Campus-Management

Algorithmic Intelligence vs. Artificial Intelligence — When Is the “Old-Fashioned” Method Better?

Claudia

This blog reports on AI in campus management. That includes the question of where AI reaches its limits – and where proven rules are the better solution. Because behind the gloss of the new technologies lurks an uncomfortable question: for many core processes at universities, classic algorithmic intelligence is not just sufficient — it is simply better.

By algorithmic intelligence we mean deterministic, rule-based procedures: a clearly defined input is processed according to explicit rules and reproducibly leads to a uniquely determined result. Learning AI systems, by contrast, derive their results from patterns in training data and work probabilistically, producing a likely result.
Which kind of intelligence fits which problem in higher education?

Illustration comparing an algorithmic, rule-based computation path with a neural network
Algorithmic computation path vs. neural network

When the Nail Turns Out to Be a Screw

“We solve the problems with AI,” some startups promise. It sounds modern, progressive, future-proof. No wonder AI shows up more and more often in university strategies and IT roadmaps. But as so often, reality is more complex: after the peak of inflated expectations about AI and the trough of disillusionment comes the plateau of productivity. And on that plateau it becomes clear which kinds of problems can be solved with language models and which are better solved with algorithmic rule intelligence (algorithms, for short).
Roughly five cases can be distinguished:
A) Problems that are already solved with algorithms and that language models could NOT solve (rule task, classic)
B) Problems that are already solved with algorithms and that language models could NOT solve better (rule task, advantageous)
C) Problems that are already solved with algorithms and that language models could solve better (rule-to-AI task, added value)
D) Problems that CANNOT be solved with algorithms but that language models could solve (AI task, new)
E) Problems that CANNOT be solved with algorithms and that language models could NOT solve either

Established campus management systems have long had field-tested solutions for A and B. For them, AI becomes exciting above all for C and D. Some startups, on the other hand, advertise with “We do it with AI.” That can be misleading in two ways: not every task is solved better by AI. And sometimes “with AI” merely means that someone had the classic rule set programmed via vibe coding.

A decision logic for introducing AI-based solutions could look like this: Is there already a rule set that reliably fulfills the task? Then there needs to be a concrete need for improvement before a language model can even be considered as a replacement. If there is no solution yet, one should first check whether a clear rule set can be developed. Only when the task involves open language, unstructured information or patterns that are hard to put into rules does a learning model become particularly interesting. And even then, its benefit must be measured against the concrete result.

In other words: AI in the sense of machine learning and deep neural networks is a powerful tool for problems in which patterns have to be recognized in large amounts of data, language has to be understood or complex relationships have to be approximated. But not every problem is an AI problem. And not every “clever” solution has to be a learning AI solution. No – a proven rule set can be the better solution: simpler, more transparent and more reliable. Where no rule set exists yet, it is even worth checking whether one can be developed – before deploying a language model.

Decision tree for choosing between a rule-based method and AI, leading to cases A to F
Decision tree (German labels): Is a proven method available? Is a language model useful? Is there demonstrable added value? The paths lead to A/B “Keep the classic method”, C “Add AI selectively”, D “Develop an AI solution”, E “Currently unsolved” and F “Develop a rule set”.

Example 1: Calculating the Average Grade

A weighted average grade according to the examination regulations is a mathematically fully defined problem. Weightings, rounding and pass thresholds are fixed. The result can be determined exactly and reproducibly with a calculation procedure. There is exactly one correct answer — and it can be calculated exactly with an algebraic procedure.

Using an AI model here that “learns” from past grading would not only be unnecessarily costly. It would simply be wrong: a model would introduce noise into the calculation where mathematical exactness is required. A grade of 2.3 under the examination regulations (on the German scale from 1.0 to 5.0) is 2.3 — not an estimate, not an approximation. Not to mention that new examination regulations with a new grade calculation would first require generating a large amount of training data.

