Artificial intelligence, or AI, is a collective term for technology that has been developed to mimic human cognitive functions such as learning, recognizing patterns and solving problems. Generative AI is a specific subcategory within the broader field of AI.

These pages about AI use in your studies deal with generative AI. This concerns all students and probably has the greatest impact on general study practice and learning. 

Differences and similarities between traditional AI and generative AI

Traditional AI is primarily designed to analyze, interpret and classify existing data. Examples include recognizing patterns in large data sets, predicting behaviour, or recognizing diseases on X-rays.

Generative AI differs by actively creating entirely new content. Through instructions (prompts), the technology can generate new text, images, audio, video and computer code that resemble human-generated content. This technology powers tools such as Microsoft Copilot (all students have access to a paid educational license through AAU), ChatGPT, Mistral, Claude and Google Gemini.

Generative AI thus appears more creative, but it shares its foundation with traditional AI. Both technologies, with the help of machine learning, are trained to find patterns and connections based on gigantic amounts of data.

Neither traditional nor generative AI possesses any kind of consciousness, intentions or genuine human understanding or sensibility. For example, when generative AI writes a text, it doesn't understand the content; it simply calculates the statistical probability of which words typically follow one another.

Since both technologies are based on the data they were trained on (which is usually created by humans), they also risk reproducing and reinforcing cultural and social biases. And, the lack of factual understanding means that generative AI can invent false information (hallucinations) and make it appear credible. Although the technology is constantly improving, many of the pitfalls lie in the technology itself, so there will always be an inherent risk that you should be aware of and understand.

When opportunities and uncertainty interact

A lot of students express an understandable concern that using generative AI in their studies and learning may lead to a suspicion of cheating. Because in addition to all the benefits of generative AI, there is also the downside that it can be used to produce finished answers without you yourself going through the learning process intended by the assignment.

These concerns even lead some students to opt out of using generative AI altogether. But Aalborg University wants to educate the AI experts of the future and ensure that all students are AI-savvy in their own field.

Therefore, the "AI in your studies" pages aim to guide you on how to use generative AI actively and responsibly as part of your study practice.

Generative AI has great potential as your "study partner" or an extra member of your project group. This can be for developing ideas, explaining complex concepts or proving feedback on structure.

The concept of AI literacy includes the skills you need to navigate, understand, and use AI in a meaningful and responsible way. It's not just about learning how to enter the right instructions (prompts) in a chatbot. On the contrary, it is a combination of technical understanding, critical thinking, ethical awareness and your own core academic competence.

Disciplinary knowledge as your strongest filter

It is a misconception that generative AI can replace the need for knowledge. In fact, the opposite is true; solid disciplinary knowledge is more important than ever. It requires in-depth academic insight to understand when the machine's answer is valuable and when it is directly misleading or unimportant. 

AI literacy means that you use technology as a cognitive partner for feedback, but rely on yourself as the critical judge. You must avoid outsourcing your own thinking and inadvertently skipping the resistance necessary for learning, thus building professional judgment.

Good academic practice and transparency

In your study practice, AI literacy is very much about academic integrity. The three fundamental principles you must navigate by are honesty, transparency and accountability. If you use generative AI for tasks like developing ideas, structuring, or information retrieval, you need to be open about it. You should always declare and document your use of AI, for example in the methods section of your assignment, so that it is clear where and how the technology has played a role. Remember that generative AI is not a source with intrinsic truth value and that you are responsible for the validity of your final product.

Ethical judgment and data security

AI literacy also extends beyond the walls of the university and into your broader understanding and responsible use of digital technologies. It involves understanding the data security and ethical implications of the technology. You must learn that you never simply feed confidential data, copyrighted material or personal data into language models.

At the same time, AI literacy calls for continuous reflection on the larger effect of the technology, including its climate impact and energy consumption as well as the cultural biases embedded in its training data. The goal is for you to develop as a student and be able to make conscious, ethical and informed choices about when it is even appropriate and justifiable to use artificial intelligence.

On the page "Generative AI – introduction and overview" you can read more about a number of different aspects of using generative AI as a student.

You can also get a general introduction to generative AI and chatbots in AAU's micro course. You get a look under the hood to learn about how generative AI and LLMs (Large Language Models) work, as well as how generative AI can be used in academic practice.