Responsible AI Use at the St. Pölten UAS
Rules, Data Protection, Tools
Generative AI tools such as ChatGPT or Copilot can be helpful in work and study contexts – for example, for generating ideas, structuring content, or increasing efficiency.
However, their use requires special caution: Sensitive or personal information should not be entered, as these tools do not guarantee confidentiality.
Responsible use involves considering aspects such as information security, data protection, legal requirements, copyright, and academic integrity.
Guidance is provided by the data classification guide of the St. Pölten UAS – it supports risk assessment and the selection of appropriate protective measures.
You can find more details further down on this page.
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AI Tools | Data Classification Guide for AI Tools | AI Glossary
AI Tools
Details on data classification (referred to "Classification" in the table) can be found further below.
| Tool | Description | Classification | Costs? |
|---|---|---|---|
| Adobe Firefly | Create images, text effects, and videos within Adobe creative applications using AI | Level 4 only | Yes |
| Big Interview | AI-assisted interview preparation with mock interviews and personalised feedback systems | Level 4 only | Yes |
| Canva | AI-supported design platform for creating graphics, images, videos, and presentations | Level 4 only | Only for the premium version |
| ChatGPT | Conversational AI assistant for text generation, analysis, and creative tasks | Level 4 only | Only for the premium version |
| Claude | Advanced AI assistant for text analysis, programming, and complex reasoning tasks | Level 4 only | Only for the premium version |
| Consensus | AI-assisted research tool for finding and analysing scientific literature and studies | Level 4 only | Only for the premium version |
| Copilot | Conversational AI assistant from Microsoft for text generation, analysis, and creative tasks | safe when logged in with the St. Pölten UAS account | Level 4 only | No |
| Copilot for Microsoft 365 | AI assistant integrated into Microsoft 365 applications for productivity enhancement | Levels 2–4 | Yes |
| Copilot with Enterprise Data Protection | Conversational AI assistant from Microsoft for text generation, analysis, and creative tasks | safe when logged in with the St. Pölten UAS account | Levels 2–4 | Not for the St. Pölten UAS |
| Cursor | AI-powered code editor with intelligent code completion and generation capabilities | Level 4 only | Only for the premium version |
| DALL-E | OpenAI's image generation model creating images from detailed text descriptions | Level 4 only | Yes |
| DeepL Translator/Write | Advanced AI translation service supporting multiple languages with high accuracy | Level 4 only | Only for the premium version |
| Eleven Labs | AI voice generation and speech synthesis platform for realistic audio content | Level 4 only | Only for the premium version |
| Elicit | AI research assistant for literature reviews and scientific paper analysis | Level 4 only | Only for the premium version |
| Gamma | AI presentation creator that produces slides and documents from simple text prompts | Level 4 only | Only for the premium version |
| GitHub Copilot | AI pair programmer that offers code suggestions and completions within development environments | free for verified students and educators | Level 4 only | Not for the St. Pölten UAS |
| Google Gemini | Google's conversational AI assistant for text generation, analysis, and creative tasks | Level 4 only | Only for the premium version |
| Gradescope | AI-powered grading and plagiarism detection platform for educational assessments | Levels 2–4 | Yes |
| Grammarly | AI writing assistant for grammar checking, style improvement, and content optimisation | Level 4 only | Only for the premium version |
| LitMaps | Research visualisation tool that creates literature maps to explore academic paper connections | Level 4 only | Only for the premium version |
| Midjourney | Premium AI image generator known for artistic and creative visual outputs | Level 4 only | Yes |
| nanoHUB | Educational platform that provides computational tools and simulations for scientific learning | Level 4 only | No |
| NotebookLM | Google's AI note-taking and research assistant for organising and analysing information | Level 4 only | Only for the premium version |
| Perplexity | AI search engine that gives comprehensive answers with source citations and references | Level 4 only | Only for the premium version |
| PlayHT | Text-to-speech AI platform that creates natural-sounding voice recordings from written content | Level 4 only | Only for the premium version |
| PopAi | Multi-purpose AI assistant for document analysis, chat, and content generation | Level 4 only | Only for the premium version |
| QuestionWell | AI tool that generates educational questions and assessments from input content | Level 4 only | Only for the premium version |
| Research Rabbit | Literature discovery platform that uses AI to find and connect relevant research papers | Level 4 only | No |
| ScholarAI | AI summarisation tool that converts long articles into structured summary flashcards | Level 4 only | Only for the premium version |
| Scholarcy | An online summarising tool that generates and converts long articles into summary flashcards | Level 4 only | Only for the premium version |
| SciSpace | Comprehensive research platform with AI tools for paper discovery and analysis | Level 4 only | Only for the premium version |
| Scite | Citation analysis platform that helps researchers to evaluate and understand scientific literature | Level 4 only | Yes |
| Semantic Scholar | AI-assisted academic search engine for finding and analysing scientific publications | Level 4 only | No |
| Stable Diffusion | Open-source AI image generator that creates high-quality images from text descriptions | Level 4 only | Yes |
| Wolfram Alpha | Computational knowledge engine that provides mathematical calculations and factual answers | Level 4 only | Only for the premium version |
Data Classification Guide for AI Tools
Data classification is a systematic approach to organising information based on its sensitivity level and the potential impact of unauthorised disclosure. It helps ensure the responsible use of AI tools in accordance with the AI guidelines of the St. Pölten UAS and the requirements of the EU AI Act.
