Introducing AI in Education Glossary for Researchers and Practitioners
By Waleed S. Mandour, Dec. 23
From personalized learning experiences to automated assessment and feedback, AI is not just reshaping how we teach but also redefining how we learn. Perhaps, one of the most significant breakthroughs in the educational field is the emergence of conversational Large Language Models (LLMs) like OpenAI’s ChatGPT and Google’s Gemini. Indeed, they are today’s gateways to an immersive world of interactive and tailored education, whether it is for teaching language, law, or medicine. These developments call for us to become acquainted with a new vocabulary: a glossary of AI in Education that would support our epistemological background to maximize the benefits of adopting AI in teaching and learning. That is what I am presenting to both the researcher and the practitioner in the field.
AI in Education: A Game Changer
AI’s role in education is multifaceted and profoundly impactful. Adaptive learning systems tailor educational content to individual learner's needs, allowing for a more personalized and effective learning experience. It is believed by all of us that AI-powered tutoring systems provide support that was once the exclusive domain of human tutors, making quality education more accessible.
Perhaps the most revolutionary aspect is the advent of conversational LLMs like ChatGPT. These models have the ability to understand and generate human-like text, enabling them to converse, answer queries, and even create educational content. This not only makes learning more interactive but also introduces a level of flexibility and adaptability that was previously unimaginable.
The Power of the Right Terminology
Staying abreast of the terminology is crucial, especially with the enormous epistemological leaps since the 4th industrial revolution. Therefore, understanding terms like Neural Networks, Machine Learning, Adaptive Learning, and Natural Language Processing is essential for both researchers and practitioners in education. This knowledge not only aids in comprehending the tools and technologies being used but also in critically evaluating their applications and implications in education.
For researchers, this glossary serves as a foundational framework for exploring new AI-driven educational methodologies. For educators and practitioners, on the other hand, it offers insights into integrating these technologies into their teaching practices effectively. I have collected the terms from multiple sources followed by an expert review using the triangulation method (which is a part of my research work) to ensure accuracy and representativeness. However, the interdisciplinary field of AI in education is constantly evolving, so staying updated on the latest developments is crucial for effective implementation and responsible application.
The Glossary of AI in Education: 60 Core Terms To Know
In the table below, I present a glossary of 60 fundamental terms associated with the emerging interdisciplinary domain: AI in Education, organized alphabetically. I have included definitions and applicable examples (drawn with the help of GPT-4 and Gemini Pro) in the context of teaching and learning English, Math, or IT. Additionally, for those interested in expanding their knowledge, I have provided references to some of the most cited papers related to each concept, based on Google Scholar ratings. This resource is tailored to deepen understanding and support further exploration in the field of AI in Education.
# | English Term | Definition | Example Application in Teaching/Learning (English, Math, IT) | Examples From Top-Cited Papers |
|---|---|---|---|---|
1 | Adaptive Learning | A technology-driven approach to education that adjusts the content and pace of learning to the individual learner's needs. | In Math, adaptive learning platforms adjust the difficulty levels of problems based on student performance, ensuring personalized learning experiences. | |
2 | AI in Education | The application of Artificial Intelligence techniques to facilitate and enhance learning, teaching, and administrative processes in educational settings. | In IT classes, AI is used to demonstrate real-world applications like predictive analytics or AI-driven software development. | |
3 | AI Hallucination | This phenomenon occurs when the AI generates responses that are not grounded in reality or factual information, often as a result of the model's limitations in understanding context, the complexity of the input, or biases in the training data. To overcome this issue, LLM developers tend to deploy multiple methods such as fine-tuning with feedback and using advanced algorithms. | When learning English, an AI incorrectly teaches a student that "ain't" is a formal contraction in English, suitable for use in academic writing or formal speech. This reflects the AI's misunderstanding or misrepresentation of language norms and formalities. | |
4 | AI Safety | The field of study in AI that aims to ensure that AI systems are safe and operate as intended. | In computer science courses, students learn about the ethical implications and safety considerations essential to AI development. | |
5 | AI-Driven Curriculum Development | The term refers to using AI tools and methodologies to design and implement educational curricula. | In English, AI-driven tools analyze student performance and engagement data to suggest updates in literature or grammar topics. | |
6 | AI-powered Assessment | The use of AI technologies to automate and enhance the assessment of student learning. | In Math, AI-powered systems provide instant grading and feedback on complex problems, enhancing learning efficiency. | |
7 | AI-powered Tutoring Systems | Educational software that uses AI to provide personalized tutoring and feedback to students. | In IT, AI tutors offer personalized guidance and support in coding, algorithms, or network management. | |
