Public Understanding of AI
through Transdisciplinary Teaching Education

WP1: Project management.

The aim of WP1 is the coordination of all project participants and the internal and external coordination of organisational points. The project management ensures the internal flow of information through regular communication with all project participants and keeps an eye on the progress of the project. As the Dean of the Faculty of Electrical Engineering and Information, it initiates and accompanies the advertisement and appointment of the professorship applied for immediately after the planned start date of the project, so that an appointment can be made in the winter semester 2022/23. Together with Division II (Human Resources and Legal Affairs) and Division III (Financial Resources), the requested
personnel and material resources will be managed throughout the course of the project. Division IV (Students) and Media Didactics will develop the options for issuing certificates (micro-credentials) based on the digital teaching units from Milestone C to increase the attractiveness and reach of the programmes developed. The project management is also responsible for the final administrative project documentation.

Contact person:

WP2: Interactive, collaborative learning platform for artificial intelligence.

WP milestones:

• Specification of the AI learning modules / proof-of-concept of the learning platform (month 12)
• First basic AI learning modules (month 24)
• Subject-specific AI learning modules (month 32)
   

The aim of WP2 is to develop an AI learning platform based on existing platforms such as JupyterHub, Apache Zeppelin or CoCalc. Firstly, requirements for the platform are collected, evaluation criteria are defined and the existing platforms are evaluated against them. A proof of concept with basic functions is then provided, which is continuously developed further as the project progresses. An important milestone is the connection to the university's learning management system (Moodle). Subject-specific functions that are necessary for the transdisciplinary AI modules will also be integrated into the platform.

UAP 2.1: Requirements analysis and proof of concept (16.75 PM, months 1-12) 
The requirements for a teaching/learning platform will be analysed and scenarios for its use developed in several workshops with lecturers, students and AI experts. In particular, subject-specific features that need to be taken into account in the general concept are to be identified. The result is a requirements document with a detailed description of the application scenarios, which will be continuously updated during the project. A first version of the learning platform is then designed and implemented in a proof of concept, which initially supports some of the previously identified case scenarios. The aim is not to develop a completely new version, but to evaluate, select and use open source systems (e.g. JupyterHub, Apache Zeppelin, CoCalc) based on the requirements.

UAP 2.2: Connecting the AI learning platform to Moodle (6 PM, months 13-24) 
The integration of the learning platform with the university's LMS (Moodle) should enable learners to have a seamless transition between the different learning systems and learning should not be hindered by media disruptions or system changes. The systems should not only be connected via simple web links, but integrated learning processes should be established between the subsystems. To this end, the integration scenarios from UAP 2.1 are first worked out in detail in this WP and processes are defined. The interface between the AI learning platform and Moodle can then be defined and finally implemented.

UAP 2.3: Development of subject-specific functions in the learning platform (4 PM, months 17-32) 
In the various disciplines, data is often provided in subject-specific formats (e.g. sensor data in mechanical engineering, text-based protocols in Health Care, video sequences in social work), which also require special AI methods for processing and evaluation. In this work package, the corresponding functions are to be developed in order to provide these specific data formats and analysis functions in the learning platform. To this end, the scenarios from UAP 2.1. are analysed in this work package and examined for subject-specific functions. Once they have been identified, these functions for data preparation and evaluation will be implemented. Initial results can also be incorporated into the proof of concept.

Contact person:

WP3: Development of digital AI learning modules.

WP milestones:

• Prioritised list of AI learning modules to be developed (month 14)
• First AI learning modules in the AI learning platform (month 16)
• All AI learning modules in the AI learning platform (month 43)
• Final version of the AI learning modules and evaluation results (month 48)
   

In this work package, the learning platform provided in WP2 is to be filled with content. The learning modules (learning nuggets) will be structured as modularly and independently of each other as possible so that lecturers can decide individually which units to integrate into their courses. Subject-specific learning modules (e.g. data preparation for health data) should be able to be developed on the basis of generic modules (e.g. data preparation). Therefore, the learning modules should not be made available as self-contained packages, but as customisable modules.

UAP 3.1: Identification of basic AI learning modules (3 PM, months 9-14) 
A structure of basic modules (e.g. data preparation) and subject-specific modules (e.g. data preparation in the Faculty of Health Care) will initially be developed with lecturers from various faculties.
In addition, a continuous process for the development, updating and evaluation of these modules will be defined. Subsequently, the results of the workshops from UAP 2.1 will be used to identify basic topics
that are to be implemented in the learning modules. As a further basis, existing curricula and proposals in the fields of AI, data science and computer science are analysed. The resulting list of possible AI learning modules is prioritised according to relevance and urgency.

