Our vision: Smart university assistances, which as dialogic AI indispensably enrich the university experience and work efficiency for both students and staff in daily operations
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KI im Hochschulkontext: Forschung, Praxis und Campus-Management
The AI Chatbot Study Advisor That Never Clocks Out
Saturday evening, 10 p.m. Jonas wants to study. He’s on the university’s website, wondering whether the part-time degree program also accepts applicants with vocational training instead of a high school diploma. No one answers the phone. The email would have to wait until Monday. And the neighboring university’s tab is just one click away.
This is the exact moment a university wins or loses a prospective student. AI-powered chatbots close this gap — around the clock, multilingual, and grounded in reliable institutional information.
What’s Happening at German Universities Right Now
The trend is real and accelerating. In 2025, HTW Berlin’s AI chatbot for student services was recognized as a best-practice example by the Hochschulforum Digitalisierung (Germany’s Higher Education Forum for Digitalization). The bot runs GDPR-compliant on the university’s own servers, answering questions about deadlines, documents, and contact persons around the clock — without requiring student services staff to intervene.
What AI Can Do — and What It (Still) Can’t
AI chatbots in study counseling aren’t a replacement for human advisors — or are they? Good AI chatbots don’t just deliver fast, reliable initial information. On one hand, they handle the same recurring baseline questions — admission requirements, class scheduling, application deadlines — giving human advisors back the time they actually need for complex individual cases. On the other hand, with a solid and up-to-date knowledge base, a chatbot can do even more: engage with an applicant’s specific life situation, communicate in their language, work through details patiently, and suggest alternative degree programs available at the institution.
TraiNex Applicant Chat: A Real-World Example Built into the Campus System
Many applicant chat solutions are built as standalone systems. As an integrated platform, TraiNex takes a different approach: the applicant chat is not an add-on — it’s a native part of the campus management system. This means the chatbot always knows, without any manual upkeep, when and where a degree program starts, whether it runs as part-time, full-time, or distance learning, and whether spots are still available in the selection process. In addition to context drawn from the campus management system, the chatbot also draws on the university’s linked information pages to generate answers — no duplicate data maintenance, no synchronization issues. When something changes on the university website — for example, in the program curriculum — the chat picks it up automatically.
Proven particularly effective in practice: the chat responds in German, English, Chinese, Turkish, and many other languages — switching automatically when a question is asked in a different language. The application link appears exactly when it fits naturally into the conversation. Or applicants can have a summary of the conversation sent to them, so they can pick it up later or reach out to a human advisor when they’re ready.
What This Means for Universities
The question of whether to invest in AI chatbots for study counseling is no longer relevant — it’s a question of how. Universities that integrate early and close to their core systems benefit on two fronts: they reduce the workload on their advising staff and strengthen their digital presence with prospective students who expect a modern, instantly responsive first point of contact, available 24/7.
German University AI Guidelines Ignore Administration
When a university adopts an AI policy today, the content almost always revolves around the same questions: May students use ChatGPT for assignments? How does the examination office detect AI-generated submissions? What rules apply to oral exams? That focus is understandable — teaching and academic integrity are the core mission of any higher education institution. Yet a comprehensive analysis by the German Higher Education Forum on Digitisation (HFD) reveals that an entire domain has been systematically overlooked: university administration.
30 Policies, 16 Federal States — and a Conspicuous Gap
In May 2026, the HFD published its “Guidelines Check 2026” — a comparative analysis of 30 officially published AI guidelines drawn from all 16 German federal states. The study, produced by the CHE Centre for Higher Education Development, paints a nuanced picture of progress: AI policies are becoming more common, more detailed, and more legally aware. But a pattern stands out.
The HFD describes AI guidelines as “enabling documents”: texts that do not merely regulate, but also contextualise and inspire. Well-crafted guidelines, the analysis argues, give university members orientation in an environment that is changing technologically and legally faster than any statute can keep pace with.
Among the identified blind spots — alongside accessibility and ecological resource consumption — university administration is explicitly named. Almost all documents examined address teaching staff and students. Administrative processes such as campus management, examination organisation, document management, or secretarial work barely feature.
