Artificial Intelligence and Education

As educators, by making ethical principles the focal point of studies on the role of artificial intelligence (AI) in education, we can pave the way for developing effective solutions and recommendations. To achieve this, Hisar School has initiated the process of creating a Hisar Schools AI Education Policy, which will serve as a valuable resource and example for the education sector.

The Computer Science Teachers’ Association (CSTA) and the Association for the Advancement of Artificial Intelligence (AAAI) propose defining AI in education through five key themes to clarify its role:

Perception – Computers can perceive the world through sensors.
Representation and Reasoning – Computers create models using data structures and apply reasoning algorithms to generate new knowledge from existing information.
Learning – Computers can learn from data. Machine learning is a type of statistical inference that identifies patterns in data.
Natural Interaction – AI developers aim to create systems that interact naturally with humans.
Social Impact – AI can have both positive and negative effects on society.

What is Artificial Intelligence (AI)?

AI refers to machine-based systems capable of making predictions, recommendations, or decisions that impact real or virtual environments, guided by a set of objectives defined by humans. AI systems interact with us, often autonomously, adapting their behavior through learning. (UNICEF 2021)

Key Developments in AI History

Alan Turing (1950): Proposed that if a computer could convincingly imitate human behavior, it could be considered “thinking.”
Dartmouth Workshop (1956): US scientists introduced the term “AI” and set goals for developing intelligent machines.
IBM Deep Blue (1997): Defeated chess champion Garry Kasparov, proving that computers could perform complex, intelligent tasks.
Google DeepMind AlphaGo (2015): The first AI to beat a professional Go player without a handicap.

AI Learning and Neural Networks

Machine Learning: AI develops skills by learning from examples rather than being explicitly programmed by humans.
Neural Networks: Inspired by the structure of the human brain, deep learning uses layered artificial neural networks to achieve human-like performance in various fields.

Generative AI

Generative AI creates new content—such as text, images, music, and video—based on the data it has been trained on. These tools are supported by foundational AI models capable of performing tasks like summarization, classification, and question-answering.

AI Education Policies

Integrating AI into education goes beyond delegating tasks to robots; it involves understanding the broader social and pedagogical implications. Countries continue to publish AI strategies and roadmaps:

Australia: Australian Framework
New Zealand: New Zealand Framework
United States: US AI Future Learning
Singapore: National AI Strategies

Hisar School’s AI Policy

Hisar School’s Information Strategies Center works to raise awareness, develop strategies, and position AI education policies with input from administrators, academics, and industry experts. Key areas of focus include:

Ethics, Transparency, and Accountability

Establish ethical guidelines to ensure AI positively impacts learning while addressing potential risks.

Data Privacy and Security

AI systems collect vast amounts of student data, necessitating rigorous data protection measures.

Accuracy and Reliability

Recognize that generative AI tools can produce misleading or biased content and address these challenges.

Curriculum Integration

Educators need to understand AI’s core mechanisms and limitations to safely integrate AI tools into teaching and learning.

Academic Integrity

Develop acceptable use policies to promote ethical AI use, ensuring students produce original work.

Professional Development

Ongoing training for teachers and staff to keep up with AI advancements.

Insights and Recommendations

The US Department of Education recommends the following for AI integration in education:

Keep humans (teachers/students) at the center of AI use.
Align AI approaches with a shared educational vision.
Use designs grounded in modern learning principles.
Prioritize trust-building with AI tools.
Educate and involve teachers throughout the process.
Focus R&D efforts on AI in educational contexts.

A McKinsey report highlights AI’s potential to reduce teachers’ administrative workload, allowing them to spend more time interacting directly with students. Automating repetitive tasks can free educators to focus on enhancing the learning experience.

For more details, click here to access Hisar School’s AI Education Policy.

Artificial Intelligence and Education