WP 3.2: Implementation of the learning modules in the AI learning platform (20 PM, months 15-43) 
The learning modules defined in WP2.1 are implemented on the university's learning platform or LMS and made available to lecturers for use in their courses. This is done according to the prioritisation list from WP 3.1. This is a continuous process in which new modules are added while existing modules are used or further developed.

UAP 3.3: Further development of the AI learning modules (28.5 PM, months 25-48) 
The learning modules provided are used by the lecturers in the courses (see WP6-AP9). This requires professional, didactic and technical support. At the same time, the extent to which the learning modules support the students' learning process and the extent to which the learning modules have helped the lecturers to organise their course is evaluated. For the evaluation of students and lecturers, criteria (or question catalogues) are defined that go beyond the usual course evaluation. The results obtained are used to further develop and continuously improve the AI learning modules.

Contact person:

WP4: Courses on applications of AI.

Milestones:

• Course is designed (month 16)
• Course is held for the first time (month 21)
• Revised version of the course (month 39)
   

The aim of the AP is a seminar-based course for students outside of computer science, which provides an overview of artificial intelligence methods using various application examples and, in particular, shows suitable and unsuitable areas of application. The course includes a practical part in which students gain direct experience with the techniques through exercises. The aim is for the course to be worth a total of 5 ECTS credits, which enables it to be included directly in elective subject catalogues and specialisations of existing degree programmes.

UAP 4.1: Design of the course (5.25 PM, months 10-21) 
Based on the results of UAP 2.1, relevant areas of application at The Hochschule Niederrhein are first identified and thus also potential points of identification for students of this course. This is followed by the (transdisciplinary) development and didactic design of the course. As the target audience includes students from different degree programmes who have considerable differences in prior technical knowledge (e.g. text technology vs. social work), a modularised structure with different learning paths is designed. The individual parts created in advance are integrated into an overall concept and supplemented by suitable foundation courses to create a coherent course. This will be flanked by the ongoing and parallel development of a blended learning course in Moodle, for which the results of WP3 will be used as a basis.

WP 4.2: Implementation and further development (10.5 PM, months 16-39) 
The course will be offered for the first time in the summer semester 2023. The subsequent evaluation of the course will be based on the university's standards, but will also be accompanied in accordance with the concept of the entire project in the sense of the Scholarship of Teaching and Learning (cf. UAP 10.2). Based on the results obtained, the course will be optimised in the further course of the project. In addition to the qualitative dimension of further development, the thematic breadth will also be reviewed. To this end, the potential areas of application at the university will be systematically explored once again and any gaps identified will be closed.

Contact person:

WP5: Course on social aspects of AI.

Milestones:

• Course is designed (month 16)
• Course is held for the first time (month 21)
• Revised version of the course (month 39)
   

The aim of the WP is a seminar-based course for Master's students (especially in computer science) that highlights the potential and challenges of the use of artificial intelligence from a social perspective. This course should be part of the non-technical compulsory elective area of computer science in order to convey the social connection to the topic of AI, which can only be briefly addressed in all other technical courses.

UAP 5.1: Design of the course (4.5 PM, months 10-27) 
The foundation courses of this course are social AI issues such as data ethics, data (in)justice, sustainability of AI applications, cases and errors of automated decision-making systems, data sovereignty of citizens. In order to prepare the topics, external experts are identified here, who initially contribute keynote speeches to a lecture series, which then form a basis for a later implementation of the course in UAP 5.2. Furthermore, case studies, especially from the field of social sciences (see WP7), serve as a foundation course that encourages students to reflect on the use of AI.

UAP 5.2: Implementation and further development (9 PM, months 22-45) 
After individual lectures, the course will take place for the first time in the winter semester 2023/24. External experts will continue to be involved in teaching education by giving lectures, posing problems and questions and discussing AI applications. This shows students the social relevance of the solution. The subsequent evaluation of the courses is based on the university's standards, but is also accompanied in accordance with the concept of the entire project in the sense of the Scholarship of Teaching and Learning (see UAP 10.2). Based on the results obtained, the course will be optimised in the further course of the project.

You can access the round robin event here.

Contact person:

WP6: Transdisciplinary teaching education in food process technology.

