technology.

In order to improve the participants’ understanding of ChatGPT, we had demonstrations in the meeting. ChatGPT is an artificial intelligence (AI)-based conversational agent that can generate various types of content, including college-level essays. Generative AI systems can generate text, images, or other representations with relatively little human input. They work most effectively when the prompt is precise and specific, so it is really important to formulate a good question, to which ChatGPT then "writes" the answer.

We - the participants of the IUNTC asked ChatGPT if it could write us an opening speech for the following day’s EAC. All participants were amazed at how good the result was. Since this was a vivid experience of how the technologies will also revolutionize technical communication, the group decided to show it at the opening of the EAC - a demonstration of how AI and digitization will change technical communication and teaching.

Hence, it is not surprising that the advent of generative AI fundamentally challenges accepted knowledge, assumptions, and behaviors in higher education. And one thing is also obvious: ChatGPT and GPT-4 are only the forerunners of what we can expect from future generative AI-based models and tools. So it is worth looking into the impact on higher education.

The next IUNTC meeting on Wednesday, 7th of June at 3:00 pm will continue to deal with this topic in detail: AI in academia: balancing benefits and challenges by Jenni Virtaluoto & Prof. Sissi Closs.

Another central question during the meeting was: how can we make technical communication more attractive as a field of study for more students? There was a suggestion on this topic from the University of Munich University of Applied Sciences to find project partners in the community for joint marketing campaigns. The problem that too few students are interested in the courses of study is common across several universities. However, it applies not only to technical communication, but also to other courses of study. In spite of the challenge of recruiting students, technical communication graduates are very attractive for the job market and can expect interesting job offers and a good salary. One challenge is that students may be reluctant to take technical subjects that involve a lot of technology. Current topics such as user experience design or information design may be more attractice. For this reason, many universities are adapting the content of their study programs.  In addition, technical communication is not very well recognized as a profession in many countries. Targeted recruitment campaigns would have to start here. In addition, the desired target group - school leavers - is not easy to reach.  It´s important to leverage social media and create a social media presence and to showcase technical communication’s relevance and importance which can attract more students who might be interested in pursuing a career in this field. Therefore tekom as the professional organisation for technical communication can help attract more students by providing networking opportunities and marketing campaigns.

The IUNTC meeting ended with a nice dinner together at pleasant summer temperatures in a beer garden in Karlsruhe.

The group discussed what kind of changes technical writers have to deal with nowadays. The participants agreed that the working practices of technical writers have shifted considerably: communication, collaboration and shared learning are highly relevant today.

Communicating in agile teams, applying more frequent, less formal feedback and working in shorter increments of time for rapid or continuous delivery is gaining more and more importance. You must deliver something every day; testing is much shorter and more interactive.

These developments imply that technical writers need to acquire more and different competencies than before. “I was just surprised I had to develop skills I hadn’t before,” said one of the participants.

For technical editors, who used to belong to a separate department, this means a serious change. The backing of the doc team is missing; editors have to work independently and make decisions on their own. As this way of working requires a high level of personal responsibility, it means a big change for employees who are used to hierarchical structures.

The big advantage of agile teams: daily communication in the meetings gives all team members insight into each other's work. Technical writers often get direct feedback on their documentation and can then adjust it to improve the user experience.

Video conferencing or web meetings are extremely important for editors, as they usually have no access to the development environment and must rely on being part of the information flow.

In the context of agile development, only a few specifications are still available to editors as a source of information. Therefore, a good document management system helps to manage documents. Most technical writers see this as an advantage: products and their documentation are tested in a timely manner so that errors are discovered more quickly, and the editors' work can be assessed.

When working according to the waterfall principle, developers often have no way of recording their meetings with editors in terms of time. In Scrum mode, the creation of individual texts is a backlog item, which also strengthens the position of the editor in the team. A key takeaway from the meeting was that technical writers must have an open ear; their documentation might otherwise fall out of step. IUNTC participants thanked Kai for his interesting and insightful presentation.

