Artificial Intelligence: Human-computer Interaction Methodologies

Artificial Intelligence: Human-computer Interaction Methodologies

• discover the key concepts covered in this course
• identify main steps in the HCI process and name multiple methodologies used
• describe the principles of the anthropomorphic approach to HCI
• describe the principles of the cognitive approach to HCI
• describe the principles of the empirical approach to HCI
• name and define multiple models that can be useful in HCI studies
• identify reasons why the iterative approach has shown to be most practical when designing software applications
• describe the principles of prototyping and distinguish between a prototype and a demonstration product
• define use cases for an AI application by creating personas and scenarios
• create a prototype of a simple AI application
• troubleshoot usability of an AI application prototype
• specify the central role of user feedback and focus groups in SlideHCI studies
• recognize how CI/CD became essential to any kind of software company and list multiple factors that make CI/CD important for AI companies
• summarize the key concepts covered in this course

Human computer interaction (HCI) design is the starting point for an artificial intelligence (AI) program. Overall HCI design is a creative problem-solving process oriented to the goal of satisfying largest number of customers. In this course, you will cover multiple methodologies used in the HCI design process and explore prototyping and useful techniques for software development and maintenance. First, learn how the anthropomorphic approach to HCI focuses on keeping the interaction with computers similar to human interactions. The cognitive approach pays attention to the capacities of a human brain. Next, learn to use the empirical approach to HCI to quantitatively evaluate interaction and interface designs, and predictive modeling is used to optimize the screen space and make interaction with the software more intuitive. You will examine how to continually improve HCI designs, develop personas, and use case studies and conduct usability tests. Last, you will examine how to improve the program design continually for AI applications; develop personas; use case studies; and conduct usability tests.

Artificial Intelligence: Human-computer Interaction Overview

Artificial Intelligence: Human-computer Interaction Overview

• discover the key concepts covered in this course
• define Human Computer Interaction as a multidisciplinary field essential to computer science and describe its importance for the success of software companies
• list the components involved in human-computer interaction (HCI) studies and specify their role
• identify main objectives of HCI studies
• recognize the multidisciplinary nature of HCI and list the areas most involved in the studies
• describe the development of HCI studies and their significance to the overall software development process
• specify recent trends in HCI research and list ideas that can become most impactful
• list the tools commonly used for HCI studies and specify their purpose
• identify potential users of an AI application and define the context for its use
• describe the role of a user-oriented approach in the success of AI applications
• specify why explainability research in AI is required for developing user friendly applications
• apply common tools used in HCI to choose appropriate tasks for an AI application
• work with simple mock-ups and identify techniques useful for developing interfaces
• summarize the key concepts covered in this course

In developing AI (artificial intelligence) applications, it is important to play close attention to human-computer interaction (HCI) and design each application for specific users. To make a machine intelligent, a developer uses multiple techniques from an AI toolbox; these tools are actually mathematical algorithms that can demonstrate intelligent behavior. The course examines the following categories of AI development: algorithms, machine learning, probabilistic modelling, neural networks, and reinforcement learning. There are two main types of AI tools available: statistical learning, in which large amount of data is used to make certain generalizations that can be applied to new data; and symbolic AI, in which an AI developer must create a model of the environment with which the AI agent interacts and set up the rules. Learn to identify potential AI users, the context of using the applications, and how to create user tasks and interface mock-ups.