Systems like TraiNex deliberately rely on algorithmic intelligence here: the rules of the examination regulations are coded exactly, and the calculation is deterministic and fully traceable at any time. New examination rules can be integrated quickly. For examination offices and accreditation bodies, traceability is not optional — it is mandatory.

Example 2: Detecting Student Dropout Early

Early warning systems for dropout risk are a popular field of application for AI. A model could analyze exam results, attendance and other data to produce a risk assessment.

First, however, it must be clear what task the system is supposed to fulfill. Is it about identifying, according to defined criteria, a reason to offer support? Then a rule-based warning system can be the superior solution: if a student has failed three exams and, at the same time, their attendance rate drops below 50 %, an alert is triggered — transparent, traceable, explainable. The rule is known, can be defined by the university itself, and its application can be fully documented.

If, on the other hand, the goal is to predict future dropouts as accurately as possible, that is a different task. AI prediction models can be better here, because it is about changing patterns in large amounts of data. But why the model flagged a student as a “dropout risk” cannot easily be explained — and that is delicate both in terms of data protection law and ethics. Especially with personal risk predictions, the requirements for transparency, traceability and data protection rise.

Example 3: Checking Exam Eligibility

May this student register for this exam? Have they completed the prerequisites, are they in the right semester, are all required coursework components in place? This, too, is not an AI problem. It is a rule-checking problem.

The eligibility requirements are precisely defined in the examination regulations — and an algorithm can apply these rules to thousands of registrations within seconds. Eligibility rules are configured per degree program or federal state, every registration is checked against them, and rejections are communicated with a justification. If errors occur, the rule set can be adjusted quickly and safely.

An ML model that “learns” from past eligibility decisions would not only introduce unnecessary complexity here — and with it unnecessary energy consumption and unnecessary costs. It would also carry the risk of reproducing historical injustices or making wrong decisions that cannot be explained and may not hold up legally.

The regulatory perspective is also interesting: the EU AI Act classifies certain AI systems in education as high-risk systems — among other things, when they decide on access or admission or evaluate learning outcomes. Where the examination regulations already specify clear rules, a deterministic algorithm can be not only simpler and more accurate, but also considerably less problematic from a regulatory point of view.

The Right Technology for the Right Problem

The mistake does not lie in using AI — it lies in using it indiscriminately. AI is powerful when training data is available, when patterns are to be recognized that humans cannot grasp, when language needs to be understood, or when large amounts of unstructured data need to be analyzed.

Classic algorithms are superior when the problem is mathematically fully defined, when exactness is required, when traceability is legally or ethically necessary — and when data protection and transparency are the priority.

For campus management systems, this means: grade calculation and the application of clearly defined eligibility rules belong in the hands of rule-based procedures. An early warning alert based on defined criteria can be implemented the same way. AI can complement these where it brings a demonstrable advantage — for example in understanding freely worded advisory requests or in an individual learning dialogue. And for all the problems where proven rule-based procedures do not deliver a good solution, the use of language models should be considered.

This categorical thinking can help when deciding for or against AI. As a professional, however, you should then reconnect the categories, because in the medium term the combination of rule set and language model is likely to be particularly promising. The SMARTA architecture, for example, uses the “Algorithmic Prompt Composer (APC)”, which controls a language model in a rule-based way with database access and then processes its response according to rules. Classic loops with termination conditions are also a good framework for keeping AI agents running.

Conclusion: Not the Newest Technology — the Right One

The question is not whether to use AI. The question is where it really helps — and where an existing or newly developed rule set is the better solution, or whether AI brings more risk than benefit. Universities that understand this difference make better technology decisions. And they build systems that students, teachers and accreditors can trust.

If a rule is enough, you don’t need a language model. When rules reach their limits, AI can show its strengths. Being modern does not mean building AI in everywhere. Being modern means solving the problem correctly. Sometimes with AI, sometimes without AI, sometimes in combination with a rule. And only those who know the world of rule-based algorithms can also solve a university’s problems sensibly with AI.