Kai Weber is a lead technical writer for financial and banking software, SimCorp GmbH. He is a long-standing member of tekom and a regular conference speaker.

The general goals

“We have a research gap in Germany. For this reason, we need more graduates who are interested in research,” Dr. Michael Meng, Professor of Applied Linguistics at Merseburg University of Applied Sciences, said at the last IUNTC meeting on December 15. He has been working since 2012 in the department of economics and information technology at at Merseburg University of Applied Sciences, researching in the fields of technical communication, information design and psycholinguistics.

The professor sees the main reasons for this, among other factors, as the lack of time in universities of applied sciences. With 16 to 18 hours of teaching obligations, it is difficult to build and lead a research group. “However, it is also important to do more at universities to introduce students to research,” says Dr. Meng. The role and importance of research, according to Dr. Meng, must be demonstrated in order to spark students’ interest and allow them to actively collaborate. Teachers should continually ponder how to introduce students to research, interest them in it and enable them to conduct research.

For this purpose, a module of 4 semester hours per week (a total of 150 hours) was integrated into the third semester of the “Information Design and Media Management” master’s degree program at the University of Merseburg, dedicated to research and research methods specifically and exclusively.

This project has a history dating back to 2014, when Professor Closs initiated an exchange project between HKA and PU. This project was called ADDI – ADaptive Digital Learning System. The focus was on Communication and Media, live student exchanges between HKA and PU, and the development of a digital platform to share content and collaborate: karlifi.org.

When in-person exchanges became impossible in 2021 due to the pandemic, a new project was initiated to enable virtual exchanges: VIEL (Virtual Intercultural Exchange and Learning). A third partner was included, UL. Students at the three universities get to know one another in weekly meetings online, attend workshops, and complete small group assignments (like making podcasts and short videos). Students receive a certificate and get to meet at one of the participating universities if no travel restrictions are in place. To-date, VIEL students and staff have met in Kenya (February 2022) and Ireland (July 2022). Both ADDI and VIEL were funded by BWSplus (the Baden-Württemberg-STIPENDIUM for University Students, a programme of the Baden-Württemberg Stiftung).

A third project in this cycle and by far the most ambitious, MCM (Master’s in Communication and Media) is funded by the DAAD (Deutscher Akademischer Austauschdienst –German Academic Exchange Service). Its goal is to introduce a new study programme in the discipline of technical communication at PU. The project includes various stages: curriculum development, accreditation of the programme at PU, implementation of a suitable IT environment and equipment, staff training, marketing of the programme in Kenya, and the establishment of a network to get support from local companies and to connect them with programme staff, students, and graduates.

Following a live kick-off event at PU in September 2022, a pilot programme is already underway with 12 students from PU taking Special Courses in Communication and Media. The pilot involves 11 external teachers, with most classes delivered virtually but some on-site. It is a very important phase in the project because the feedback from students and teachers will shape the delivery of the Master’s in Communication and Media, due to start in Autumn/Winter 2023.

The various connected projects have been successful because of funding from BWSplus and DAAD, a stable project team, excellent leadership, a high level of commitment on the Pwani side, and growing interest from students and lecturers. 

To learn more about the projects, or to get involved, see: 
Master’s in Communication and Media Website: mcmstudy.org
Karlifi Learning Platform: karlifi.org

Collaborative international inter-institutional projects offer immense benefits for technical communication students and teachers. Students learn first-hand about the challenges and rewards of collaboration, virtual exchange, and intercultural communication. They also develop transferable skills that help to prepare them for the workplace. Teachers experience new and dynamic pedagogical approaches, and successful projects may lead to research, as well as teaching, partnerships. While many institutions encourage international inter-institutional exchanges because of these and other benefits, establishing contacts and projects is time-consuming and complex.

On Wednesday, September 21^st, during the first International University Network in Technical Communication (IUNTC) meeting after the summer break, Suvi Isohella, University Teacher in Technical Communication at the University of Vaasa in Finland, led an engaging and informative discussion about the Trans-Atlantic and Pacific Project (TAPP). Suvi explained how TAPP supports international inter-institutional exchanges in technical communication.