Milestones:

• Teaching platform (hardware and software of food process systems) is set up as a basis for implementation (month 12)
• Course scenarios developed as conceptual possibilities (month 27)
• Concept for standardised course versions revised (month 45)
   

The aim of the WP is a seminar-based course blueprint for Bachelor's and Master's students of Computer Science, Nutrition and Food Sciences as a transdisciplinary exchange platform for the step-by-step development of overall goal-orientation competence in the
implementation of interdisciplinary projects with food technology and process engineering components.

UAP 6.1: Development of the teaching platform (8 PM, months 1-12) 
The foundation course is the development of the teaching platform for food process scenarios. To this end, the planned robot components are first assembled into an exemplary system and calibrated for basic operation. Based on the hardware, possible
"traditional" scenarios (without AI components) for using the programmable robot units in food production are then automated. For example, the production and packaging of chocolates is conceivable here. In the second step, automated process data acquisition is realised in the platform. Additional sensors (temperature, humidity) or a camera unit are connected for this purpose. This periphery is used to iteratively develop selected programming scenarios for general, practical automation tasks (optical control of production or correct placement) and to collect reference and example data as a foundation course for further task variants. This provides the basis for the development and selection of didactic implementation variants in UAP 6.2.

UAP 6.2: Scenario variants as a course possibility space (9.75 PM, months 13-27) 
The scenario settings prepared via UAP 6.1 and the associated measurement data sets are systematically grouped according to didactic objective variants and various interdisciplinary food science focal points as a matrix system. This creates a pool of learning platform scenarios on AI application topics such as machine learning, digital twins, etc. The temporal distribution of process control in two case variants is taken into account as a further aspect level. In terms of the range of practical implementation in food technology, fast, continuous production process implementations are compared with optimisation scenarios of batch-based product development that are not critical in terms of real time. This provides
an overview of which scenario variants can be used for the development of transdisciplinary learning modules with adaptation of the materials from WP3.

UAP 6.3: Alignment and revision of course scenario variants as a standard blueprint (11.25 PM, months 28-45) 
The learning platform scenarios created in UAP 6.2 are implemented in project-oriented courses and evaluated after initial implementation. The matrix system established in UAP6.2 forms a decision-making basis for orientation and formulation of feedback focal points. This is used to iteratively abstract intersections between different scenario variants in such a way that generally transferable templates for selected standard competence objectives are created in the sense of a further development of the learning platform application.

Contact person:

WP7: Transdisciplinary teaching education in the field of social work.

Milestones:

• Robot platform and data sets are ready as a basis for implementation (month 12)
• Project event is held for the first time (month 27)
• Further developed project events are established (month 45)
   

The aim of the WP is to organise transdisciplinary project events for Bachelor's and Master's students from the two disciplines of social work and computer science. These events will focus on the use of data analysis in social forecasting and the use of social robots in therapeutic situations and counselling. In this context, AI should recognise emotions and states of mind through to psychopathological symptoms and communicate accordingly.

UAP 7.1: Building technical foundations (8 PM, months 1-12) 
In the first part of this work package, the necessary teaching platform will be built, a robot (Furhat Robot) designed for social applications, which was developed for academic work on natural language processing and emotional intelligence in the field of human-machine communication. Its possibilities for the representation of facial expressions are to be explored and a first easily programmable interface for students of non-technical degree programmes is to be created, which will enable practical work with the robot in the first project event (generation of facial expressions or recognition of facial expressions). At the same time, suitable methodological concepts and AI tools will be selected. Again, simple software tools that enable quick success in data analysis without extensive knowledge should be found according to the level of competence.

UAP 7.2: Conception of the project event (9.5 PM, months 13-27) 
In the second step, work is carried out on the general conception of the projects, in particular the development of suitable questions, the selection of case scenarios and suitable data sets. Instructions are also created to help non-technical students train the robots and simulate communication situations. The materials and data created in UAP 7.1 and 7.2 will be integrated into the developed learning platform
(see WP3). Finally, the developed concept will be piloted and evaluated in the summer semester 2023 as part of a first transdisciplinary project course.

WP 7.3: Further development of the project course (11.5 PM, months 28-45) 
The project course will be continuously expanded as the project progresses. This happens on several levels. From a didactic point of view, not only the general course evaluation, but above all the SoTL activities running in parallel (cf. UAP 10.2). From a technical point of view, the interfaces to the robot platform will be further expanded in order to create new interaction possibilities and to enable further questions and case scenarios that were not yet available in the pilot programme (cf. UAP 7.1). Another core element of the further development is the integration of external experts from the field of application into the project events, which promotes the ongoing exchange between the university on the one hand and practice and society on the other.