Why This Gap Matters
Administration is the backbone of any university. Without well-functioning processes in examination management, enrolment, or data governance, even the most innovative teaching cannot run smoothly. And it is precisely here that AI holds significant potential — but also significant risk when no clear rules exist.
Consider examination registration: in many institutions, staff manually verify whether a student meets the prerequisites for a given exam — a time-consuming and error-prone process. AI-assisted systems can perform these checks automatically and consistently. But who bears responsibility when the algorithm errs? Which data may the system access? How long are decision logs retained?
Or take document management: universities handle thousands of sensitive records — transcripts, applications, examination papers. AI can help classify documents, monitor deadlines, and reduce processing times. But without clear guidelines, key questions remain open: Which AI tools are GDPR-compliant? Who is authorised to deploy these systems, and who is not?
The silence of current guidelines on these issues is not a niche problem. It means that administrative staff make consequential decisions every day without institutional backing — creating legal uncertainty and blocking meaningful efficiency gains.
The EU AI Act Raises the Stakes
The HFD analysis also points to the EU AI Act, which has imposed binding requirements on AI deployment since 2025. Universities using AI in administration must classify the relevant systems, document their use, and in certain cases implement specific safeguards. Institutions that fail to address this operate in a regulatory grey zone.
Systems that automatically decide on admissions or examination eligibility may fall under the AI Act’s “high-risk” profile — with corresponding documentation and transparency obligations. An AI policy that excludes administration provides no guidance here.
What Universities Should Do Now
The HFD’s report concludes with a call for AI guidelines to be designed dynamically — not as static documents, but as living frameworks that evolve alongside technological development. This applies all the more to administration.
In practical terms: universities should expand their existing guidelines with a dedicated section on AI in administration. This section should define which processes may be AI-supported, which data protection and transparency requirements apply, and how staff will be qualified to work with AI tools.
Campus management systems such as TraiNex — which digitally map examination organisation, attendance tracking, and document management — can play a structuring role in this context. They establish the database foundation on which AI can operate sensibly and compliantly, provided the institutional framework is in place. TraiNex goes further: it can selectively anonymise data and uses GDPR-compliant AI models integrated directly into its processes, ensuring that sensitive data never leaves the controlled environment of the campus management system.
Conclusion
The Higher Education Forum on Digitisation has delivered an important stocktaking with its Guidelines Check 2026. The finding is clear: German higher education has made progress in strategic AI integration over the past two years — but administration has been left behind. It is time to correct that.
The pattern is not new. An empirical survey conducted in 2023 as part of a publication on AI maturity already showed that teaching is penetrated by AI significantly faster than administration. In the AI Maturity Matrix for Higher Education, many institutions are still best described as “AI Teaching Experimenters” — universities that teach about AI use. Attaining the status of “AI Process Professional” — an institution that has systematically integrated AI into administrative processes — remains the exception.
→ Workshop materials on the AI Maturity Matrix — freely available
→ Full HFD report: “AI Guidelines in Check 2026” (in German)
EU AI Act from 2 August 2026: What Universities Need to Know
From 2 August 2026, further key provisions of the EU AI Act apply. Does this affect us as a university? Do we now need to have our AI tools reviewed? What happens if we do nothing? The short answer: it very much depends on what you do with AI. The longer answer is here.
What Applies from 2 August 2026?
Anyone who interacts with an AI system must in principle be able to recognise that they are dealing with AI. A chatbot for students or a digital assistant should therefore identify itself clearly as an AI system at the latest at the beginning of the conversation – unless this is already obvious.
For universities, this means above all:
- Clearly label AI chatbots,
- Do not present AI-generated media as authentic recordings,
- Define responsibilities for AI systems in use,
- Document AI deployment in a traceable manner.
AI Literacy Remains Mandatory
Since February 2025, institutions have been required to offer measures to promote the AI competence of their staff. From August 2026, compliance with this obligation can be more strictly monitored.
A specific certificate is not required. However, the following are advisable:
- Internal rules for ChatGPT and other AI tools,
- Training on data protection, hallucinations and confidential data,
- Special briefings for sensitive areas such as the examinations office, admissions and HR,
- Documentation of the measures offered.