The TAPP network was established in 1999/2000 by Dr Bruce Maylath and Dr Sonia Vandepitte. What began as an exchange between two universities has since grown to a network of 49 universities in 21 countries across five continents. The network coordinator maintains a database of project partners and project types, and helps individuals to find suitable partners, and to establish and run projects.

Suvi described three types of project supported by TAPP.

1. Writing-translation projects, where writers prepare a source text for translation, and translators localise the text for the target audience.
2. Bilateral translation-editing projects, where translators get feedback from editors about their translations (usually into English, which is not their first language).
3. Multilateral projects, that reflect the workplace norm of cross-functional collaboration.

Students at the University of Vaasa have been involved in several multilateral projects since 2010. The Vaasa students have focussed on usability testing in many of these projects. For example, as illustrated in the figure below, they have worked with students from universities in Spain and the US to write, translate, and test instructions. They have also worked on multilateral projects to develop personas, and to create infographics.

Suvi explained that TAPP projects are successful because they are administratively uncomplicated. Projects do not depend on funding, and the teachers collaborate to design assignments that match their modules and learning outcomes. Of course, as with any inter-institutional collaboration, teachers must take several steps to ensure projects are successful. Students need detailed instructions and scheduling can be complicated, especially when multiple institutions are involved.

Suvi’s talk concluded with a lively discussion and question and answer session. This IUNTC meeting, like many others, was very well-attended and included participants from all over the world. Several members of TAPP were present, including Dr Patricia Minacori, Dr Bruce Maylath (outgoing coordinator) and Dr Ashley Petts (coordinator since August 2022).

You can learn more about TAPP and how to get involved, at: The Trans-Atlantic and Pacific Project | University of Houston-Downtown (uhd.edu).

Find the presentation slides here.

Thirty-three participants joined the meeting, which started with an interesting lecture by Dr. Christiane Zehrer on “Agiles Methods and their meaning in todays working world”. Dr Zehrer is an agile practitioner (Certified Scrum Master) and a technical communication instructor at Hochschule Magdeburg-Stendal, Germany.

Currently, around 27.3 percent of employees in technical communication in Germany work in software companies. About three percent of all employees in a software company are technical writers. Software industry is a growing segment, with a growing ratio of technical writers in business units.

The work of technical writers in software development differs significantly from other industries. For one thing, they use other tools, e.g. component content management systems are less common in software development than in other industries. They also create other types of information for use, e.g. API (Application Programming Interface) documentation and embedded help. In addition, there is the digitization trend: With growing digitization, products, business models, value chains, customer behavior, and the workplace change. This also leads to radical changes in the work of technical communicators. As an example, agile project management methods are already common in most software companies, leading to a stronger interlink between product development and technical communication.

A Mentimeter survey of meeting participants showed that most are already familiar with agile project management methods, especially Scrum and Kanban. In contrast to classic project management, agile methods differ mainly in the shorter and more detailed planning phases. Task packages are kept as small as possible and scheduled for a fixed time. For example, daily standups are fixed elements: Teams coordinate their work through frequent, but usually short meetings. Sprint backlogs – a prioritized list of all those functions that are due at the end of the respective work period – are another important agile element. They consist of user stories, which describe the product from the user's point of view and focus on the task that the user wants to do. This provides a different perspective on the product. User stories are further devided into tasks, functions are secondary.  User stories and tasks are usually visualized using material or a virtual agile board. 

Thus, technical writers, who work in the software industry, are an integral part of  the agile development process. The question in the group was whether employees of technical communication, especially in software development, need new skills such as training in agile methods, and whether the future would see an increased need for generalists or specialists. These questions were discussed in breakout rooms.

As a conclusion, the following was noted: Project management, time management, und team communication will play a particularly central role in future. Also technological trends for context-based information delivery are becoming increasingly important. So technical competencies will be required in the future, e.g. to be able to read code for testing and user experience design. But above all, skills in interpersonal communication are still essential. The self understanding as a technical writer changes to the “information developer”.