Algorithmic Intelligence vs. Artificial Intelligence — When Is the “Old-School” Method Better?

Claudia

This blog covers AI in campus management. That includes the question of where AI reaches its limits — and where proven rules are the better solution. Behind the shine of new technology lurks an uncomfortable question: for many core processes at higher education institutions, classic algorithmic intelligence isn’t just sufficient — it is simply better.

By algorithmic intelligence we mean deterministic, rule-based methods: a clearly defined input is processed according to explicit rules and reproducibly leads to one clearly defined result. Learning AI systems, by contrast, derive their output from patterns in training data and work probabilistically, arriving at a likely result.
Which kind of intelligence fits which higher-education problem?

When the Nail Becomes a Screw

“We solve problems with AI” is the pitch of many a startup. It sounds modern, forward-looking, future-proof. No wonder AI keeps turning up more often in university strategies and IT roadmaps. But, as so often, reality is more complicated: after the peak of inflated expectations and the trough of disillusionment comes the plateau of productivity. And on that plateau it becomes clear which kinds of problems language models can actually solve — and which are better solved with algorithmic rule-based intelligence (algorithms, for short).

Five broad cases can be distinguished:

  • A) Problems already solved with algorithms that language models could NOT solve (a classic rule task)
  • B) Problems already solved with algorithms that language models could not solve any better (a rule task, advantageous as is)
  • C) Problems already solved with algorithms that language models could solve better (a rule-to-AI task, added value)
  • D) Problems algorithms cannot solve, but that language models could (a new AI task)
  • E) Problems that neither algorithms nor language models could solve

One decision logic for introducing an AI-based solution could be: Is there already a set of rules that reliably performs the task? Then a concrete need for improvement has to exist before a language model is even considered as a replacement. If no solution exists yet, the first step should be to check whether a clear set of rules can be developed. Only once the task involves open-ended language, unstructured information, or patterns that are hard to formalize does a learning model become genuinely interesting. And even then, its benefit has to be measured against a concrete result.

In other words: AI in the sense of machine learning and deep neural networks is a powerful tool for problems where patterns in large volumes of data need to be recognized, language needs to be understood, or complex relationships need to be approximated. But not every problem is an AI problem. And not every “clever” solution has to be a learning AI solution. A proven set of rules can be the better solution: simpler, more transparent, and more reliable. Where no set of rules exists yet, it is even worth checking whether one can be developed — before deploying a language model.

Example 1: Calculating a Grade Point Average

A weighted grade point average calculated under examination regulations is a mathematically fully defined problem. Weightings, rounding, and pass thresholds are fixed. The result can be determined exactly and reproducibly with a computational procedure. There is exactly one correct answer — and it can be calculated exactly with an algebraic method.

Using an AI model here that “learns” from past grading would not just be unnecessarily complex. It would simply be wrong: a model would introduce noise into a calculation that demands mathematical exactness. A grade of 2.3 under the examination regulations is 2.3 — not an approximation, not an estimate. Not to mention that a new set of examination regulations with a new grading formula would first require generating a large volume of training data.

Systems like TraiNex deliberately rely on algorithmic intelligence here: the examination regulations are encoded exactly, the calculation is deterministic, and it remains fully traceable at any time. New examination rules can be integrated quickly. For examination offices and accrediting bodies, traceability isn’t optional — it’s mandatory.

Example 2: Detecting Dropout Risk Early

Early-warning systems for the risk of dropping out are a popular field for AI. A model could evaluate exam results, attendance, and other data to produce a risk assessment.

But first it has to be clear what task the system is meant to perform. Is it about flagging, by defined criteria, an occasion for a support offer? A rule-based warning system can be the superior solution here: if a student has failed three exams and attendance falls below 50 percent at the same time, a flag is triggered — transparent, traceable, explainable. The rule is known, can be defined by the institution itself, and its application can be fully documented.