Contact person:

WP8: Transdisciplinary teaching education in the field of textile technology.

Milestones:

• Construction and commissioning of the teaching platform (month 12)
• Completion of the documented experimental course (month 24)
• Transfer of the project course to regular operation (month 45)
   

The aim of the WP is to establish a teaching platform for intelligent cooperative robotics for applications in textile technology and its use in transdisciplinary project courses for students of textile technology and computer science. The WP is divided into the phases of setting up, programming and calibrating the robotics system and sensor technology, developing suitable demonstration experiments, designing the course along and based on the results of WP3 and assigning project assignments.

WP 8.1: Development of the teaching platform (8 PM, months 1-12) 
The first step is to develop a teaching platform based on hardware from the DOBOT Magician modular system, which can be used to illustrate a range of tasks in intelligent cooperative robotics in textile technology. Once the components have been procured, they are first assembled and then additional sensors are integrated so that further data sources can be used to automate and analyse the processes. Finally, the software part of the set-up, the programming of the automation and the calibration of the overall platform follow. Documentation for instructing students is generated to enable safe operation within teaching education. The functionality is tested by loading pre-trained AI models as examples.

UAP 8.2: Design of teaching materials (7.75 PM, months 13-24) 
Building on UAP 8.1, the teaching materials are designed on the basis of WP3 and by creating suitable demonstration experiments that address issues of cooperative robotics. The materials created for the courses are evaluated after their creation with
the implementation of demonstration experiments together with suitable test persons. The materials created are used in the course, which is supervised by the didactics person WMA1.

UAP 8.3: Designing and piloting projects (13.25 PM, months 25-45) 
Transdisciplinary project assignments for students to work on themselves, in particular the use of CNN, supervised/reinforcement learning for the automation of textile production. Based on the results of UAP 8.1 and UAP 8.2, relevant applications in textile technology are identified and suitable project assignments are defined. The quality of the teaching education will be checked and ensured on the basis of the piloting in order to be able to transfer the results (with adjustments if necessary) to regular operations.

Contact person:

WP9: Transdisciplinary teaching education in the Health Care application area.

Milestones:

• Health Data Lake is established (month 8)
• First course has been held (month 20)
• Courses have been evaluated (month 27)
• Courses further developed on the basis of re-registration (month 45)
   

The aim of the WP is a transdisciplinary course for Bachelor's and Master's students from the Faculties of Health Care and Electrical Engineering and Computer Science on analysing data from healthcare.
The amount of newly produced and freely available data in Health Care is enormous. Large volumes of standardised data are routinely generated and transmitted to the relevant bodies for further analysis. Examples of this are annual quality reports from hospitals (Section 137 (3) sentence 1 no. 4 SGB V), annual transmission of service data (Section 21 KHEntgG). In addition,
other (structured and unstructured) data, such as radiological image data or medical datasets from the Kaggle community, are also generated in the context of diagnostics and therapy, which can be used for further analyses. In addition to the core area of healthcare, large amounts of data are generated on a daily basis, for example by health apps or sensors, particularly in the extended area of eHealth, smart health and/or mobile health applications. On the other hand, the market offers a large number of freely available intelligent AI tools that enable students to carry out comprehensive predictive and prescriptive analyses of the data as well as further analyses, such as the quality and process flow according to predefined treatment paths and the determination of deviations through (intelligent) process mining.
The students at the Faculty of Health Care have a high level of expertise in, for example, the collection and analysis of routine data, qualitative requirements for treatment pathways (SOPs), the procedure for process analyses and improvement or the use of imaging techniques. Against this technical background, the methods and potentials of AI are to be jointly developed and communicated in a transdisciplinary manner by means of various case studies in collaboration with the Faculty of Electrical Engineering and Information Technology.

UAP 9.1: Development of a Health Data Lake (4 PM, months 1-8)
The first step is the development of a "Health Data Lake". This is where extensive, structured and unstructured Health Care data is stored and managed for analysis. On the one hand, the freely available data sources are identified and loaded into the data lake. In addition, the existing cooperation networks of the Faculty of Health Care and the eHealth Competence Centre are used to include further data from e.g. hospitals or providers of Health Care software in the data lake.