- TraiNex supports you with the e-tutorial “AI at the University in 10 Chapters”. You can store this directly in TraiNex for teaching staff and administrative employees, have them complete it and automatically document the evidence. Contact us.
Are Students Affected?
When using generally available AI tools privately, students are normally neither providers nor operators within the meaning of the AI Act. They are, however, protected as data subjects. If a university uses AI for admissions, examination assessment or examination monitoring, obligations arise for the university.
What Applies to Teaching Staff?
Teaching staff are likewise neither providers nor operators within the meaning of the AI Act. However, they must comply with the university’s requirements, data protection law, copyright law and examination regulations. In order to assess the use of AI in these areas, participation in further training is necessary.
How TraiNex Classifies AI Functions
At TraiNex, we assess every AI function based on its specific purpose and impact.
AI-supported search functions, text suggestions, summaries and general chatbots assist users without themselves making decisions about admissions, grades or sanctions. These risk classes are largely permitted without issue.
The picture may look different when an AI result is used directly for consequential decisions. An automatic evaluation of attendance data, for example, is to be assessed differently if examination eligibility is derived from it without further review.
That is why we specifically examine:
- Purpose and data used,
- Impact on students and staff,
- Degree of automation,
- Human control options,
- Logging and traceability,
- Data protection and IT security.
What Applies to AI Agents?
AI agents do not form a separate risk category in the AI Act. Here too, what matters is which tasks they take on.
An agent that prepares documents, transfers information or proposes changes for approval is to be assessed differently from an agent that independently rejects admissions, changes grades or sanctions individuals. The latter would be too autonomous and too risky under the AI Act. To achieve an acceptable and permitted risk class, at minimum human approval would be mandatory.
At TraiNex AI agents, we therefore rely on:
- clearly defined areas of responsibility,
- graduated access rights,
- complete logging,
- human approvals for consequential actions,
- the ability to stop or reverse actions,
- restriction of AI autonomy.
See waas.campus-management-system.de
What Universities Should Do Now
Universities and educational institutions should now:
- Record the AI systems they use,
- Document their specific purposes of use,
- Correctly label AI chatbots and generated content,
- Offer staff appropriate training and guidelines,
- Particularly scrutinise applications that influence admissions, examinations or HR decisions.
Conclusion
From 2 August 2026, transparency, labelling and documented accountability will become more important for universities above all. Next milestone: the comprehensive high-risk requirements for the education sector apply from 2 December 2027.
AI Agents in Campus Management: Autonomous Digital Employees?
AI agents are more than just smart chatbots. In May 2026, the Hochschulforum Digitalisierung published a widely discussed paper on “Agentic AI in the Higher Education System” — and it hits on what many university administrators already sense: the next wave of AI integration is not about interactively answering questions in a chat, but about goal-oriented execution of tasks using available tools. Predefined AI workflows are helpful here, but even without them, the agent searches for a solution, monitors states, acts autonomously, and triggers actions in campus management. This will affect administrative processes more than students directly. What does this mean for campus management?
Chatbot vs. Agent: A Fundamental Difference
A chatbot responds. An AI assistant follows a workflow. And an AI agent? An AI agent acts. That sounds simple, but it is the crucial difference. While a chatbot reacts to a question and then waits, an AI agent can receive a task, independently plan and execute multiple steps — and in doing so, access data, systems, tools, and other agents. It operates essentially like an employee.
Concretely: A chatbot says “Your exam registration deadline is March 15.” An AI agent independently checks whether all prerequisites are met, enters the registration, sends a confirmation, and notifies the examination office — without a human having to trigger each step.
What Does This Mean for Campus Management?
In university operations, there are many processes that still run manually or semi-automatically today — processes that are perfectly suited for AI agents:
- Exam registration with prerequisite checking: The agent independently verifies whether participation requirements are met, and automatically registers the student — or escalates borderline cases to a human administrator.
- Document management: Incoming applications are classified, routed, and — where possible — directly processed, before a human provides final approval.
- Timetable optimization: Based on room availability, lecturer schedules, and student numbers, the agent generates planning proposals or directly reassigns rooms.
