Glossary
Master the language of AI to make informed decisions, develop effective policies, and integrate technology meaningfully into classrooms. Each definition combines industry-standard explanations with specific educational applications, giving you the clarity and confidence to lead in an AI-powered future.
A
Academic integrity
The honest and responsible use of information and resources in educational settings. It means properly citing sources, doing your own work, and using AI tools ethically to support learning rather than bypass it. With AI becoming common in classrooms, academic integrity now includes understanding when and how to appropriately use these tools for learning.
Adaptive algorithms
Computer programs that automatically adjust their behavior based on data and feedback, learning from patterns to improve performance over time. In education, these algorithms power personalized learning platforms that adjust difficulty, pacing, and content based on how each student performs, creating customized learning paths for every learner.
Adaptive interfaces
User interfaces that automatically adjust their layout, features, or presentation based on user needs, preferences, or abilities. In educational settings, this means learning platforms that can change font sizes for struggling readers, simplify navigation for younger students, or provide alternative controls for students with disabilities.
Adaptive learning
Educational technology that adjusts content difficulty, pacing, and teaching methods in real-time based on individual student performance and learning patterns. This approach ensures each student gets appropriately challenging material—neither too easy nor too frustrating—maximizing engagement and learning outcomes.
AI accessibility
The design and implementation of AI systems to be usable by people with disabilities or diverse needs. In schools, this means AI tools that work with screen readers, offer text-to-speech, provide visual alternatives, and ensure every student can participate fully regardless of their abilities.
AI adoption
Autonomous software programs that can perceive their environment, make decisions, and take actions to achieve specific goals without constant human intervention. In education, AI agents might automatically grade assignments, monitor student progress, create study guides, or alert teachers when students need help.
AI assessment
The evaluation of AI systems' effectiveness, accuracy, and impact on intended outcomes. For schools, this means measuring whether AI tools actually improve student learning, teacher efficiency, and educational equity while identifying areas for improvement.
AI content detection
Technology that identifies whether text, images, or other content was created by AI rather than humans. Schools use these tools to maintain academic integrity, helping teachers distinguish between student-generated work and AI-generated submissions.
AI ethics in education
The moral principles and guidelines specifically governing AI use in educational settings. This includes protecting student privacy, ensuring equitable access, preventing academic dishonesty, maintaining human relationships in teaching, and prioritizing genuine learning over metrics.
AI evaluation checklist
A systematic tool for assessing AI systems before implementation in schools. These checklists typically cover data privacy, educational effectiveness, bias detection, cost-benefit analysis, accessibility features, and alignment with curriculum standards.
AI explainability
A systematic tool for assessing AI systems before implementation in schools. These checklists typically cover data privacy, educational effectiveness, bias detection, cost-benefit analysis, accessibility features, and alignment with curriculum standards.
AI governance
The framework of policies, procedures, and oversight mechanisms for managing AI use in organizations. School districts need AI governance to ensure responsible deployment, protect student data, maintain educational quality, and comply with regulations.
AI guidelines
Documented best practices and rules for using AI in specific contexts. Educational AI guidelines typically cover appropriate use cases, data handling procedures, academic integrity standards, and ethical considerations for students and teachers.
AI hallucination
When AI systems generate completely false or nonsensical information that appears plausible but has no basis in reality or its training data. In education, hallucinations pose serious risks as students might receive entirely incorrect facts that sound authoritative, making it crucial to teach verification skills and maintain human oversight of AI-generated content.
See also: AI confabulation
AI confabulation
When AI systems mix real facts with fabricated details, creating partially true but ultimately unreliable information. Unlike pure hallucination, confabulation is particularly dangerous in education because the blend of truth and fiction makes errors harder to detect, requiring careful fact-checking of all AI-generated educational content.
See also: AI hallucination
AI implementation
The practical process of deploying AI systems in real-world settings. Successful implementation in schools requires technical setup, staff training, pilot testing, policy creation, and ongoing support to ensure the technology enhances rather than disrupts learning.
AI literacy
The ability to understand, evaluate, and effectively work with AI technologies, including their capabilities and limitations. For students and educators, AI literacy is becoming as fundamental as digital literacy, encompassing both using AI tools effectively and understanding their impact.