8 | Algorithmic Bias | The tendency of AI systems to produce results that are systematically prejudiced due to erroneous assumptions in the machine learning process. It can be either gender, cultural, or data bias. | If AI-driven tools are predominantly trained on datasets consisting of predominantly one dialect or accent (say, American English), they might not effectively recognize or properly evaluate other dialects or accents (like British, Australian, or Indian English). This could lead to the unfair assessment of learners' language skills | |
9 | Artificial General Intelligence (AGI) | An AI system with the ability to understand, learn, and apply its intelligence to solve any problem, much like human intelligence. | In advanced IT courses, discussions around AGI potential and its implications for future technology developments. | |
10 | Artificial Neural Network (ANN) | A computational model based on the structure and functions of biological neural networks. The term is used for pattern recognition and machine learning. | In Math, ANNs are used to model and solve complex numerical problems, demonstrating advanced computational techniques. | |
11 | Attention Mechanism | A component in neural networks that helps the model focus on relevant parts of the input for making decisions. | In machine translation systems when translating from English to Arabic. For instance, in translating the sentence "The cat sat on the mat" to Arabic, the attention mechanism helps the model focus on the word 'cat' in English to accurately translate it to 'القطة' in Arabic. This mechanism ensures the model pays attention to relevant parts of the sentence during translation | |
12 | Augmented Reality (AR) | A technology that superimposes a computer-generated image on a user's view of the real world, thus providing a composite view. | In English literature, AR brings texts to life by providing immersive, interactive experiences of literary worlds and characters. | |
13 | Automated Essay Scoring (AES) | A technology used in educational settings to evaluate and grade written essays using algorithms. AES systems analyze the text of essays, assessing various aspects such as grammar, coherence, logic, and subject matter relevance. These systems use natural language processing and machine learning techniques to simulate human grading, providing a scalable and consistent method for essay evaluation. | It can be seen in standardized testing environments, like the GRE, where essays are graded not only by human assessors but also by an AI system. The AES tool evaluates the essays for various elements such as structure, coherence, grammar, and relevance to the prompt. | |
14 | Big Data | Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. | In Math, big data concepts are applied in statistics and probability lessons to teach data analysis and interpretation. | |
15 | Chatbot | A computer program designed to simulate conversation with human users, especially over the Internet. | In language learning, chatbots, such as the ChatGPT phone app, provide conversational practice, aiding in language fluency and comprehension. | |
16 | Clustering | A type of unsupervised learning that involves grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. | In Math, clustering algorithms can be used in teaching statistical categorization and pattern recognition. | |
17 | Computer Vision | The application of AI technologies that enable computers to interpret and analyze visual data from the world. In education, it can be used for automated grading analyzing, and grading visual assignments like diagrams, sketches, or handwritten work and Interactive Learning Environments (ILE). | In Math, an AI-driven system uses computer vision to analyze handwritten equations. It can then provide instant feedback on the correctness of the solutions, identify common errors, and offer personalized hints or additional practice problems based on the student's performance. | |
18 | Cognitive Robotics | A branch of robotics that involves programming robots with a form of human-like cognition to enable them to perform complex tasks. | An IT course could involve a robot that interacts with students to teach programming concepts. The robot could analyze students' coding patterns, provide feedback on their programming assignments, and adapt to the difficulty of tasks based on the student's learning progress. | |
19 | Data Augmentation | The process of increasing the amount and diversity of data for training models, especially by modifying existing data or creating synthetic data. | In computer science, data augmentation techniques are taught to enhance the quality and quantity of training datasets in AI. | |
20 | Data Cleaning | The process of detecting and correcting (or removing) corrupt or inaccurate records from a dataset. | In IT, data cleaning is taught as part of data management skills, emphasizing its importance for accurate data analysis. | |
21 | Data Integration | Combining data from different sources and providing users with a unified view of these data. | In Math, data integration is used to combine data from various sources for comprehensive statistical analysis. | |
22 | Data Privacy | Data Privacy in the context of AI and technology refers to the handling, processing, and storage of personal and sensitive information in a manner that respects individual rights and complies with legal standards. It involves safeguarding data against unauthorized access or disclosure and ensuring that personal information is used responsibly, ethically, and only for its intended purpose. | Teaching English involves an online language learning platform that collects students' personal information, progress data, and interaction logs. Ensuring data privacy in this context means implementing robust security measures to protect this sensitive information from unauthorized access. Encryption and Data Anonymization are two methods to use here. | |