- Student support tasks: Early warning system for declining attendance or impending exam failures — including automatic notification to programme directors.
- FAQ and service communication: Frequently asked questions are answered fully and in context, without tying up secretariat capacity.
- Programme setup: Module handbooks are reviewed and transferred into campus management including schedules and lecturers, who are informed and contacted as needed.
- General press monitoring: Search for interesting topics for our social media presence, send us 3 proposals each week and draft text and image for Instagram — posting after approval.
Crucially: the agent acts within clearly defined boundaries and with human oversight. It is not merely a responder or assistant — the agent is a decision-maker. And every decision must be traceable after the fact (Explainable AI).
TraiNex and AI Agents: WAAS — Workforce as a Service
At TraiNex, we are developing this approach further under the concept of WAAS — Workforce as a Service. The idea: AI agents receive onboarding, a workplace, computer and email address, as well as a TraiNex account with assigned permissions. The TraiNex AI agent takes on defined task packages in campus management, acting in its own name on behalf of the institution — documented, traceable, and compliant.
Initial real-world tests have been very successful. TraiNex AI agents respond to emails, read attachments, enter data, or check lists — typically overnight, either automatically or on demand. The TraiNex AI agents know TraiNex’s functions inside and out. There is a team of AI agents guided by an AI team leader, who also assigns tasks to humans when necessary — for example, to make a phone call. Curious? More at waas.ki-campus.eu
Conclusion
AI agents are no longer a science-fiction concept. They will fundamentally transform campus management systems in the coming months — as digital employees who initially take over routine tasks. Universities that engage with this now and test legally compliant use cases will be well positioned in the competition.
KI-LOTSE: A New National Advisory Hub for AI Strategy at German Universities
Where should a university start when it wants to integrate artificial intelligence systematically — not just in one course or department, but across research, teaching, and administration? That question has been hard to answer. Since spring 2026, there is a dedicated national resource: KI-LOTSE.
What is KI-LOTSE?
KI-LOTSE is a project of the German Rectors’ Conference (HRK), funded by the Federal Ministry for Research, Technology and Space (BMFTR). The name is intentional: a “Lotse” is a pilot or navigator — and that is exactly what the initiative offers. As the project puts it: “Guidance center for orientation, technology, service and expertise on artificial intelligence in higher education.”
The primary audience is university leadership and strategic decision-makers: people who are less interested in the next AI tool review and more concerned with questions like: What infrastructure does our institution need? What does the EU AI Act require — or prohibit? How do we govern AI use in examinations?
What KI-LOTSE Offers
The platform covers four thematic areas — legal questions, infrastructure, personality development, and AI & didactics — alongside two direct service tracks:
Strategic advisory: University leadership teams can now apply for free, on-site consultation sessions lasting one or two days. The offer is designed for rectorates and presidiums looking to embed AI into institutional governance — not as a side project, but as part of a coherent strategic framework.
Legal support: A dedicated entry point for legal questions surrounding AI in higher education — from data protection to examination law and the EU AI Act. Not a law firm, but reliable orientation and practical guidance — free of charge.
On top of this, KI-LOTSE runs think tank activities: thematic working groups (including one focused on “AI and Examinations”) and a Good Practice database to ground strategic decisions in concrete examples from the field.
Why This Matters
There is no shortage of AI-related content for higher education — toolkits, webinars, prompt guides. What is often missing is the governance layer: how does an institution decide what to do with AI — and what not to do? How does it create clarity and trust for faculty, students, and administration?
KI-LOTSE addresses exactly that. And it does so with the backing of the HRK, an organization with the institutional mandate to think at a system level. That combination — credibility, expertise, and strategic scope — is rare and welcome.
Institutions already working on AI readiness and strategy may also find value in the freely available workshop materials on the AI Maturity Matrix that we published here. The two resources complement each other well: the Maturity Matrix helps institutions assess where they currently stand, while KI-LOTSE provides the guidance and framework to decide where to go next.
Looking Ahead
KI-LOTSE launched in April 2026 — it is a young project, but the concept is sound. A bundled, HRK-operated service hub that does not add to the noise, but helps universities navigate it. We wish the team every success and are glad this resource now exists.