AI model
A mathematical framework trained on data to make predictions or decisions. Educational AI models are trained on learning data, successful teaching strategies, and curriculum standards to provide personalized instruction and assessment.
AI policy
Formal rules and regulations governing AI development and use. School AI policies address student data privacy, acceptable use, academic integrity, equity considerations, and teacher autonomy while ensuring compliance with educational regulations.
AI professional development
Training programs designed to help educators effectively integrate AI into their practice. This includes learning to use AI tools, understanding AI capabilities and limitations, developing AI literacy, and adapting pedagogy for AI-enhanced classrooms.
AI prompting in the classroom
Teaching students and using effective prompts to get better results from AI systems. This emerging skill helps learners articulate clear questions, provide appropriate context, and critically evaluate AI responses for academic work.
AI readiness
An organization's preparedness to successfully adopt and benefit from AI technologies. School readiness includes technical infrastructure, staff skills, supportive policies, cultural openness to change, and clear educational goals for AI use.
AI risk management
Identifying, assessing, and mitigating potential negative impacts of AI systems. In education, risks include data breaches, bias in assessments, over-reliance on technology, academic dishonesty, and loss of human connection in teaching.
AI safety
Measures ensuring AI systems don't cause harm to users or society. For schools, AI safety includes content filtering, age-appropriate responses, prevention of harmful advice, protection from manipulation, and maintaining student wellbeing.
AI support
Resources and assistance for effectively using AI technologies. This includes technical help desk services, professional development, peer mentoring, documentation, and ongoing coaching to help educators integrate AI successfully.
AI training
The process of teaching AI systems to perform specific tasks using data and feedback. In education, this includes training models on curriculum standards, successful teaching strategies, and diverse student learning patterns.
AI transparency
Making AI systems' operations, decision-making processes, and limitations clear and understandable to users. For educators and students, transparency means understanding how AI grades assignments, makes recommendations, or identifies learning gaps.
AI-assisted instruction
Teaching that incorporates AI tools to enhance delivery and personalization of content. Teachers use AI to differentiate lessons, provide instant feedback, identify struggling students, and create customized learning materials while maintaining human oversight.
AI-generated content
Material created by artificial intelligence systems, including text, images, videos, or audio. In education, this includes practice problems, lesson plans, study guides, and creative writing, and requires careful review for accuracy and appropriateness.
AI-powered
Describes systems or tools that use artificial intelligence as their core technology. AI-powered educational tools go beyond simple automation to provide intelligent adaptation, prediction, and personalization based on data analysis.
AI-powered gamification
Using AI to create adaptive game-like learning experiences that adjust to student performance. These systems personalize challenges, rewards, and progression paths to maintain optimal engagement and learning for each student.
AI-supported blended learning
Combining traditional face-to-face instruction with AI-enhanced digital learning. AI personalizes the online components, tracks progress across both formats, and helps teachers coordinate in-person activities with digital practice.
Algorithm
A step-by-step procedure for solving problems or completing tasks. In education, algorithms determine how AI systems assess student work, recommend content, adapt difficulty levels, and predict learning outcomes.
Artificial intelligence (AI)
Computer systems capable of performing tasks that typically require human intelligence, such as understanding language, recognizing patterns, and making decisions. In education, AI transforms teaching through personalization, automation of routine tasks, and insights into student learning.
At-risk alerts
Automated notifications when AI systems detect students who may be struggling or falling behind. These alerts help teachers intervene early, identifying issues like consistent wrong answers, declining engagement, or patterns suggesting learning difficulties.
Automated grading
Technology that evaluates and scores student work without human intervention. Automated grading now includes essay evaluation and complex problem-solving, giving teachers more time for instruction while students get faster feedback. Modern systems often include batch grading capabilities, allowing teachers to evaluate entire class sets simultaneously while maintaining consistency across all submissions.
Automation
Using technology to perform tasks with minimal human intervention. In education, automation handles routine tasks like attendance, grading, scheduling, and progress reporting, freeing educators to focus on teaching and relationship-building.
B
Bias in AI
Systematic errors or unfair preferences in AI systems resulting from biased training data or flawed algorithms. In education, bias can affect grading, recommendations, and learning opportunities, making it critical to ensure AI treats all students equitably.
C
Change management
The structured approach to transitioning individuals and organizations to new ways of working. Implementing AI in schools requires careful change management to address teacher concerns, update processes, and shift institutional culture.
