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

1. Introduction

The use of artificial intelligence in higher education is now considered a key driver of the transformation of contemporary teaching and learning. Its presence is reshaping approaches to instruction, the design of learning materials, assessment practices, and the dynamics of the relationship between teachers and students.

However, technology alone does not guarantee success. The effectiveness and meaningfulness of artificial intelligence in education depend on the context in which it is implemented – on the people, institutions, and environments that enable or restrict its use.

To understand the conditions for the successful integration of artificial intelligence in higher education, it is necessary to consider the influence of two interconnected frameworks: the micro-level educational context, which encompasses individual courses, teaching approaches, and student characteristics, and the macro-level educational context, which includes institutional, legal, social, and technological factors.

The following sections examine how these factors interact and shape the real opportunities, challenges, and responsibilities associated with the use of artificial intelligence in higher education.

Particularly important are the perceptions and attitudes of teachers and students, as they largely determine the speed, manner, and extent to which new technologies are adopted within the educational process.

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2. Micro-level educational context

In the micro context of education, the application of AI is not universal and must be adapted to the specific educational environment. AI tools and methods should be selected based on the following factors:

• The specific characteristics of the academic discipline (type of subject)—for example, foreign language learning, mathematics, and history each require different approaches to personalisation, assessment, and support of learning through AI.

• The individual characteristics and needs of participants – including prior knowledge, learning style, pace of work, and specific needs such as learning difficulties or giftedness.

This statement emphasises the importance of pedagogically thoughtful integration of AI. Technology alone does not ensure quality learning unless it is aligned with the educational environment and individual needs.

Therefore, the need for personalisation, the type of activity, and the way AI is applied will vary, for example, between courses in law, medicine, computer science, art, and other fields.

Example:

In the course "Introduction to Statistics," the instructor uses an AI tool that automatically generates tasks for students based on their level of understanding and previous mistakes. Students with more prior knowledge receive more advanced tasks, while those struggling with the material are offered additional support through shorter video explanations generated by the AI. The instructor uses student behaviour analytics to differentiate the approach for the next meeting – offering some students additional literature and others group work to practice basic concepts.

This approach demonstrates how AI can support personalised learning and serve as a tool for teachers in data-driven lesson planning. The key responsibility of the teacher remains the ethical use of AI tools and the critical interpretation of the recommendations received.

Reflection questions:

• Consider your educational environment: How could you use AI to provide students with a more individualised approach to learning?
• Are there segments of your course that you could automate or further customise with the help of AI?
• How might the goals of your course align with the possibilities of artificial intelligence?

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2.1. Subject characteristics

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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2.2. Students characteristics

The student profile and their relationship to AI technology strongly influence both the methods and success of AI implementation in education. Differences in digital literacy, prior experience, and attitudes toward technology shape students’ readiness for learning with AI support.

Students with more developed digital skills often demonstrate greater confidence and willingness to experiment, while those with less experience require additional support, a clear framework, and tailored instructions. Motivation and perception of technology are equally important – if students view AI as a tool that facilitates understanding and learning, their engagement and interest in the content increase.

Teachers can address these differences by implementing personalised learning paths and adaptive systems. For example, in the online course “Artificial Intelligence in Education,” learners first assess their digital competencies and select either a basic or advanced module based on the results. The AI system then provides content in multiple formats – text, video, and interactive – allowing students to learn according to their preferences and pace.

This approach not only increases learning efficiency but also fosters trust in AI as a support for teachers, strengthening the partnership between students and technology.

Example:

• An instructor leads the online course “Artificial Intelligence in Education” for 30 teachers from various fields (STEM, social sciences, and humanities) to introduce them to the possibilities of applying AI in teaching.

• Some students have a high level of digital literacy and already use AI tools such as ChatGPT, while others have no previous experience. The instructor uses a self-assessment tool for digital competencies and offers two parallel modules – basic and advanced – so everyone can learn according to their knowledge.

• Some students are highly motivated and open to technology, while others are skeptical and concerned about automation. The instructor includes a forum for exchanging opinions on the advantages and challenges of AI, encourages open discussion, and provides examples where AI supports, rather than replaces, the instructor’s role.

• Students prefer different learning styles – some learn better by watching videos, others by reading texts, and others through practical tasks. The instructor uses an adaptive learning tool that offers content in multiple formats (text, video, interactive quizzes), tailored to individual preferences.

• Some students have special educational needs, such as reading difficulties or limited English proficiency. The instructor uses AI tools to translate and summarise texts, convert content to audio format, and use visuals to make content accessible to everyone.

Questions to consider:

• How do differences in students’ digital literacy and attitudes affect the way you use technology in your classroom?

• What approaches could you take to increase students’ sense of competence and confidence in using AI?

3. Macro-level educational context

The macro-level educational context encompasses factors that extend beyond an individual classroom, course, or learner, relating to the institutional, legal, social, and technological conditions in which artificial intelligence (AI) is developed and implemented. These factors shape the environment in which educational institutions determine how, when, and why to use AI tools in their work.

The macro-level educational context includes factors beyond the immediate teaching environment, course, or learner, such as:

• Institutional conditions, including organisational strategies for introducing AI into education, institutional policies, and available resources.
• Legal conditions, such as legislation, data protection standards, and ethical frameworks that ensure responsible and transparent use of AI.
• Social conditions, including the acceptance of AI tools and systems and prevailing social norms.
• Technological conditions, referring to the availability, security, and reliability of AI tools and systems.

