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

The use of artificial intelligence in higher education occurs within a framework of legal, social, and technological factors that shape how and how quickly it is integrated into teaching and learning. These factors together determine the level of safety, ethics, and acceptance of the technology in the higher education environment:

• EU AI Act – A European Union regulation governing the use of artificial intelligence, especially systems considered high-risk in education, such as automated grading and personalised learning recommendations. It introduces obligations related to transparency, human oversight, and data protection, ensuring the responsible use of AI in educational contexts.
  Example: Universities using AI tools for automated assessment must clearly inform students about how the system operates and provide the opportunity for human review and correction of results. Although such measures may slow implementation, they increase trust and help ensure fairness in grading.
• GDPR – The General Data Protection Regulation requires institutions to ensure that all AI tools comply with principles of privacy and security. This includes data encryption, minimisation of collected information, and clearly defined access policies.
  Example: When using learning analytics tools, the institution must ensure that students’ personal data is used exclusively for educational purposes, with their prior consent and under strict data protection measures.
• Ethics and responsibility – The use of AI in education must be fair, transparent, and non-intrusive. Systems must respect human dignity, avoid bias, and provide understandable decisions.
  Example: Before implementing a system for recommending learning resources, it is necessary to evaluate the algorithm to ensure it does not favour particular groups of students based on socio-demographic characteristics.
• Technological development – The rapid advancement of AI technologies requires continuous monitoring of new solutions and evaluation of their pedagogical value. Ongoing professional development for teachers and technical support are key to sustainable implementation.
  Example: Introducing a new system for generating feedback requires that teachers be trained in its capabilities and limitations so they can appropriately interpret and apply the results.
• Social perception – The attitudes of teachers and students can significantly influence the pace and mode of AI implementation. Skepticism, fear of replacing human roles, or lack of trust in technology can slow adoption, while openness and trust foster innovation.
  Example: If an institution communicates transparently about the purpose and functioning of AI tools and involves teachers and students in decision-making processes, trust and willingness to use the technology in education are likely to increase.

Examples:

EU AI Act – The law introduces a requirement that artificial intelligence tools used in education, such as automated assessment systems, must be transparent and under human supervision. Institutions must ensure that students and instructors are clearly informed about how the AI system works and provide the option for human intervention. While this may slow down or complicate implementation, it can also increase trust in the system.

GDPR –  Protecting the personal data of students and instructors requires all AI tools to comply with privacy regulations, which may include data encryption, data minimisation, and clearly defined information access policies. This may require additional technical and organisational adjustments during implementation.

Ethics and accountability – Institutions must ensure that the use of AI does not violate students' rights, that systems do not discriminate, and that decisions are understandable and fair. This often means additional evaluation and adjustment of AI tools to meet these ethical standards.

Technological development – The rapid advancement of AI technologies requires ongoing education for instructors and constant monitoring of new tools and their impact on the learning process. This can increase costs and resource requirements, but also ensures that teaching remains modern and effective.

Social perception – If students and instructors are skeptical of AI, they may use it less or even reject new tools, slowing integration. Conversely, a positive attitude and user support facilitate adoption and encourage innovation in teaching.

Questions for reflection:

• How does your institution address ethical and legal issues in the use of artificial intelligence?

• In what ways can trust and responsible use of artificial intelligence among teachers and students be systematically strengthened?