User Experience (UX) Design In the AI Age Graduate Academic Certificate

Course Description:
This graduate-level course provides an in-depth exploration of usability and user experience metrics with a focus on meeting the needs of enterprise clients, technical users, and individual users. Students will learn how to evaluate websites, mobile applications, enterprise software, dashboards, customer portals, infrastructure platforms, and information systems using evidence-based UX methods. The course covers usability testing, heuristic evaluation, card sorting, tree testing, UX statistics, performance metrics, issue-based metrics, self-reported metrics, behavioral analytics, and AI-assisted UX tools.

Students will examine how UX metrics support product adoption, workflow efficiency, error reduction, audit readiness, user trust, customer satisfaction, and business value in enterprise technology settings. They may engage in real or simulated enterprise client consulting to understand business goals, technical workflows, user roles, evaluation questions, and stakeholder expectations. Hands-on projects reinforce user-centered evaluation, client needs analysis, technical product evaluation, data interpretation, stakeholder communication, and professional consulting practice. By the end of the course, students will be prepared to evaluate complex digital products and enterprise software, identify usability and workflow problems, and provide evidence-based recommendations that improve user satisfaction, operational efficiency, product quality, and client outcomes.

Course Objectives:
By completing this course, students will be able to:

• Identify usability and user experience needs of enterprise clients, technical users, individual users, and key stakeholders.
• Explain how UX metrics support user satisfaction, product adoption, workflow efficiency, error reduction, compliance, and business performance.
• Evaluate websites, mobile applications, enterprise software, dashboards, customer portals, infrastructure platforms, and information systems using evidence-based UX methods.
• Select and apply appropriate UX methods, including usability testing, heuristic evaluation, card sorting, tree testing, UX analytics, and AI-assisted evaluation.
• Analyze performance-based, issue-based, self-reported, behavioral, and tree testing metrics to identify usability, accessibility, workflow, documentation, and design problems.
• Translate UX evidence into actionable recommendations for users, product teams, IT leaders, executives, and enterprise clients.
• Prepare professional UX reports, executive summaries, severity matrices, and stakeholder presentations that connect user experience findings to client needs and organizational goals.

Course Format:
This course is offered 100% online through Canvas and is designed to be applied, interactive, and client-oriented. Students will learn through online modules, readings, lectures, discussions, quizzes, hands-on assignments, guest speaker sessions, case studies, and applied UX evaluation projects. Students may engage with real or simulated enterprise clients to understand business goals, technical environments, user workflows, product challenges, and evaluation priorities. The course prepares students to contribute to UX research, product analytics, enterprise software evaluation, technical consulting, and client-facing roles in organizations that build and support large-scale digital systems.

Course Description: 
This graduate-level course is only taught during the fall semester. It introduces MA in IxD (Interaction Design) candidates and other UNT graduate students to knowledge-construction approaches and methods that are rooted in and guided by what designers refer to as “human-centered problem framing.” The learning experiences that transpire as this course evolves have been designed to help students gain the understandings necessary to better comprehend—rather than assume—how and why particular human needs and wants should be accounted for and examined within a given situation or set of circumstances. These analyses and assessments are then utilized to help students develop and critically explore a wide variety of ideas that could positively affect this situation or set of circumstances. Finally, these ideas become the “fuel” that guides the design of physical or digital prototypes, or some combination of both, that can be tested and improved and tested and improved again until they are ready to be realized as design process outcomes suitable for implementation or potential manufacture.

Course Objectives:
By completing this course, students will be able to: 

• Effectively engage in the kinds of observational and interactive research necessary 
  to uncover hidden frustrations among specific user groups and audiences
• Construct knowledge about how analyzing the real-world experiences of these people can yield understandings that guide effective design decision-making
• Work within a cross-functional team to generate a multiplicity of original product, service, and/or system ideas in a non-judgmental environment, and then synthesize these into tangible, actionable concepts that are informed by the knowledge 
  of the entire group
• Engage in the kinds of “build-to-think” and “rapid experimentation” methods that guide the practice of creating quick, roughly functional mockups, such as simple sketches, paper prototypes of interfaces, and basic models, that allow a wide variety 
  of ideas to be tested early in the design process (this type of “rapid prototyping” and “usability testing” teaches students to assess what’s working and what’s failing before lots of time and money are spent on developing something that is flawed)
• Present the ideas they’ve developed in ways that enable them to be effectively understood and then implemented or created/manufactured either by those who comprised their user-groups or audiences, and/or those whose financial and logistical support would be necessary to realize and sustain them.

Course Format: 
This course is structured into two subsequent sections, each of which transpires over the course of eight weeks. The first entails a series of lectures by the course instructor that introduces students to key aspects of human-centered problem framing, user research, idea generation, prototyping, and usability testing. These will provide the means for students working in teams of three or four to identify and frame a “problematic or undesirable” situation or set of circumstances that they wish to design a product, service, or system to address. The second section involves the student teams working to develop and prototype ideas that become manifest as a tangible product, service, or system, and then formulating 
and showcasing a “pitch presentation” of whatever they will have developed to a critical audience of design faculty and User Experience and Interaction Design professionals.

