Environmental Design
The process of addressing surrounding environmental parameters when devising plans, programs, policies, buildings, or products. Important for creating designs that are sustainable and contextually appropriate.
The process of addressing surrounding environmental parameters when devising plans, programs, policies, buildings, or products. Important for creating designs that are sustainable and contextually appropriate.
A user-centered design process that involves understanding users' needs and workflows through field research and applying these insights to design. Essential for creating designs that are deeply informed by user contexts and behaviors.
A pop-up dialog that appears when a user attempts to leave a page or application, which can be used to prevent loss of progress or data, or to confirm user intent. While it can be used ethically to prevent data loss or confirm actions, designers must avoid using it to deceive, delay, block, or interfere with the user's intent, thus ensuring it does not become a dark pattern.
A phenomenon where users consciously or subconsciously ignore banner-like information or advertisements on websites. Important for designing effective web content that captures user attention.
A research technique that explores the context in which users interact with a product, service, or environment to understand their needs and behaviors. Crucial for gaining deep insights into user contexts and designing more relevant solutions.
A method in natural language processing where multiple prompts are linked to generate more complex and contextually accurate responses. Essential for enhancing the capability and accuracy of AI models in digital products that rely on natural language understanding.
In-product assistance provided within the context of a specific task or screen, tailored to the user's current needs. Important for enhancing user experience by providing timely and relevant assistance.
A research method where participants take photographs of their activities, environments, or interactions to provide insights into their behaviors and experiences. Important for gaining in-depth, visual insights into user contexts and behaviors.
Qualitative data that provides insights into the context and human aspects behind quantitative data. Crucial for gaining deep insights into user behaviors and motivations.
The tendency to forget information that can be easily found online, also known as digital amnesia. Important for understanding how access to information impacts memory and designing experiences accordingly.
Research conducted in natural settings to collect data on how people interact with products or environments in real-world conditions. Crucial for gaining authentic insights into user behaviors and contexts.
A qualitative research method that studies people in their natural environments to understand their behaviors, cultures, and experiences. Crucial for gaining deep insights into user behaviors and contexts.
A structure or framework used to create effective prompts for AI systems, ensuring clarity and context. Important for standardizing and improving AI prompt design.
A method used in AI and machine learning to ensure prompts and inputs are designed to produce the desired outcomes. Essential for improving the accuracy and relevance of AI responses.
Needs and expectations that are not explicitly stated by users but are inferred from their behavior and context. Crucial for identifying and addressing unarticulated user needs.
A self-regulation strategy in the form of "if-then" plans that can lead to better goal attainment and behavior change. Useful for designing interventions that promote positive user behaviors.
The study of how individuals make choices among alternatives and the principles that guide these choices. Important for designing decision-making processes and interfaces that help users make informed choices.
The process of linking language to its real-world context in AI systems, ensuring accurate understanding and interpretation. Crucial for improving the relevance and accuracy of AI-generated responses.
A search method that seeks to improve search accuracy by understanding the contextual meaning of terms in a query rather than just matching keywords. Important for understanding modern search algorithms and optimizing content accordingly.
A research method that focuses on understanding phenomena through in-depth exploration of human behavior, opinions, and experiences, often using interviews or observations. Essential for gaining deep insights into user needs and behaviors to inform design and development.
A research method that involves observing subjects in their natural environment. Crucial for gathering authentic data and insights into real-world behaviors and interactions.
A field research method where researchers observe and interview users in their natural environment to understand their tasks and challenges. Crucial for gaining authentic insights into user behavior and needs.
Quantitative data that provides broad, numerical insights but often lacks the contextual depth that thick data provides. Useful for capturing high-level trends and patterns, but should be complemented with thick data to gain a deeper understanding of user behavior and motivations.
A principle often used in behavioral economics that suggests people evaluate options based on relative comparisons rather than absolute values. Important for understanding decision-making and designing choices that highlight beneficial comparisons.
The error of making decisions based solely on quantitative observations and ignoring all other factors. Important for ensuring a holistic approach to decision-making.
Technologies that enable machines to understand and interpret data on the web in a human-like manner, enhancing connectivity and usability of information. Essential for improving data interoperability and accessibility on the web.
The Principle of Exemplars is an information architecture guideline that uses representative examples to illustrate content categories. Crucial for enhancing user understanding and facilitating content discovery.
Net Promoter Score (NPS) is a metric used to measure customer loyalty and satisfaction based on their likelihood to recommend a product or service to others. Crucial for gauging overall customer sentiment and predicting business growth through customer advocacy.
A network of real-world entities and their interrelations, organized in a graph structure, used to improve data integration and retrieval. Crucial for enhancing data connectivity and providing deeper insights.
Case-Based Reasoning (CBR) is an AI method that solves new problems based on the solutions of similar past problems. This approach is essential for developing intelligent systems that learn from past experiences to improve problem-solving capabilities.