If, instead, the goal is to predict future dropouts as accurately as possible, that is a different task. AI forecasting models can do better here, because it is about shifting patterns across large datasets. But why the model flagged a particular student as a “dropout risk” is not easily explained — and that is sensitive both under data protection law and ethically. Especially with personal risk predictions, the requirements for transparency, traceability, and data protection rise sharply.

Example 3: Checking Admission Requirements

May this student register for this exam? Have the prerequisites been met, are they in the right semester, are all the required coursework credits on file? This, too, is not an AI problem. It is a rule-checking problem.

Admission requirements are defined exactly in the examination regulations — and an algorithm can apply these rules to thousands of registrations every second. Admission rules are configured per degree program or state, every registration is checked against them, and rejections are communicated with a reason. If an error is found, the rule set can be adjusted quickly and safely.

A machine-learning model that “learns” from past admission decisions would not only introduce unnecessary complexity here, along with unnecessary energy consumption and unnecessary cost. It would also risk reproducing historical injustices or making decisions that are neither explainable nor, in some cases, legally defensible.

The regulatory angle is worth noting too: the EU’s AI Act classifies certain AI systems in education as high-risk — among other things, when they decide on access or admission, or evaluate learning outcomes. Where examination regulations already set out clear rules, a deterministic algorithm can be not only simpler and more accurate, but also far less problematic from a regulatory standpoint.

The Right Technology for the Right Problem

The mistake isn’t using AI — it’s using it indiscriminately. AI is powerful when training data is available, when patterns need to be recognized that people can’t easily perceive, when language needs to be understood, or when large volumes of unstructured data need to be evaluated.

Classic algorithms are superior when a problem is mathematically fully defined, when exactness is required, when traceability is legally or ethically necessary — and when data protection and transparency are the priority.

For campus management systems, that means: grade calculation and the application of clearly defined admission rules belong in the hands of rule-based methods. An early-warning flag based on defined criteria can be implemented the same way. AI can add value where it brings a demonstrable advantage — for instance, in understanding freely worded advising requests, or in an individual learning dialogue. And for every problem where proven rule-based methods don’t offer a good solution, deploying a language model is worth considering.

This kind of categorical thinking can help when deciding for or against AI. As a practitioner, though, you should then reconnect the categories — because in the medium term, the combination of rules and language models is likely to be especially promising. TraiNex’s own SMARTA architecture, for example, uses an “Algorithmic Prompt Composer (APC)” that steers a language model on a rule basis with database access, and then processes its response by rules as well. Classic loops with exit conditions are also a good scaffold for keeping AI agents running reliably.

Conclusion: Not the Newest Technology — the Right One

The question isn’t whether to use AI. The question is where it truly helps — and where an existing or newly developed rule set is the better solution, where it brings more risk than benefit. Institutions that understand this distinction make better technology decisions. And they build systems that students, faculty, and accreditors can trust.

If a rule is enough, you don’t need a language model. If rules reach their limits, AI can show its strength. Being modern doesn’t mean building AI into everything. Being modern means solving the problem correctly — sometimes with AI, sometimes without it, sometimes combined with a rule. And only those who know the world of rule-based algorithms can also use AI to solve a university’s problems in a meaningful way.

→ Read the original German article

German University AI Guidelines Ignore Administration

Claudia

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

Claudia

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.

Lecturer and student looking at digital AI icons with EU map in the background – EU AI Act and universities
Illustration: AI-generated image (Dr. Maex / TraiNex) – EU AI Act and universities in the digital age

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:

  1. Record the AI systems they use,
  2. Document their specific purposes of use,
  3. Correctly label AI chatbots and generated content,
  4. Offer staff appropriate training and guidelines,
  5. 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?

Claudia

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?

AI agents and humans working together as a team with TraiNex
AI agents in campus management — click to enlarge

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.

→ Full paper by Hochschulforum Digitalisierung (PDF)