23 | Data Preprocessing | Techniques applied to raw data to prepare it for further processing and analysis. | In IT, students learn about data preprocessing techniques as a crucial step in the data analysis pipeline. | |
24 | Data Visualization | The representation of data or information in a graph, chart, or other visual format to help users understand the significance of data. | In Math, using data visualization tools to aid in the interpretation of complex statistical data and trends. | |
25 | Data-Driven AI | Artificial intelligence systems that are trained and developed based on large sets of data. These systems analyze and learn from the data to make decisions or predictions, adapting their behavior and understanding based on the information they process. | A data-driven AI language teaching tool could analyze vast amounts of text and spoken language data to understand language patterns and usage. It could then provide personalized language lessons to students, adapting the teaching content and difficulty based on the learner's progress, common mistakes, and learning pace. | |
26 | Deep Learning | A subset of machine learning in AI that imitates the workings of the human brain in processing data and creating patterns for use in decision making. | In computer science, deep learning is explored for its applications in image recognition, NLP, and predictive modeling. | |
27 | Educational Data Mining (EDM) | The process of using data mining techniques to analyze data from educational settings to improve learning outcomes and teaching methods. | In IT, EDM is used to analyze student learning patterns, providing insights for personalized education strategies. | |
28 | Explainable AI (XAI) in Education | AI methods and techniques that make the results and operations of AI systems understandable to humans. | In language teaching, an AI-driven language learning platform not only identifies areas where a student needs improvement, such as verb conjugation or vocabulary but also explains why certain exercises are chosen. For instance, if the AI recommends additional practice with past tense verbs, it would explain that this recommendation is based on the student's frequent errors in past tense usage in their recent exercises. That helps learners understand the reasoning behind specific learning paths, enhancing their educational experience by making AI decisions clear and understandable. | |
29 | Explainable Machine Learning (MLX) | A subset of AI focused on making machine learning processes and outcomes understandable to human users. See Explainable AI (XAI). | In Math, MLX can be used to demonstrate how AI models solve complex problems, providing step-by-step explanations to enhance student comprehension. | |
30 | Explainable Reinforcement Learning (XRL) | Reinforcement learning where the decision-making process is made transparent and understandable. | In IT classes, XRL can demonstrate how certain algorithms optimize network performance, giving students practical insights into network management. | |
31 | Fine-Tuning | Adjusting a pre-trained model slightly to adapt it to specific tasks or datasets. | An example of fine-tuning in the context of English teaching/learning is adapting a general AI language model to specialize in English language tutoring. Initially, the model is trained on diverse linguistic data to understand various aspects of the English language. Then, it undergoes fine-tuning with specific datasets focused on English grammar, vocabulary, idioms, and common language learning challenges. | |
32 | Human-in-the-Loop AI | AI systems that include human interaction in their learning and decision-making processes. | In Math education, this can involve students providing feedback on AI-generated problem sets, helping the system to better align with curriculum standards. | |
33 | Intelligent Learning Environments (ILEs) | Advanced technology-based learning environments that adapt to the needs of learners. | ILEs can be used in IT education to create simulations that adapt to students' skill levels, providing personalized learning experiences. | |
34 | Job Displacement | The risk of jobs being automated and workers being displaced by AI and other technologies. | In teaching, this concept can be explored in economics or social studies, discussing the impact of AI on future job markets. | |
35 | Knowledge Representation | The method used by AI systems to represent information about the world, actions, and goals to solve complex problems. Essentially, it's about creating data structures and algorithms to represent knowledge about the world, such as objects, facts, events, and the relationships between them, in a format that AI systems can process and reason about. This representation is crucial for AI to effectively perform tasks like problem-solving, decision-making, and learning. | In English, knowledge representation techniques can be used to map out plot structures to analyze exams results. | |
36 | Language Model | A statistical model that calculates the probability of a sequence of words. Technically, it is an algorithmic model that determines the likelihood of a sequence of words. It's used in natural language processing to predict the next word in a sentence, based on the words that precede it. These models are fundamental in various language-related tasks such as speech recognition, text generation, and machine translation. See also, Large Language Model. | In teaching English, language models can generate realistic language exercises, helping students practice grammar and vocabulary. | |