UAP 9.2: Selection of methodological concepts (4 PM, months 5- 12)
Suitable methodological concepts and AI tools for use in teaching education are selected in parallel. Depending on the level of competence, simple SW tools that enable quick success in data analysis without extensive knowledge should be used, for example, as part of the Bachelor's degree programme. More complex tools that promote methodological and conceptual work in particular should be selected for courses as part of the Master's degree programme. In some cases, existing collaborations can also be expanded further. In addition, it is planned to use complex systems as part of Master's projects, which are already anchored in the curriculum, in order to promote explorative work, for example.

UAP 9.3: Designing teaching education (6.25 PM, months 13-27)
This is followed by the (transdisciplinary) development and didactic design of the course. Different learning paths are required for the respective groups according to the different competence levels, requirements and objectives. The aim here is to develop appropriate didactic concepts and compile basic methodological knowledge. The result of this phase is the implementation of a blended learning course in Moodle, taking into account the results of WP3. The different learning paths enable the course to be used in the various courses in the different degree programmes.

WP 9.4: Use in teaching (3.25 PM, months 13-27)
This is followed by their use in teaching education. External experts from the field of AI, software providers and healthcare companies (hospitals) are involved in teaching education through expert presentations, the specification of problems and questions and discussion of the results. On the one hand, this shows students the practical relevance of the solution and, on the other hand, also diffuses the potential of AI into companies. The subsequent evaluation of the teaching education and practical events is based on the university's standards, supplemented by subject-specific questions.

UAP 9.5: Evaluation and further development (11.5 PM, months 28-45)
The results of the evaluation form the basis for the didactic and content-related examination and revision as part of the quality cycle. Furthermore, the database of the Health Data Lake requires constant updating and expansion. It is also necessary to constantly monitor the innovative software and methods market in the field of AI in order to utilise the latest concepts, methods and tools.

Contact person:

WP10: Overall didactic concept and translation into various teaching-learning formats.

Milestones:

• Status quo survey (month 3)
• Overall didactic concept taking into account internal measures for teaching and learning
• development (month 14)
• First SoTL results of the courses (month 21)
• Handouts and publication of the results (month 48)
   

The initial aim of the WP is to develop an overall didactic concept for the transdisciplinary projects, which will be continuously monitored and evaluated during implementation. Components of the didactic concept include concrete application scenarios in teaching education, minimum didactic standards and concrete requirements for the transdisciplinary projects.
In the spirit of the Scholarship of Teaching and Learning (SoTL), the participating lecturers should research their teaching ideas, implementation strategies and experiences, develop them further from their discipline and feed this knowledge into their subject cultures based on evidence. Expert knowledge gained in this way is disseminated and discussed both within the university and externally. The development of suitable teaching research questions as well as the survey and evaluation phases are didactically flanked
.

UAP 10.1: Basic didactic concept for transdisciplinary projects (8 PM, months 1-14) 
The overall didactic concept for the transdisciplinary projects is developed at the start of the project. To this end, the status quo at the HSNR and the current state of didactics and research are first analysed. Key topics include collaboration and interaction in student teams, transdisciplinary project assignments and teaching/learning formats that take heterogeneous student groups into account. Minimum didactic standards for transdisciplinary projects are also formulated. Suitable higher education didactic qualification programmes will be developed to support lecturers. It will also be determined how the topics of transdisciplinary project assignment and/or AI can be placed in the university's internal Le/Ni teaching project funding programme.

UAP 10.2: Scholarship of Teaching and Learning (18 PM, months 10-45) 
All courses in the course of the project are researched in coordination with university didactics in terms of the Scholarship of Teaching and Learning (SoTL). This applies to all courses in this project, with a particular focus on the transdisciplinary projects from work packages WP6 to WP9 due to their new character. On the one hand, lecturers will receive ongoing didactic advice. On the other hand, they will be supported in analysing their courses by developing suitable teaching research questions and instruments in order to answer detailed didactic questions about the course.

UAP 10.3: Documentation of results, network and transfer (11.5 PM, months 9-48) 
The knowledge gained during the course of the project is continuously summarised in the form of handouts and subsequently published both internally and externally. The university didactics project person is also responsible for designing and organising events to disseminate and multiply the project results both within and outside the university: initial good practices and results are discussed within the university in the collegial exchange format CoLe/Ni and stored in the Good Teaching Practice database. For external dissemination, the results will be prepared in the form of publications and discussed at relevant specialist meetings. Finally, suitable internal university measures for teaching development will be identified to support the project (e.g. internal funding lines).

