User:Luke Lindo

Artificial Intelligence

Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.

Background

AI is a concept that has been around, formally, since the 1950s, when it was defined as a machine's ability to perform a task that would've previously required human intelligence. In 1950 Alan Turing introduced the Turing test as a way of testing a machines intelligence.

By the year 1955, the term AI was coined. Since then, AI has evolved from simple chatbots to automated systems which are capable of carrying out tasks related to human intelligence.

Types of AI

AI can be classified in two categories:

1. AI Based on Capabilities
2. AI Based on Functionality

AI based on Capabilities.

1. Narrow AI
2. General AI
3. Strong AI

AI based on Functionality.

1. Reactive AI- This is described as such because it reacts in the way it was programmed via input inputs which yields predictive results. An example of reactive AI includes Netflix recommendations through its algorithms.
2. Limited memory AI - This is characterized by the ability to absorb learning data and improve overtime based on experience. This is the AI that is widely used in deep learning tools as it uses historical data to make predictions. Limited memory AI is used in self-driving cars.
3. Theory of mind AI- AI of this nature is characterized by machines having decision making capabilities equal to humans. An important aspect of this AI is that machines would have the capability to understand and remember emotions and adjust behaviors based on these emotions
4. Self-Aware AI- Self-aware AI is characterized by human consciousness and intelligence. This AI is not yet developed.

An Overview of Artificial Intelligence in Education

In line with the adoption and use of new technologies in education, artificial intelligence has also been extensively leveraged in the education sector. The adoption of AI in various areas of the education sector or departments in educational institutions is starting to become widespread.

The Use of artificial intelligence in education has had a major impact, including improved efficiency, global learning and improved effectiveness and efficiency in education administration among others. As time progresses AI continues to develop along with new ways of its application in education.

Impact of AI on the teaching environment

AI's Determination of Learning Styles

One of the key advancements in AI is the ability to predict learning styles, which has significant implications for the field of education. By understanding how students learn best, educators can tailor their teaching methods to optimize learning outcomes. research has shown that individuals have unique learning styles, such as visual, auditory, or kinesthetic preferences. AI learning style prediction aims to identify these preferences by analyzing vast amounts of data, including student performance, engagement, and feedback.In special needs education.

AI learning style prediction has the potential to revolutionize assessment methods. Traditional assessments often rely on standardized tests, which may not accurately capture a student’s true understanding or potential. With AI, assessments can be tailored to individual learning styles, providing a more comprehensive and accurate evaluation of a student’s knowledge and skills. This shift towards personalized assessments ensures that students are not unfairly disadvantaged due to a mismatch between their learning style and the assessment format.

AI's Determination of Learner deficiencies

One area where AI is making significant strides is in the identification and addressing of learning disabilities. By utilizing AI algorithms and machine learning, educators can now better understand the unique needs of students with learning disabilities and provide targeted support to help them succeed.

Learning disabilities, such as dyslexia, attention deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), affect millions of students worldwide. These disabilities can create significant barriers to academic success, as students with learning disabilities often struggle with traditional teaching methods and require specialized instruction. Early identification and intervention are crucial for these students, as research has shown that providing appropriate support can lead to improved academic outcomes.

AI-based approaches have shown potential, for example, in the early detection of dyslexia. A well-published example is the Swedish company “Lexplore” that has developed a system that quickly scans for students at risk and detects dyslexia by tracking reader eye movements. The system uses data-based pattern recognition, and the company is now expanding to the US and UK, offering school andschool-district wide scanning. AI-based systems have also been successfully developed for the diagnosis of ASD and ADHD. Child-robot interaction seems to enable new forms of diagnostics. And special needs educational applications.

Impact of AI on the learning environment

Personalized learning

Personalized learning means that each student's learning experience is tailored to fit their needs. Personalized learning lets each person's wants and learning goals be met by changing things like the speed at which they learn, the materials they use, the order in which they learn them, the technologies they use, the quality of the materials, the way they are taught, and the materials they use to learn. However large-scale personalization may not be possible without the use of artificial intelligence technology. By using AI for personalized learning, learners can receive training at their own pace and when it's most convenient for them. AI can be used to carry out the following strategies to enhance personalised learning:

1. Adaptive learning - AI-powered adaptive learning platforms analyze student data, such as their performance, strengths, weaknesses, and learning pace. Based on this information, the system can provide personalized learning pathways for each student, offering appropriate content, resources, and activities that align with their specific needs.
2. Intelligent tutoring systems - AI-driven tutoring systems provide individualized guidance and support to students.
3. Personalised Recommendations - AI algorithms can analyze vast amounts of data, including a student's past performance, interests, and goals, to generate personalized recommendations for educational resources, books, articles, videos, and other learning materials. This helps students discover relevant content that matches their specific needs and preferences.
4. Multimodal learning - AI technologies facilitate multimodal learning experiences by incorporating various formats, such as text, audio, video, and interactive elements.
5. Personalized Assessment and Feedback - AI can automate the assessment process and provide timely, personalized feedback to students. Intelligent grading systems can evaluate assignments, quizzes, and exams, enabling faster feedback delivery.

The Power of Organized Information

AI provides the advantage of organized information, allowing students to access knowledge from various sources effortlessly. With AI-powered systems, educational platforms can efficiently categorize and present information, enabling students to find relevant resources quickly and enhancing their learning experiences. AI platforms such as OpenAI and Quillbot are some of the plethora of platforms that aid in providing learners with organized information to facilitate research.

Challenges and Concerns of AI in Education

1. Developing a comprehensive view of public policy on AI for sustainable development: The complexity of the technological conditions needed to advance in this field require the alignment of multiple agencies and
2. institutions. Public policies have to work in partnership at international and national levels to create an ecosystem of AI that serves sustainable development. Such linkages may take longer in some parts of the world due to lack of support from governments.
3. Ensuring inclusion and equity for AI in education: The least developed countries are at risk of suffering new technological, economic and social divides with the development of AI. Some main obstacles such as basic technological infrastructure must be faced to establish the basic conditions for implementing new strategies that take advantage of AI to improve learning.
4. Preparing teachers for an AI-powered education: Teachers must learn new digital skills to use AI in a pedagogical and meaningful way and AI developers must learn how teachers work and create solutions that are sustainable in real-life environments.
5. Developing quality and inclusive data systems: If the world is headed towards the datafication of education, the quality of data should be the main chief concern. It´s essential to develop state capabilities to improve data collection and systematization. AI developments should be an opportunity to increase the importance of data in educational system management.
6. Dealing with ethics and transparency in data collection, use and dissemination: AI opens many ethical concerns regarding access to education system, recommendations to individual students, personal data concentration, liability, impact on work, data privacy and ownership of data feeding algorithms. AI regulation will require public discussion on ethics, accountability, transparency and security.