AI in Education: Current Trends and Challenges

State of the Art and Practice in AI in Education

• Recent AI developments have created high expectations for AI's impact on education and learning (AIED).
• These expectations often misunderstand current technical capabilities, lack knowledge of advanced AI in education, and have overly narrow views of education's role in society.
• The article provides an overview of existing AI systems in education, including their pedagogical and educational foundations.
• It develops a typology of AIED systems and illustrates various ways AI is used in education and learning.
• It examines how these applications are grounded in different interpretations of AI and education.
• The article discusses potential obstacles in the implementation of AIED.

Introduction

• AI is frequently compared to "new oil" and a significant technological advancement, leading to massive global investments.
• Notable policy statements regarding AI development and regulation have emerged.

What is AI?

• There's no single universally accepted definition of Artificial Intelligence (AI).
• AI is often described as research focused on creating technologies that can perform tasks requiring human intelligence.
• Different definitions exist for various purposes, including regulations for products entering the common market.
• The UN-backed UNICEF definition of AI is machine-based systems making predictions or decisions impacting virtual or real-world environments.

Data-driven AI

• Data-driven AI is based on using large datasets to create prediction models that improve with experience/training data.
• It frequently involves complex computer systems with numerous parameters adjusted iteratively until accuracy is sufficient.
• Deep learning, a type of data-driven AI, uses multiple layers of computational neurons (nodes) mapped to create representations/abstractions.
• Training deep learning models often requires significant computational resources.

Knowledge-based AI

• Knowledge-based AI relies on representing human knowledge in a format processable by computer programs.
• Expert systems are a type of knowledge-based AI that mimics expert decision making.
• Knowledge-based AI typically utilizes if-then rules to guide decision-making and problem-solving.
• It's currently less prevalent in mainstream AIED applications compared to data-driven AI.

A Taxonomy of AIED Systems

• AIED systems are categorized into student-focused, teacher-focused, and institution-focused categories.
• These categories overlap, and different applications can fall under multiple categories.
• Several specific AIED systems are described, including Intelligent Tutoring Systems (ITS), AI-assisted apps, simulations, and tools for learners with disabilities.

Roadblocks on the AI Highway

• The article addresses ethical concerns in AIED, including issues of human autonomy, dignity, and data privacy.
• AIED colonization, where powerful corporations from developed countries exert influence in less-developed regions, is a key concern.
• Commercialization of education through AIED development and implementation.
• The complex relationship between technology development and human learning and cognitive development presents obstacles.

Personalization

• Personalization in education is a frequently discussed topic, but its detailed application in AIED is less explored.
• Current AIED tools often use learner averages to create personalized learning paths, potentially ignoring individual needs and other social interaction aspects of learning.

Efficacy and Impact

• Evaluating the efficacy impact of AIED tools through independent and large-scale studies is crucial.
• Numerous studies from organizations like What Works Clearinghouse have yielded differing conclusions.

AIED Colonialism

• AIED products are increasingly being developed and exported from wealthier, more developed countries to developing nations, creating power imbalances.
• The languages (e.g., English) and contexts used to train and test AIED tools can often differ from the educational goals and practices in developing nations.

Commercialization

• AIED companies, often large corporations, aim to maximize profits which can potentially push education into a purely commercial market.

This quiz explores the recent developments and practices of artificial intelligence in the field of education. It examines the impact of AIED systems, their applications, and potential obstacles in implementation. Gain insights into the current capabilities of AI and their implications for learning and educational frameworks.