User-Centered Design (UCD) is an iterative design approach that focuses on understanding users' needs, preferences, and limitations throughout the design process. Crucial for creating products that are intuitive, efficient, and satisfying for the intended users.
A cognitive bias where people assume others share the same beliefs, values, or preferences as themselves. Important for helping designers avoid projecting their own biases and assumptions onto users during research and design.
A cognitive bias where individuals with low ability at a task overestimate their ability, while experts underestimate their competence. Crucial for designers to create educational content and user interfaces that accommodate varying levels of user expertise.
The ability to perform actions or behaviors automatically due to learning, repetition, and practice. Important for understanding user habits and designing intuitive user interfaces.
A theory that suggests people learn behaviors, skills, and attitudes through observing and imitating others, as well as through direct experiences. Crucial for understanding how users acquire new behaviors and designing educational or training programs.
The process of preparing, equipping, and supporting individuals to successfully adopt change to drive organizational success and outcomes. Crucial for ensuring smooth transitions and effective implementation of new processes or systems.
A principle that suggests people are more likely to comply with requests or follow suggestions from authority figures. Important for designing persuasive experiences and understanding user compliance.
A memory aid that helps individuals recall information through associations, patterns, or acronyms. Important for designing educational content and interfaces that enhance memory retention.
Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that uses human input to guide the training of AI models. Essential for improving the alignment and performance of AI systems in real-world applications.
A phenomenon where learning is improved when study sessions are spaced out over time rather than crammed together. Crucial for designing educational and training programs that enhance long-term retention.
A cognitive bias where people rely too heavily on their own perspective and experiences when making decisions. Important for designers to recognize and mitigate their own perspectives influencing design decisions.
A phenomenon where people are more likely to remember information when they are in the same state of consciousness as when they learned it. Important for understanding how context affects memory recall and designing experiences that facilitate better retention.
A symmetrical, bell-shaped distribution of data where most observations cluster around the mean. Fundamental in statistics and crucial for many analytical techniques used in digital product design and data-driven decision making.
A type of artificial intelligence that enables systems to learn from data and improve over time without being explicitly programmed. Crucial for developing intelligent systems that can make data-driven decisions.
The ability to recognize, understand, and manage one's own emotions and the emotions of others. Essential for designing empathetic user experiences and effective team collaboration.
The tendency to favor people who are similar to oneself in terms of background, beliefs, or interests. Important for recognizing and mitigating bias in user research and team dynamics.
The process of making tools, methods, and knowledge accessible to a broader range of people within an organization or community, allowing non-specialists to participate and contribute meaningfully. Important for fostering inclusivity, enhancing collaboration, and leveraging diverse perspectives to improve outcomes across various disciplines.
A method of splitting a dataset into two subsets: one for training a model and another for testing its performance. Fundamental for developing and evaluating machine learning models in digital product design.
An AI model that has been pre-trained on a large dataset and can be fine-tuned for specific tasks. Essential for developing state-of-the-art NLP applications.
Large Language Model (LLM) is an advanced artificial intelligence system trained on vast amounts of text data to understand and generate human-like text. Essential for natural language processing tasks, content generation, and enhancing human-computer interactions across various applications in product design and development.
Knowledge Organization System (KOS) refers to a structured framework for organizing, managing, and retrieving information within a specific domain or across multiple domains. Essential for improving information findability, enhancing semantic interoperability, and supporting effective knowledge management in digital environments.
In AI, the generation of incorrect or nonsensical information by a model, particularly in natural language processing. Important for understanding and mitigating errors in AI systems.
An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Essential for driving data-informed decision making, predicting trends, and uncovering valuable insights in digital product design and development.
A cognitive bias where people ignore general statistical information in favor of specific information. Critical for designers to use general statistical information to improve decision-making accuracy and avoid bias.
The core values outlined in the Agile Manifesto, including individuals and interactions, working software, customer collaboration, and responding to change. Fundamental for guiding agile practices and fostering an agile mindset.
A learning phenomenon where information is better retained when study sessions are spaced out over time rather than crammed in a short period. Crucial for designing educational tools and content that optimize long-term retention.
In AI and machine learning, a prompt that specifies what should be avoided or excluded in the generated output, guiding the system to produce more accurate and relevant results. Crucial for refining AI-generated content by providing clear instructions on undesired elements, improving output quality and relevance.
Generative Pre-trained Transformer (GPT) is a type of AI model that uses deep learning to generate human-like text based on given input. This technology is essential for automating content creation and enhancing interactive experiences.
The core principles that underpin agile methodologies, focusing on collaboration, flexibility, and customer satisfaction. Crucial for guiding agile practices and ensuring effective project delivery.
The process of encoding sensory input that has particular meaning or can be applied to a context, enabling deeper processing and memory retention. Important for understanding how information is processed and stored, enhancing design of educational content.
Guidelines and principles designed to ensure that AI systems are developed and used in a manner that is ethical and responsible. Crucial for building trust and ensuring the responsible use of AI technologies.
A statistical method used to assess the generalizability of a model to unseen data, involving partitioning a dataset into subsets for training and validation. Essential for evaluating model performance and preventing overfitting in digital product analytics.
A cognitive bias where individuals favor others who are perceived to be similar to themselves, affecting judgments and decision-making. Crucial for understanding biases in team dynamics and decision-making processes among designers.
A type of bias that occurs when the observer's expectations or beliefs influence their interpretation of what they are observing, including experimental outcomes. Essential for ensuring the accuracy and reliability of research and data collection.
A set of algorithms, modeled loosely after the human brain, designed to recognize patterns and perform complex tasks. Essential for developing advanced AI applications in various fields.
A bias that occurs when the sample chosen for a study or survey is not representative of the population being studied, affecting the validity of the results. Important for ensuring the accuracy and reliability of research findings and avoiding skewed data.
A sales technique used to uncover a prospect's pain points through a series of targeted questions. Important for understanding customer needs and driving effective sales conversations.
A mindset and approach that embodies the entrepreneurial spirit, passion for improvement, and deep sense of ownership typically associated with a company's founders. Essential for maintaining agility, innovation, and customer-centricity as organizations grow and mature.