Artificial Intelligence and Education

Artificial Intelligence Advisory Board

All stakeholders within the Hisar School community share the responsibility of using AI technologies ethically, consciously, and responsibly, enhancing educational and administrative processes, supporting professional learning, and strengthening community engagement. By upholding data privacy, security policies, and institutional standards, the school community actively contributes to the development of a mindful and responsible AI culture.

This section aims to establish a common understanding within the school community by explaining fundamental concepts related to artificial intelligence.

Artificial Intelligence (AI)

Artificial intelligence refers to machine-based systems capable of making predictions, generating recommendations, and making decisions based on specific objectives. These systems learn through data processing and algorithms, adapting their behavior either autonomously or in collaboration with humans (UNICEF, 2021; UNESCO, 2024). AI encompasses interdisciplinary fields such as Natural Language Processing (NLP), Machine Learning (ML), Computer Vision, and Robotics:

• Natural Language Processing (NLP): Enables computers to understand, process, and generate meaningful responses in human language.
• Machine Learning (ML): Refers to a system’s ability to learn from data and make predictions.
• Computer Vision: Involves perceiving, analyzing, and recognizing images.
• Robotics: Focuses on the development of autonomous systems that interact with the physical world.

These technologies have broad applications in education, healthcare, industry, and data analytics, supporting learning processes, improving decision-making, and optimizing workflows (OECD, 2019; UNESCO, 2024).

Large Language Models (LLM)

LLMs are advanced AI systems trained on large datasets to generate human-like text (e.g., ChatGPT, Gemini, Claude, Mistral 7B). These models are widely used in text analysis, natural language processing, and automated response systems (European Commission, AI in Education White Paper, 2024).

Local Large Language Models (Local LLMs)

Local LLMs are open-source large language models that can operate on local devices, reducing reliance on cloud-based AI services. Examples include DeepSeek-R1, Mistral 7B, Falcon, BLOOM, and OpenHermes-2.5. These models enhance data privacy and provide independent usage capabilities, making them preferable for institutions and organizations (OECD, AI and the Future of Education, 2023).

Generative AI

Generative AI refers to technologies that create new and original content using large datasets and advanced AI models. These systems can generate text, images, music, sound, and video, offering customized outputs (UNESCO, 2023; European Commission, 2021).

Common examples of generative AI tools include ChatGPT, DALL-E, Copilot, Runway ML, Kaiber AI, Gemini, and MidJourney. These tools:

✔ Summarize information and answer queries
✔ Generate unique content in text, image, and other formats
✔ Support personalized learning by adapting educational materials
✔ Facilitate innovative ideas and creative projects

According to UNESCO’s Guidance for Generative AI in Education and Research (2023), it is critical to ensure the ethical, responsible, and reliable use of these technologies. Users should be aware of data privacy, algorithmic biases, and content accuracy (UNESCO, 2023; European Commission, 2021).

Algorithmic Bias in AI

Algorithmic bias occurs when AI systems produce unfair outcomes for specific individuals or groups due to biases in training datasets. This issue can result in discrimination in key areas such as education, recruitment, healthcare, and law.

To reduce bias in AI decision-making, it is essential to:

• Improve diversity in datasets
• Ensure algorithmic transparency

Research has shown that imbalances in training data can lead AI systems to develop systematic biases against certain groups (Papakyriakopoulos & Mboya, 2021).

AI Literacy

AI literacy refers to an individual’s ability to understand, interpret, and consciously use AI systems and technologies. While different models and approaches exist in literature, AI literacy generally focuses on:

✔ Understanding the capabilities and limitations of AI
✔ Developing a critical perspective on AI applications
✔ Making informed decisions aligned with ethical principles (Casal-Otero et al., 2023)

According to UNESCO (2024), AI literacy extends beyond technical skills to include critical thinking, ethical considerations, and social justice awareness. Additionally, academic studies emphasize the importance of evaluating algorithmic transparency, data privacy, and societal impact when understanding AI technologies (Zhang & Dafoe, 2021).

Responsible and Ethical Use of AI

Developing comprehensive policies and strategies is essential to ensure the conscious, secure, and ethical use of AI technologies.

To support this, UNESCO’s AI Competency Framework for Teachers and AI Competency Framework for Students aim to promote the conscious, ethical, and effective use of AI technologies (UNESCO, 2024).

• The AI Competency Framework for Teachers guides educators on how to integrate AI into teaching responsibly and effectively.
• The AI Competency Framework for Students helps develop skills in critical thinking, ethical evaluation, and decision-making based on human rights.

At Hisar School, AI literacy education is structured within the K-12 curriculum, incorporating international frameworks to ensure that students develop a critical, ethical, and informed approach to AI.

Integration into Educational Processes and Curriculum

As part of Turkey’s National AI Strategy (2021-2025), increasing AI applications in education and training a qualified workforce in this field are key priorities. Hisar School supports the integration of AI technologies into teaching while ensuring:

✔ Teacher professional development in digital competencies
✔ A student-centered approach
✔ An inclusive and equitable learning environment

The ethical, responsible, and conscious use of AI tools in education is essential. To support this, teachers are provided with international guidelines and academic resources.

AI Competency Framework for Teachers and International Approaches

To help educators effectively use AI tools in education, UNESCO’s AI Competency Framework for Teachers serves as a reference guide (UNESCO, 2024).

Hisar School adopts the following principles for AI integration in education:

1. Understanding AI Fundamentals

   • Teachers gain knowledge about how AI systems work and their role in education.
   • They develop awareness of data processing and algorithmic operations.
   • They determine effective ways to use AI tools in education.

2. AI-Supported Instructional Design

   • Teachers personalize learning using AI technologies.
   • They integrate AI-driven assessment and feedback systems into teaching.
   • They enhance lesson planning efficiency through AI tools.

3. Critical and Responsible AI Use

   • Teachers analyze the ethical aspects of AI in education.
   • They raise awareness about algorithmic biases and AI’s societal impact.
   • They promote ethical AI use among students.

4. Continuous Learning and Professional Development

   • Teachers are provided with ongoing training in AI education.
   • Workshops and seminars on AI-powered teaching strategies are organized.
   • Collaboration and knowledge sharing among educators are encouraged.

5. Assessment and Adaptive Learning

   • AI-based assessment tools are customized for individual student needs.
   • The impact of AI tools on education is regularly analyzed.
   • Student data privacy and security are safeguarded.

AI and Critical Thinking

According to The International Journal of Artificial Intelligence in Education (2023), AI literacy training for teachers significantly enhances students’ critical thinking and data literacy skills (IJAIED, 2023).

AI accelerates access to information, helping students evaluate diverse sources and develop critical thinking skills. However, AI should be complemented with academic research methods to prevent misinformation and bias.

Thus, both teachers and students must adhere to the principles of accuracy, reliability, and academic integrity when incorporating AI into education.