37 | Large Language Model (LLM) | It is a more complex and advanced type of language model that is trained on vast amounts of text data. These models use deep learning, particularly neural networks, to understand and generate human-like text. LLMs are capable of performing a wide range of language tasks, including translation, summarization, question answering, and text generation, with a high level of proficiency. Their large training datasets enable them to have a broad understanding of language nuances, context, and even specific domain knowledge. | In English learning, LLMs like GPT-3.5 or Gemini Pro (Bard) can create interactive writing exercises or generate creative writing prompts. | |
38 | Learning Rate | In machine learning, this refers to the step size at which learning progresses. It controls how much the weights in the model are adjusted with respect to the loss gradient. A higher learning rate allows the model to learn faster, potentially skipping over important details or causing instability. | In Math education, learning rate concepts can be used to optimize the pace at which students learn new mathematical concepts. | |
39 | Long Short-Term Memory (LSTM) | A type of recurrent neural network especially suited for learning sequences and time series data. Unlike standard feedforward neural networks, LSTMs have feedback connections, making them suitable for processing sequences of data. They are particularly adept at learning from experience to classify, process, and predict time series when there are time lags of unknown duration between important events. This makes LSTMs ideal for tasks such as speech recognition, and language modeling. | In Math, LSTMs can be used to predict student performance trends, aiding in personalized learning planning. | |
40 | Machine Ethics | The branch of ethics that studies the moral implications and responsibilities of AI. | In IT ethics classes, discussions can center around the ethical use and implications of AI in society. | |
41 | Machine Learning | A subset of artificial intelligence that involves the development of algorithms and statistical models enabling computers to perform tasks without explicit programming. It focuses on using data and algorithms to imitate the way humans learn, gradually improving accuracy. ML is used in a wide range of applications, from email filtering and computer vision to self-driving cars. The core principle is to learn from data, identify patterns, and make decisions with minimal human intervention. | An AI-powered tutoring system that adapts to each student's learning style and pace: the system analyzes students' performance on math problems, identifying areas of difficulty and mastery. Based on this data, the AI then personalizes the learning content, offering targeted exercises and tutorials to address specific weaknesses. | |
42 | Machine Perception | A system to interpret and understand data from the physical world using sensors and machine learning algorithms. It encompasses the replication and enhancement of human senses like sight, sound, and touch in machines. | In computer science classes, exploring machine perception can include how computers process and interpret images and sounds. | |
43 | Multi-Modal Learning | The concept refers to mimicking human information processing in AI systems that can process and interpret information from multiple types of data sources or sensory inputs simultaneously, such as text, images, and audio. | In English and other languages, multi-modal AI tools can enhance language learning through interactive, sensory-rich experiences. | |
44 | Natural Language Inference (NLI) | As a subfield of Natural Language Processing in AI, NLI refers to determining whether a given hypothesis can logically and semantically be inferred from a given premise. The goal is to ascertain whether the hypothesis is true (entailment), false (contradiction), or undetermined (neutral) based on the given premise. This involves a deep understanding of language semantics. See Natural Language Processing | In IT, NLI can be used to develop systems that automatically evaluate the logical coherence of code comments or documentation. | |
45 | Natural Language Processing (NLP) | A branch of AI that focuses on enabling machines to understand, interpret, and respond to human language in a way that is both meaningful and useful. It involves the development of algorithms that can process, analyze, and generate human language, whether in text or spoken form. NLP applications include language translation, sentiment analysis, speech recognition, and chatbots. | In English learning, NLP can be used for the automated grading of essays or for providing personalized feedback on language use. | |
46 | Neural Network | A computational model designed to simulate the way the human brain analyzes and processes information. | In IT, neural networks can be used to teach students about complex data processing and pattern recognition. | |
47 | Neuromorphic Computing | A type of computing that mimics the structure and functioning of the human brain. | In computer science education, neuromorphic computing can be introduced to explore advanced computational models and AI development. | |
48 | Parsing | The process of analyzing a string of symbols, either in natural language or computer languages. | In programming classes, parsing can be taught to understand how compilers work and how to process programming languages. | |
49 | Pattern Recognition | The ability of systems to identify and categorize data based on the patterns and regularities in the data. This is a fundamental aspect of AI that allows machines to interpret, categorize, and learn from data. In education, this can be applied in various ways, such as recognizing handwriting in student submissions, categorizing student responses in assessments, or identifying learning patterns to tailor educational content to individual student needs. | In Math, pattern recognition can be applied to teach students about sequences, series, and mathematical relationships. | |