The following classification provides guidance on which types of information are suitable for AI-supported applications – and which are not.
Overview:
- Level 1 – Highly Sensitive Information
- Level 2 – Sensitive Information
- Level 3 – Security-Relevant Information
- Level 4 – Non-Sensitive Information
Level 1 – Highly Sensitive Information
Most restricted | Highest security required
Examples:
- Health information
- Biometric data
- Financial identifiers
Not permitted:
- Biometric identification and categorisation
- Emotion recognition (except for medical or security purposes)
- Automated collection of biometric data from the internet
- Processing of special categories of personal data without pseudonymisation and a clearly defined purpose
Level 2 – Sensitive Information
Confidential | Legal protection required
Examples:
- Employee and student data
- Examination results, applications
- Unpublished research data
Not permitted:
- Social scoring by AI
- Manipulative systems or those exploiting human vulnerabilities
- Emotion recognition in educational contexts
- Risk assessments based solely on profiling
Level 3 – Security-Relevant Information
Internal use | Potential security implications
Examples:
- Administrative and operational data
- Infrastructure and security information
- Unpublished academic content
Required:
- Transparency about AI usage
- Compliance with GDPR
- Avoidance of manipulative applications
- Human control in risk assessments
Level 4 – Non-Sensitive Information
Public or intended for publication
Examples:
- General business information
- Published research and teaching materials
- Publicly accessible data
Basic restrictions also apply here:
- Users must be informed about AI usage
- Misleading applications are prohibited
- Data protection principles such as data minimisation must be observed
- The use of AI must be documented
AI Glossary
Familiarise yourself with key AI concepts using the glossary:
| Term | Definition |
|---|---|
| Artificial Intelligence (AI) | The simulation of human intelligence in machines that can perform tasks such as learning, drawing conclusions, and problem-solving |
| Artificial General Intelligence (AGI) | A theoretical AI system that can perform any intellectual task a human can, including reasoning, learning, and adaptability |
| Artificial Narrow Intelligence (ANI) |
Also known as weak AI, it is designed to perform a single task (e.g., speech recognition, recommendation algorithms) |
| Artificial Super Intelligence (ASI) | A hypothetical AI that surpasses human intelligence in all domains, including creativity and emotional intelligence |
| Algorithm | A set of step-by-step instructions followed by a computer to complete a specific task |
| Bias | Systematic errors or unfair preferences in AI outputs due to biased training data or flawed algorithms |
| Burstiness | The irregular occurrence of high-quality AI-generated content followed by less coherent outputs |
| Business Value of AI | The economic and strategic benefits of AI across industries, including automation and efficiency gains |
| Chatbot | A software application that mimics human conversation through text or voice interfaces |
| ChatGPT | A generative chatbot developed by OpenAI that generates human-like text based on context |
| Computer Vision | A field of AI that enables machines to interpret and understand visual information from the world |
| Conversational AI | AI systems designed to simulate human-like conversations, such as chatbots and virtual assistants |
| Data Augmentation | Techniques used to increase the diversity of training data by modifying existing data samples |
| Data Mining | The process of analysing large datasets to find patterns, relationships, and useful insights |
| Data Science | The interdisciplinary field that uses statistics, AI, and computing to analyse and interpret complex data |
| Deep Learning | A subset of machine learning that uses neural networks with multiple layers to learn patterns in data |
| Embeddings | Representations of words, images, or other data as numerical vectors in AI models |
| Ethical AI | The study and implementation of AI systems that are fair, unbiased, and aligned with human values |
| Explainability (XAI) | The ability of an AI system to explain its decision-making process in a way humans can understand |
| Few Shot Learning | A machine learning technique where a model learns from a very small amount of labelled data |
| Fine-Tuning | Adjusting a pre-trained AI model with additional training data to specialise it for a particular task |
| Foundation Model | A large-scale AI model trained on diverse datasets that can be adapted for different tasks (e.g., GPT, BERT) |
| Generative AI | AI that creates new content such as text, images, music, and code |
| Generative Adversarial Network (GAN) | A type of AI model where two networks compete to improve the quality of generated data |