Contact person:

9th round table event: Technology meets animal welfare: Artificial intelligence in turkey farming

Saving resources, improving animal health, increasing transparency - with these promises, artificial intelligence is finding its way into food production. But does the technology deliver what it promises? And what responsibility does its use entail?
At the round table event, Corinna Kuhlen (KINLI project, HSNR) will present findings from research into the use of AI in turkey farming. The focus is not only on efficiency and quality, but also on the ethical issues that arise when data-based systems intervene in agricultural decisions.

Monday, 13 October 2025, 16:00 - 17:30, in person, Speaker: Corinna Kuhlen, M.Sc.


10. Ringveranstaltung: Vertrauen im Chat? Chatbots in online counselling

Chatbots are increasingly taking over conversations that used to be held exclusively between people, from everyday questions to sensitive situations. But can they really help in psychosocial counselling? Or do they rather raise questions about trust, responsibility and the role of specialists?
In his presentation, Prof Dr Robert Lehmann will show how the KIA project is developing an assistance system to support specialists in their work. The focus will be on specific opportunities such as relieving the burden in everyday life as well as critical points. What does AI do to the sensitive relationship between the person seeking advice and the counsellor? How much technology fits into a situation where closeness and understanding are required?

Monday, 03.11.2025, 16:00 - 17:30, Online, Speaker: Prof. Dr Robert Lehmann


11. Ringveranstaltung: Artificial intelligence and liberal democracy: how do they go together?

Modern AI applications support trade, communication and transport, but are they also good for democracy and freedom? The lecture deals with the consequences of AI applications for political discourse and the preservation of democratic rights of self-determination and discusses the conditions for the use of AI that is oriented towards the common good.

Monday, 08.12.2025, 16:00 - 17:30, Online, Speaker: Prof. Dr Jessica Heesen

12th round table event: Sustainable AI in the world of work: Between resource utilisation, productivity and technology acceptance

Artificial intelligence (AI) is changing our working world - but how sustainable is its use really? The lecture is dedicated to the ecological, economic and social dimensions of artificial intelligence in the working context. It will focus on the opportunities and risks for sustainability as well as specific application examples. The aim is to reflect on the possibilities and limits of responsible use of AI and put them up for discussion.

Monday, 12.01.2026, 16:00 - 17:30, Online, Speaker: Jennifer Link, M.Sc.

Lecture: "Deepfakes & synthetic media: What challenges does AI present us with?"

Speaker: Daniel Bendahan Bitton, academic staff member at the University of Leipzig

When: 13.01.2025, 16:00 - 17:30

Where: digital

Lecture: "Ghostbots & feelings: the use of AI in the grieving process"

Speaker: Prof. Dr habil. Jürgen Karla, Department of Business Administration and Economics

When: 09.12.2024, 16:00 - 17:30

Where: digital

Lecture: "Of dating apps and sex robots: Intimacy through and with AI"

Speaker: Dr Jessica Szczuka; Research Associate at the Department of Social Psychology at the University of Duisburg-Essen

When: 13.05.2024, 16.00-17.30 hrs

Where: Future Work Lab, Petersstraße 122, 47798 Krefeld

Lecture: "Dirt slinging AI - automation on the ground of facts"

Speaker: Dr Anne Mollen, Institute for Communication Science, University of Münster;
Senior Research Associate Algorithm Watch

When: 06.05.2024, 16.00-17.30 hrs

Where: digital

Lecture:"Green and smart: Artificial intelligence from the Lower Rhine in sustainability projects"

Speaker: Jonas Becher, CEO Masasana GmbH

When: Monday, 29.04.2024, 16.00-17.30 hrs

Where: Krefeld South Campus, J-Building, E17

Lecture:"Robotics for care"

Speaker: Dr Peter Remmers, Berlin Ethics Lab, Technical University of Berlin

When: Monday, 11.12.2023, 16.00-17.30

Where: digital

Lecture: "Artificial intelligence on the road - the case of autonomous vehicles"

Speaker: Dr Maike Meyer, Heine Center for Artificial Intelligence and Data Science(HEICAD), Heinrich Heine University Düsseldorf

When: Monday, 30.10.2023, 16.00-17.30 hrs

Where: Krefeld South Campus, Building B, Room E14

Kick-off event: "Is AI writing my term paper now? Opportunities and risks in dealing with generative language models"

Speakers:Dr rer. nat. Michael Gref, Deputy Professor "Artificial Intelligence", HSNR

Mr Pascal Quindeau, academic staff (FB03 Electrical Engineering/Computer Science)

When: 05.06.2023, 16.00-17.30 hrs

Where: digital

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The Hochschule Niederrhein press release