The impact of micro- and macro-level educational environments on the use of artificial intelligence in education

The manner and purpose of applying AI in higher education largely depend on the nature of the subject, its learning objectives, and outcomes.

Each subject has unique learning requirements and objectives that influence how AI can be applied:

• Subjects with clearly structured content and rules (e.g., mathematics, physics, programming) are well-suited for AI tools that automate assessment and adapt content. In these subjects, AI can accelerate feedback and enable personalised learning.
• In contrast, the humanities and social sciences require a different approach. Here, AI supports creativity, analyses large data sets, and fosters critical thinking. In these cases, it is essential to balance AI use carefully to maintain originality and ethical standards.
• The application of AI can also vary within the same subject, depending on the level of education (e.g., primary school versus higher education).

Examples:

• STEM subjects (e.g., math, physics, programming) in a Computer Networks course: The instructor uses an AI system to automatically grade students’ lab exercises and suggest additional learning resources if the system identifies recurring errors. This accelerates feedback and allows students to progress individually. Additionally, an AI chatbot helps students resolve routine queries (e.g., “How do I configure an IP address?”), freeing the instructor’s time for more in-depth work with students.
• Humanities and social sciences (e.g., philosophy, history, communication studies) in a Media Theory course: The instructor encourages the use of a generative AI tool (e.g., ChatGPT) to simulate analyses from different theoretical perspectives. Students learn to critically evaluate the tool’s responses, compare them to the literature, and recognize potential biases. Here, AI is used to encourage critical thinking, not to provide “correct” answers. The instructor sets clear rules for the use and evaluation of work involving AI.
• Within the same subject – depending on the level of education: In the course Introduction to Economics, taught at the undergraduate level, AI is used for quizzes with automatic feedback and visualisation of economic models. At the graduate level in the same field, students use AI tools to analyse trends in financial markets and build predictive models, with mentoring support from the instructor. This demonstrates how AI application can vary not only by subject but also by level of study and cognitive complexity of the content.

For reflection: 

When planning to incorporate AI into your instructional environment (e.g., course or subject), consider the following:

• What are the educational objectives?
• Is the content structured or open-ended?
• How much freedom do students have in interpreting, reasoning, and being creative?
• Is there a risk of violating ethical standards (e.g., plagiarism or loss of authenticity of work)?

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