Chatbot
A computer program designed to simulate human conversation through text or voice. Educational chatbots can serve as 24/7 tutors, answer student questions, provide practice conversations for language learning, and offer immediate support when teachers aren't available.
ChatGPT
A specific large language model developed by OpenAI that can engage in conversational dialogue.
Fun fact: GPT stands for "Generative Pre-trained Transformer."
Color-coded mastery levels
Visual systems using colors to represent student progress and understanding. AI-powered dashboards often use green for mastery, yellow for developing, and red for needs support, helping teachers quickly identify who needs help.
Content generation
The creation of educational materials using AI systems. This includes generating lesson plans, practice problems, reading passages, quiz questions, and explanations, though all AI-generated content requires human review for accuracy and appropriateness.
Conversational AI
AI systems designed for natural, human-like dialogue that understands context and maintains conversation flow. In learning environments, conversational AI lets students ask questions in their own words and receive explanations that feel like talking with a patient tutor.
D
Data analysis
The process of examining data to discover patterns, trends, and insights, often using data mining techniques to uncover hidden relationships. Educational data analysis helps identify effective teaching strategies, predict student outcomes, understand learning patterns, and make evidence-based decisions by systematically exploring large sets of student and classroom data.
Data transparency
Clear communication about what data is collected, how it's used, and who has access. Schools must maintain data transparency to build trust with students and parents while complying with privacy regulations.
Data visualization
Presenting data in visual formats to make patterns and insights easily understandable. Educational dashboards use visualization to show student progress, class trends, and learning analytics in ways teachers can quickly interpret and act upon.
Digital citizenship
Responsible and ethical behavior in digital environments. With AI tools, digital citizenship expands to include understanding AI capabilities, recognizing AI-generated content, protecting data privacy, and using AI ethically for learning.
Digital pedagogy
Teaching methods and practices that effectively integrate digital technologies into education. AI requires evolving digital pedagogy to include new approaches like AI-assisted differentiation, automated feedback loops, and data-driven instruction.
Digital transformation
The comprehensive integration of digital technology into all areas of an organization. In education, digital transformation includes adopting AI tools, modernizing infrastructure, updating curriculum, and preparing students for an AI-influenced future.
Dynamic instructional adjustments
Real-time modifications to teaching based on immediate student feedback and performance data. AI enables these adjustments by continuously analyzing student understanding and suggesting or automatically implementing appropriate changes.
E
EdTech AI
Artificial intelligence specifically designed for educational technology applications. EdTech AI focuses on learning outcomes, pedagogical principles, and the unique needs of students and teachers rather than general-purpose AI applications.
Educational AI tools
Software applications using artificial intelligence to support teaching and learning. These include intelligent tutoring systems, automated graders, learning analytics platforms, content generators, and adaptive learning systems.
Engagement patterns
Recurring behaviors that indicate how students interact with learning materials and activities. AI analyzes engagement patterns like time on task, click patterns, and response rates to understand motivation and predict success.
F
Feedback loops
Cyclical processes where outputs influence future inputs, creating continuous improvement. In educational AI, positive feedback loops help systems learn which teaching methods work, while teachers use student feedback to refine AI implementation.
G
Generative AI
AI systems that create new content like text, images, or code based on learned patterns. In education, generative AI helps students brainstorm ideas, create first drafts, generate practice problems, and explore creative possibilities while learning about AI capabilities.
H
Human-in-the-loop
An approach keeping humans actively involved in AI operations for oversight and validation. In education, this ensures teachers remain in control, using AI insights to inform professional judgment rather than replacing human decision-making.
I
Instant feedback systems
Technology providing immediate responses to student work or questions. AI-powered instant feedback helps students correct mistakes right away, understand concepts before moving on, and stay engaged through timely reinforcement.
Intelligent tutoring systems
Computer programs providing personalized instruction without human intervention, adapting to individual student needs. These systems offer consistent, patient, always-available support that complements human teaching by providing practice and reinforcement.
Interactive polling
Real-time questioning systems that collect and display student responses instantly. AI enhances polling by analyzing responses to identify misconceptions, adjust follow-up questions, and provide immediate clarification where needed.