50 | Predictive Analytics | Techniques that use statistical algorithms and machine learning to identify the likelihood of future outcomes based on historical data. | In IT, predictive analytics can be utilized to teach data analysis, forecasting, and decision-making processes. | |
51 | Prompt Engineering | The skill of crafting inputs (prompts) to effectively communicate with and direct the behavior of AI models, especially in natural language processing. | In English language teaching, prompt engineering can help create effective and engaging prompts for AI-based language practice tools. | |
52 | Real-Time Processing | The processing of data as it is inputted or received, without delay. | In IT classes, real-time processing can be demonstrated through live coding sessions or interactive programming exercises. | |
53 | Recurrent Neural Network (RNN) | A type of neural network where connections between nodes form a directed graph along a temporal sequence, allowing it to exhibit temporal dynamic behavior. | In advanced Math or IT classes, RNNs can be used to model sequences and time-series data, such as financial forecasting. | |
54 | Responsible AI | The practice of designing, developing, and deploying AI with good intention to empower employees and businesses, and fairly impact customers and society – allowing for accountability and transparency. | In IT ethics, responsible AI can be discussed to emphasize the importance of ethical considerations in AI development and deployment. | |
55 | Semantic Analysis | The process of understanding the meaning and interpretation of words and sentences in context. This technique is crucial in educational technologies for various applications like analyzing student essays for content and coherence, interpreting open-ended responses in assessments, or providing contextually relevant information in intelligent tutoring systems. | In English learning, semantic analysis can be used for automated comprehension tests or to analyze the context and meaning of literature. | |
56 | Sentiment Analysis | The term refers to the interpretation and classification of emotions (positive, negative, neutral) within text data. In education, this can be applied to analyze student feedback, essays, or online discussion forums to gauge their sentiments and emotional responses. This understanding can help educators and institutions in assessing student satisfaction, emotional well-being, and engagement levels, allowing for a more responsive and supportive educational environment. | In English classes, sentiment analysis can be applied to analyze the tone and mood of texts or student feedback. | |
57 | Speech Recognition | The ability of a machine or program to identify words and phrases in spoken language and convert them to a machine-readable format. | In language learning, speech recognition can be used for pronunciation practice and oral language assessments. | |
58 | Speech Synthesis | An AI technology that converts written text into spoken language. In education, speech synthesis is employed to create audiobooks, assist visually impaired students, and develop language learning applications. It enhances accessibility by providing text materials in an auditory format, allowing students to listen to textbooks or instructions. See Text-to-Speech. | In English classes, it can be applied to enhance reading and language learning experiences. For instance, students could use text-to-speech tools to listen to literary texts or poems. This not only aids in understanding pronunciation and intonation but also supports students with reading difficulties or those who are auditory learners. | |
59 | Text Classification | The process of categorizing text into organized groups, using NLP. In education, text classification can be applied in various ways, such as automatically categorizing student essays into different grades or feedback categories, sorting student inquiries into relevant topics for efficient response, or organizing digital library resources for easier access. | In a Math class, students can write short explanations of their problem-solving process. The text classification system can then categorize these explanations based on the mathematical concepts or strategies employed, such as 'algebraic methods,' 'geometric reasoning,' or 'statistical analysis.' | |
60 | Text-to-Speech (TTS) | A form of speech synthesis that converts text into spoken voice output. | In English and language arts, TTS can aid in reading comprehension by providing auditory versions of texts for students. |
Conclusion: Embracing the AI Lexicon in Education
As we venture further into this AI-driven educational landscape, the need to understand and engage with this technology becomes more pronounced. I hope that the glossary of AI in Education is more than just a list of terms. By familiarizing ourselves with this lexicon, we equip ourselves to harness the potential of AI, ensuring that we are not just passive observers but active participants in this exciting educational evolution.
Please let me know how you like it. And if you do, what are the possible ways you think of using AI? I am sure there is at least ONE term that sparks a thought 😄
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Introducing AI in Education Glossary for Researchers and Practitioners © 2023 by Waleed Mandour is licensed under Attribution-NonCommercial-ShareAlike 4.0 International CC BY-NC-SA 4.0
To cite this work, please use the following APA reference:
Mandour, W. (2023, December 16). Introducing AI in Education Glossary for Researchers and Practitioners. Retrieved from osf.io/8qb9v
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