| Generative Pre-trained Transformer (GPT) | A type of AI model that predicts text using deep learning and a transformer architecture |
| Hallucination | When an AI model generates incorrect, misleading, or entirely fictional information |
| Heat Map | A visualisation tool used in AI to highlight problematic areas in a dataset or a model’s decision-making |
| Hyperparameters | Adjustable settings in an AI model that influence learning, such as learning rate and number of layers |
| Inference | The process of using a trained AI model to make predictions on new data |
| Internet of Things (IoT) | A network of connected physical devices that collect and exchange data |
| Knowledge Graph | A structured representation of information that shows relationships between different entities |
| Language Model (LM) | An AI system designed to understand, generate, and process human language |
| Large Language Model (LLM) | A powerful AI model trained on vast amounts of text data to generate human-like text (e.g., GPT-4) |
| Latent Space | The abstract space in which AI models organise and process complex data representations |
| Machine Learning (ML) | A branch of AI where algorithms learn from data and improve performance without explicit programming |
| Model Drift | The phenomenon where an AI model’s accuracy degrades over time due to changing data patterns |
| Multimodal AI | AI that processes multiple types of data inputs such as text, images, and audio |
| Natural Language Processing (NLP) | AI that enables computers to understand, interpret, and generate human language |
| Neural Network | A computer system modelled after the human brain that processes information through layers of nodes |
| Neuro-Symbolic AI | A hybrid AI approach that combines neural networks and symbolic reasoning |
| Output | The result generated by an AI system such as text, images, or predictions |
| Overfitting | When an AI model learns patterns too specific to training data, reducing its ability to generalise |
| Parameter | A variable that AI models adjust during training to improve predictions |
| Perplexity | A metric used to measure how well a language model predicts text; lower perplexity indicates better performance |
| Positional Encoding | A technique that helps AI models understand word order in text data |
| Predictive Analytics | The use of AI and statistics to forecast future outcomes based on historical data |
| Probabilistic AI | AI that incorporates probabilities in decision-making to handle uncertainty |
| Prompt | The input text given to an AI system to generate a response |
| Prompt Engineering | The practice of designing effective prompts to guide AI models toward desired outputs |
| Quantum AI | The application of quantum computing to enhance AI algorithms and processing capabilities |
| Reinforcement Learning (RL) | A type of ML where AI learns by receiving rewards or penalties for actions taken |
| Self-Supervised Learning | A machine learning approach where AI generates its own labels from raw data |
| Semi-Supervised Learning | A mix of supervised and unsupervised learning where AI learns from both labelled and unlabelled data |
| Sentient AI | A hypothetical AI system capable of consciousness, emotions, and self-awareness |
| Supervised Learning | A machine learning approach where AI learns from labelled training data |
| Synthetic Data | Artificially generated data used to train AI models when real data is scarce or sensitive |
| Temperature (in AI) | A parameter that controls randomness in AI-generated text; higher values lead to more creative responses |
| Text Classification | Categorising text into predefined labels (e.g., spam detection, sentiment analysis) |
| Tokens | The basic units (words, subwords, or characters) that AI models process in NLP tasks |
| Transfer Learning | Using a pre-trained AI model on a new but related task to improve efficiency |
| Transformer Model | An AI model architecture that excels at processing sequential data like text (e.g., GPT, BERT) |
| Turing Test | A test proposed by Alan Turing to determine if an AI can exhibit human-like intelligence |
| Underfitting | When an AI model is too simple to learn meaningful patterns from training data |
| Unsupervised Learning | A type of ML where AI finds patterns in data without labelled examples |
| Zero-Shot Learning | AI’s ability to perform tasks without prior exposure to similar examples |
Learn more about AI at the St. Pölten UAS
- Official recommendations from the St. Pölten UAS on the use of generative AI for students (with a small section for teaching staff)
Using AI to study – what you should know - We help determine how AI is transforming our world. Learn more about AI at the St. Pölten UAS
- Download PDF: Guidelines for working with generative AI tools