Interactive simulations
Digital environments where students can experiment and learn through manipulation of variables. AI-powered simulations adapt scenarios based on student actions, provide guided exploration, and create personalized learning experiences. For example, a chemistry simulation might let students mix virtual chemicals safely, with AI adjusting difficulty and providing hints based on their understanding.
Intervention workflow
The systematic process for identifying and supporting struggling students. AI streamlines intervention workflows by automatically detecting at-risk students, suggesting evidence-based strategies, and tracking intervention effectiveness.
L
Large language model (LLM)
Large neural networks trained on enormous text datasets, capable of understanding and generating human-like text. LLMs power sophisticated educational AI that can explain complex concepts, answer questions, and engage in educational dialogue across subjects.
Learning analytics
The measurement, collection, and analysis of data about learners to understand and optimize learning. AI-powered learning analytics help educators identify at-risk students, evaluate curriculum effectiveness, and make data-driven instructional decisions.
Learning gaps identification
The process of discovering specific areas where students lack understanding or skills. AI excels at identifying learning gaps by analyzing patterns in student responses, comparing against learning objectives, and pinpointing exact misconceptions.
Live insights
Real-time information about student learning as it happens. AI provides live insights showing who's struggling, what concepts are challenging the class, and when to intervene, enabling immediate instructional adjustments.
M
Machine learning
AI systems that improve performance through experience without explicit programming. In education, machine learning algorithms learn from student interactions to better predict struggles, recommend resources, and personalize learning paths. Advanced forms like deep learning use layered neural networks to recognize complex patterns in student behavior and learning progressions.
N
Natural language processing (NLP)
The branch of AI enabling computers to understand and generate human language. In education, NLP powers chatbots, automated essay scoring, language translation, and systems that understand student questions in natural language.
P
Predictive analytics
Using data and algorithms to identify likely future outcomes based on patterns. In education, predictive analytics can identify students at risk of dropping out, predict concept mastery, and anticipate needed interventions.
Prompt
The input text or instruction given to an AI system to elicit a response. Teaching students effective prompting is becoming a crucial skill, helping them get better results from AI tools for learning and creation.
Prompt engineering
The practice of designing and optimizing prompts for better AI outputs. For educators and students, prompt engineering skills help them use AI tools more effectively for teaching, learning, and creative work.
Prompt library
The practice of designing and optimizing prompts for better AI outputs. For educators and students, prompt engineering skills help them use AI tools more effectively for teaching, learning, and creative work.
R
Real-time data collection
Gathering information continuously as events occur rather than after the fact. Educational AI collects real-time data on student responses, engagement, and progress, enabling immediate intervention and support.
Responsible AI
Developing and deploying AI ethically with consideration for societal impact. In schools, responsible AI means protecting student privacy, ensuring equitable access, preventing misuse, and maintaining focus on genuine learning outcomes.
S
Student data privacy
Protection of personal educational information from unauthorized access or misuse. With AI systems handling sensitive data, schools must ensure compliance with FERPA, COPPA, and other regulations while maintaining trust with families.
Synthetic media
Artificially generated content including deepfakes, AI-generated images, or synthetic voices. Schools must teach students to identify synthetic media, understand its implications, and use these technologies ethically and responsibly.
T
Tech fatigue
Physical and mental exhaustion from overuse of technology. As AI tools proliferate in education, managing tech fatigue requires balancing digital and non-digital activities, ensuring technology serves learning rather than overwhelming students.
Technology integration
The effective incorporation of technology tools into teaching and learning processes. Successful AI integration goes beyond just using tools to fundamentally enhance pedagogy, improve outcomes, and prepare students for a technology-rich future.
Text-to-speech
Technology converting written text into spoken words, often paired with speech recognition for two-way communication. In education, text-to-speech helps struggling readers, supports language learners, assists students with visual impairments, and provides alternative ways to access content, while speech recognition allows students to respond verbally, creating fully accessible learning experiences.
Training data
The information used to teach AI systems how to perform tasks. Educational AI training data includes student work samples, curriculum standards, successful teaching strategies, and diverse learning patterns, requiring careful curation for quality and bias prevention.
V
Virtual assistant
AI-powered software that helps users complete tasks through natural language interaction. Educational virtual assistants help students find resources, answer questions, manage schedules, and provide learning support like a digital teaching aide.