Implementing AI in Education Systems
Expert-defined terms from the Professional Certificate in Data Quality Assurance using AI in Education course at HealthCareStudies (An LSPM brand). Free to read, free to share, paired with a globally recognised certification pathway.
Artificial Intelligence (AI) #
The simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction.
Machine Learning (ML) #
A subset of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It focuses on the development of computer programs that can access data and use it to learn for themselves.
Deep Learning #
A subset of ML that is based on artificial neural networks with representation learning. It can process a wide range of data resources, requires less data preprocessing by humans, and is highly scalable and flexible for various data formats.
Neural Networks #
A type of machine learning algorithm modeled after the human brain. It is designed to replicate the way that humans learn and make decisions. Neural networks are a set of algorithms, modeled loosely after the human brain, designed to recognize patterns.
Data Quality Assurance #
A process used to ensure that data is accurate, complete, and useful. It involves monitoring, cleaning, and protecting data to ensure it is of high quality and fit for its intended uses.
Data Quality #
The degree to which a set of data is accurate, complete, and useful for its intended purpose. Data quality is essential for making informed decisions, ensuring operational efficiency, and maintaining regulatory compliance.
Data Governance #
The overall management of the availability, usability, integrity, and security of data. It includes the development and execution of policies, procedures, and practices to manage data quality, ensure data security, and promote data use.
Data Management #
The practice of collecting, keeping, and using data securely, efficiently, and cost-effectively. It includes the development and execution of policies, practices, and procedures to manage data quality, ensure data security, and promote data use.
Intelligent Tutoring Systems (ITS) #
Computer-based training systems that provide personalized instruction to learners. ITS uses AI to model the learner's knowledge and skills, diagnose learning gaps, and provide tailored instruction to address those gaps.
Adaptive Learning #
A type of learning that uses AI to provide personalized instruction to learners based on their individual needs and abilities. Adaptive learning systems adjust the content, pace, and sequence of instruction to meet the learner's needs.
Personalized Learning #
A type of learning that tailors instruction to meet the individual needs, strengths, and preferences of each learner. Personalized learning systems use AI to provide customized content, pacing, and sequencing of instruction.
Educational Data Mining #
The process of discovering patterns and trends in educational data using machine learning, statistics, and data visualization techniques. Educational data mining is used to improve learning outcomes, assess student performance, and inform instructional decisions.
Learning Analytics #
The measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. Learning analytics is used to improve learning outcomes, assess student performance, and inform instructional decisions.
Natural Language Processing (NLP) #
A field of AI that focuses on the interaction between computers and human language. NLP enables computers to understand, interpret, and generate human language in a valuable way.
Chatbots #
A computer program designed to simulate conversation with human users, especially over the Internet. Chatbots are often used in customer service, education, and entertainment.
Speech Recognition #
The ability of a machine or program to identify and transcribe spoken language. Speech recognition is used in applications such as voice assistants, dictation software, and automated customer service systems.
Computer Vision #
A field of AI that focuses on enabling computers to interpret and understand visual information from the world. Computer vision is used in applications such as image and video recognition, autonomous vehicles, and medical imaging.
Recommender Systems #
A type of information filtering system that seeks to predict the "rating" or "preference" that a user would give to an item. Recommender systems are used in applications such as online shopping, music and video streaming, and social media.
Affective Computing #
The study and development of systems and devices that can recognize, interpret, process, and simulate human affects (emotions). Affective computing is used in applications such as virtual reality, gaming, and mental health.
Explainable AI (XAI) #
A subfield of AI focused on creating AI models that are transparent, interpretable, and explainable to human users. XAI is used to build trust in AI systems, ensure fairness and accountability, and comply with regulations.
Bias in AI #
Systematic errors or distortions in AI systems that result in unfair or discriminatory outcomes. Bias in AI can arise from biased data, biased algorithms, and biased human decision-making.
Fairness in AI #
The principle that AI systems should be designed and deployed in a way that treats all individuals and groups fairly and without discrimination. Fairness in AI requires addressing bias, ensuring transparency, and promoting accountability.
Accountability in AI #
The principle that AI systems should be designed and deployed in a way that ensures responsibility and transparency for their actions and decisions. Accountability in AI requires clear lines of responsibility, transparent decision-making, and effective oversight and regulation.
Privacy in AI #
The protection of personal data and information in AI systems. Privacy in AI requires addressing issues such as data collection, storage, sharing, and use, as well as ensuring transparency, consent, and control for individuals.
Security in AI #
The protection of AI systems and their components from unauthorized access, use, disclosure, disruption, modification, or destruction. Security in AI requires addressing issues such as data protection, system hardening, access control, and incident response.
Ethics in AI #
The principles and values that guide the design, development, deployment, and use of AI systems. Ethics in AI includes issues such as fairness, accountability, transparency, privacy, security, and social and environmental impact.