Course Description:
This graduate-level course provides an in-depth exploration of information architecture with a focus on organizing, structuring, labeling, designing, and documenting information in complex digital environments. Students will learn how to analyze user needs, business goals, content types, and organizational contexts to develop effective information structures for websites, applications, digital libraries, enterprise portals, knowledge bases, and AI technologies.

The course covers organization systems, labeling systems, navigation systems, search systems, metadata, taxonomy, content inventory, content modeling, card sorting, tree testing, sitemap development, wireframe documentation, navigation design, and IA evaluation. Students will also learn how to document IA decisions through professional design tools such as Figma, including sitemaps, navigation models, page structures, user flows, low-fidelity wireframes, and content organization diagrams.
Students will examine how strong information architecture supports findability, content governance, user navigation, digital service quality, and scalable content management. Hands-on projects reinforce content organization, taxonomy development, navigation design, search structure, user-centered information design, and professional IA deliverables. By the end of the course, students will be prepared to design, document, evaluate, and improve information structures that support access, navigation, and content discovery across digital products and services.

Course Objectives:
By completing this course, students will be able to:

• Explain core principles and components of information architecture, including organization, labeling, navigation, search, metadata, taxonomy, and content structure.
• Analyze user needs, organizational goals, content types, and business requirements for digital information environments.
• Design organization, labeling, navigation, and search systems that improve findability, usability, content discovery, and information access.
• Develop professional IA deliverables, including content inventories, sitemaps, taxonomies, metadata structures, content models, user flows, and navigation models.
• Apply IA research methods, including card sorting, tree testing, content analysis, and IA evaluation, to support evidence-based design decisions.
• Use tools such as Figma to create sitemaps, wireframes, page structures, navigation flows, and content organization diagrams.
• Prepare client-ready IA reports and redesign proposals that communicate content structure problems, navigation recommendations, taxonomy decisions, and evidence-based improvements.

Course Format:
This course is applied, interactive, and project-based. Students will learn through readings, lectures, case studies, discussions, hands-on IA exercises, design tool demonstrations, and applied information architecture projects. Assignments may include website or application analysis, content inventory, card sorting, tree testing, sitemap development, taxonomy design, navigation redesign, Figma-based wireframe documentation, and IA evaluation reports.

Students may work with real or simulated clients to assess content organization problems, identify navigation needs, design improved information structures, and present evidence-based IA recommendations. The course prepares students to communicate IA decisions through professional reports, visual diagrams, sitemaps, navigation models, wireframes, and client-ready design documentation.

Course Description:
This graduate-level course explores how Artificial Intelligence (AI), AI-Generated Content (AIGC), and emerging design AI technologies are transforming UX design and the digital product development lifecycle. Students will learn how to use AI to support user research, customer requirements analysis, market trend discovery, ideation, visual concept generation, UX writing, wireframing, prototyping, interface design, design-to-code workflows, usability evaluation, accessibility review, personalization, and iterative product improvement.

Students will work with AI technologies such as large language models, multimodal AI tools, generative image tools, AI prototyping platforms, AI website builders, design-system assistants, recommendation engines, and AI-enabled UX research platforms. The course examines how AI can accelerate design exploration, synthesize user feedback, support front-end implementation, and produce client-ready UX deliverables. Students will also evaluate limits of AI-generated design outputs, including bias, privacy, data quality, hallucination, explainability, accessibility, governance, and overreliance on automation.

Through hands-on exercises, case studies, workshops, and applied projects, students may work with real or simulated enterprise clients to define UX problems, apply AI tools across the lifecycle, evaluate design alternatives, and present evidence-based recommendations. By the end of the course, students will be prepared to responsibly use AI technologies to create, evaluate, and improve digital products and intelligent user experiences.

Course Objectives:
By completing this course, students will be able to:

• Explain foundational concepts of AI, AIGC, multimodal AI, and design AI in UX.
• Apply AI tools across research, requirements analysis, ideation, design, prototyping, implementation support, testing, and iteration.
• Use AI-assisted methods to analyze customer requirements, user feedback, market trends, text, image, video, and behavioral data.
• Develop AI-supported wireframes, prototypes, interface concepts, UX content, visual assets, and website design solutions.
• Use design AI to support design systems, front-end design, accessibility review, and design-to-development handoff.
• Conduct AI-enhanced UX evaluation to identify design, content, accessibility, workflow, and user experience problems.
• Critically evaluate ethical, social, bias, privacy, data quality, explainability, and governance challenges in AI-empowered UX design practice.

Course Format:
This course is applied, interactive, and project-based. Students will learn through readings, discussions, hands-on AI tool exercises, case studies, workshops, and applied AI-empowered UX design projects. Assignments include AI research analysis, AI-assisted concept generation, AI-supported prototyping, website or interface development support activities, a midterm workshop, a final project, and a final presentation. The course prepares students for AI-assisted UX design, digital product design, enterprise software innovation, technical consulting, and client-facing roles.