Together, these factors shape the environment in which educational institutions make decisions about how, when, and for what purposes to use artificial intelligence tools in their educational practice.

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3.1. Organisational factors

At the level of an individual institution, the successful application of AI in education depends on several organisational elements:

• Technical infrastructure – A stable network, equipment, and tools are essential for the sustainable use of AI.
• Digital strategies – Clearly defined plans and goals support the systematic integration of AI.
• Teacher education – Ongoing support and professional development are crucial for the effective use of AI.
• Culture of innovation – Openness to new technologies encourages the active use of AI in educational practice.

Examples:

Technical infrastructure – For example, the university provides a fast and reliable Wi-Fi network, computer laboratories equipped with the latest software for AI applications, and access to cloud platforms that enable teachers, students, and other educational stakeholders to use AI tools and systems.

Digital strategies – The institution adopts a strategic digital transformation plan in which the goal of including AI tools and systems in the teaching process is clearly defined, with a schedule for training, investment in technology, and monitoring the success of implementation.

Teacher education – Regular courses and workshops for instructors on the use of AI in education, including practical examples, training on ethical aspects, and ways to adapt teaching with AI tools.

Culture of innovation – Encouraging instructors to experiment with new AI tools through incentive programs, recognition for innovative teaching approaches, and establishing internal networks for sharing experiences and ideas on AI applications.

A culture of innovation thus becomes the driving force of institutional digital transformation – it does not come from technology itself, but from people who are willing to understand, apply, and continually question its value in education.

Questions for reflection:

• To what extent is a culture of innovation in teaching encouraged at your institution?
• Are there formal or informal mechanisms that encourage the exchange of experiences and shared learning among teachers?

3.2. Legal, social and technological conditions

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?

4. Conclusion

Artificial intelligence is bringing profound and lasting changes to higher education, but its full potential can only be realised within a carefully designed, supportive, and ethically grounded environment.

Successful integration depends on the interplay of multiple factors – from course characteristics, teaching approaches, and student needs to institutional infrastructure, legal regulations, social perceptions, and the level of digital maturity within the higher education community.

Technology alone does not guarantee better learning outcomes, but it can significantly enhance pedagogical processes when used thoughtfully and purposefully. Teachers play a key role, acting as mediators between technology and students, giving meaning and direction to its use. Their ability to critically evaluate tools, make ethical decisions, and adapt teaching methods is crucial for achieving educational goals in the digital age.

Institutions that systematically invest in digital infrastructure, teacher training, and a culture of innovation create the conditions for the sustainable use of artificial intelligence. Equally important, they help build trust in technology and ensure that artificial intelligence serves people and education – not the other way around.

Investing in teachers’ digital and pedagogical competencies is becoming a long-term strategic necessity, because only an education system that fosters critical thinking, digital literacy, and ethical responsibility can fully harness the potential of artificial intelligence.

Artificial intelligence is not a replacement for humans in education, but rather a partner and ally in creating a dynamic, inclusive, and creative learning environment. When used thoughtfully and responsibly, it can enrich the teaching and learning process, increase access to knowledge, and stimulate new forms of collaboration and learning that connect technology and humanity.

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5. Literature

• Babić, S. (2016). Factors of teachers' acceptance of e-learning and competences for its implementation in higher education institutions (Doctoral dissertation). Faculty of Organization and Informatics, University of Zagreb. 
  Description: This doctoral thesis analyses the factors – including the micro- and macro-level educational environment – that influence teachers’ acceptance of e-learning. It is relevant for understanding teachers’ attitudes and readiness to adopt digital technologies, including AI.
• Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278.
  Description: This review article presents how AI is used in education, including adaptive instruction, automated assessment and the role of personalised learning.
• European Commission (2021). Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union.
  Description: The EU’s legal framework for the use of AI technologies. It is particularly important for understanding the regulation of high-risk systems in education, such as automated grading.
• Directorate‑General for Education, Youth, Sport and Culture (European Commission) (2022, October 25). Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators. Publications Office of the European Union. Description: Guidelines that support educators in using AI and data in teaching and learning in an ethical, transparent and learner-centered way.
• Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning (Updated edition). Center for Curriculum Redesign. Description: A comprehensive overview of how AI is being integrated into education, its promises and limitations, and its implications for teaching and learning, with particular attention to the changing roles of teachers and students.
• Schiff, D. (2022). Education for AI, Not AI for Education: The Role of Education and Ethics in National AI Policy Strategies. International Journal of Artificial Intelligence in Education, 32, 527-563. 

  Description: The author emphasises the importance of ethical and informed use of AI in education policy. The paper focuses on how education should prepare society for AI, rather than merely using AI as a tool for learning.

• U.S. Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations.
  Description: A national policy document providing guidelines for the responsible use of AI in education, with a focus on the role of teachers, ethics, and human oversight.
• Perse, R. J. P. (2025). Using artificial intelligence in high school education: A review [Preprint]. Preprints.
  Description: A systematic review of the benefits and challenges of using AI in secondary education. It is relevant for understanding the use of AI in the micro-level teaching context, given that learners and students come from such educational environments.