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

Note: This course is offered online.

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.

Note: This course is offered in-person at UNT Denton campus.

Course Description: 
This graduate-level course is taught only during the fall semester. Facilitated alongside ADES 5420 (Human-Centered Interaction Design), this course helps students in the MA in IxD program and across the UNT landscape develop both the conceptual understandings and practical skills necessary to realize effective interaction design outcomes. As this set of learning experiences evolves, students will learn how and why fundamental interaction design principles and methods can improve the usability and adaptability of digital interfaces. As this is a project-based course, each student enrolled in it will be responsible for identifying and framing an opportunity to develop, design, test, re-design, and re-test a new interactive product or service system and the interface that enables its operation(s). By semester’s end, this project will be realized as a mid-fidelity prototype supported by a case study report.

Course Objectives:
By completing this course, students will be able to develop an interactive product or service system that effectively incorporates broadly and deeply informed knowledge of and about:

• how and why physical, perceptual, and cognitive human factors affect the usability 
  of various types of interface designs,
• how and why the management of cognitive load and mental effort should be accounted for in interaction design processes
• how and why prioritizing recognition over recall is essential to the evolution of effective interaction design processes
• how and why aligning the design of a given interactive system or interface should align with extant mental models
• the necessity of ascertaining how and why the needs and wants of a specific group 
  of users should align with the particular functions and features of whatever product 
  or service system is being designed for them (or, more desirably, with them…)
• ways to create and test the affordances and signifiers (by engaging in various types 
  of prototyping) that will constitute a given interactive product or service system 
  to positively affect design decision-making

Course Format: 
The calendrical structure of this course is designed so that weekly lectures, discussions, and readings can effectively contextualize and guide the week-to-week, interactive product or service system development and design processes described above. The opening portions of each weekly, three-hour, evening-based, class session will be devoted to the facilitation of lectures and critical dialogue. The latter portion of each of these class sessions will be devoted to constructive, critical exchanges between individual students and the instructor and small groups of three to four students each. These exchanges will be centered around the design decisions each student will have made week-to-week as they engage in the iterative, heuristically guided decision-making processes necessary to develop a mid-fidelity prototype and case study report that effectively: 

1. Showcases the essential functions and features of whatever they will have designed 
and constructed as an interactive product or service system, and

2. Articulates/explains how and why key design decisions were made in the manner 
that they were as that student’s project progressed over the course of the semester.

Note: This course is offered online.

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.

Note: This course is offered online.

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.

Note: This course is offered in-person at UNT Denton campus.

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 meets once per week from 6:30 to 9:20 pm on a night that is yet to be determined, and is structured into two subsequent, eight-week sections. 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.

Note: This course is offered in-person at UNT Denton campus.

Course Description:
This graduate-level course is only taught during the spring semester. Throughout the course, graduate students from the MA in Interaction Design (IxD) program and other disciplines will work individually and in teams of three or four to apply insights gained through evidence-based, design-led research. They will use this knowledge to inform the design decisions required to create or improve one to three service design systems. These students will also be challenged to engage in broadly informed, secondary research to:

• guide competitive analyses,
• prevent redundant work,
• identify gaps in previous endeavors as a means to justify new inquiries, and
• construct the theoretical foundations necessary to build credible, well-framed research

As each course project evolves, students will utilize the knowledge and understandings they will have constructed and discovered to improve or invent the features, functions, and organizational structures inherent in these service design systems. These “improvements and inventions” will occur as each project is cyclically prototyped and tested. By the end of the semester, each student will emerge with one to three case studies that showcase their abilities to engage in the variety of design processes necessary to realize an effective and efficient service design system.

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

• effectively formulate and engage in empathetically guided research approaches and methods in ways that yield useful and usable knowledge about a given user group or target audience
• engage in the types of initial, wide-spectrum research and analysis necessary to identify problematic service systems, or specific aspects of them, and then understand the contextual factors and conditions that affect these
• expand their abilities to interact with others—both as study participants and research partners or project collaborators—in ways that enhance their knowledge regarding how and why people unlike themselves perceive particular situations as they do (and then act on these perceptions)
• evolve their respective abilities to merely “perform as directed” within or according to a given set of external project parameters to the point where they are able to viably and reliably contribute to the actions undertaken by a design and development team.

Course Format:
This course meets once per week from 6:30 to 9:20 pm on a night that is yet to be determined.  Each class session will begin with an instructor-led presentation and discussion. Students and student teams will then present and critically discuss the progress of their service design projects, which will be showcased as prototypes in various stages of development. The latter portion of the semester will likely be devoted to undertaking service design project work on behalf of one or more of our Department of Design’s industry partners (in this/these instances, the entire class roster may work on a single project, with small groups assigned to specific aspects of that project).

Note: This course is offered in-person at UNT Denton campus.

Course Description:
This graduate-level course is almost always taught in the fall semester, although it is occasionally taught in the spring semester. The learning experiences that comprise it provide MA in Interaction Design students and other graduate students across UNT three to five opportunities during a 16-week semester (including exam week) to build the knowledge needed to develop complex visual information systems, graphically explicit diagrams, and instructional materials. Students then apply this knowledge by engaging in project-based activities that challenge them to design and implement these on behalf of particular groups of audiences and/or users and stakeholders. The goal of each of these activities is not merely to visualize complex data sets to allow these audiences and users to glean denotative meaning (“a home is a place where someone lives”), but to allow them to glean connotative meaning (“a specific home is warm/cool, cozy, safe, and welcoming”). By the end of the semester, students in ADES 5450 will have developed at least two interactive, information design systems. These should not only communicate essential meaning effectively to a specific audience, but also influence how these people perceive, then reflect upon, and finally act (or refrain from acting) on whatever has been presented.

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

• effectively analyze common data domains (text, cartography, networks, multivariate analog and digitally facilitated sources) to discern essential patterns and relationships in ways that can effectively guide the construction of information design systems that function as either analytical tools, storytelling systems, or both
• engage in iteratively structured design decision-making processes that allow them
  to evolve from initial ideation/idea generation project phases to rough sketching/initial prototyping to final realization as interactive, visually communicative systems
• develop their abilities to guide the iterative development of narratives that allow specific audiences to compare, contrast, and contextualize complex data domains
  and data sets in ways that allow them to analyze and synthesize these into useful
  and usable information, and—eventually—understandings
• learn to classify information gleaned from their critical analysis of various data sets into the following six structural rubrics (that then guide how a given information-based “story” should most effectively be structured): hierarchical, relational, temporal, spatial, spatial-temporal, and textural
• physically design data domains and sets into well-constructed systems manifest as and/or comprised of statistical charts, graphs, maps, diagrams, interactive products
  and experiences, and video and aural shorts, or some combinations of these

Course Format:
This course meets once per week from 6:30 to 9:20 pm on a night that is yet to be determined.  Each class session will begin with an instructor-led presentation and discussion. Students and/or student teams will then present and critically discuss the progress of whatever project-based challenge they’re addressing according to the course schedule. The first portion of the semester will be devoted to individual student projects, while the latter portion of the semester will be devoted to projects that will be addressed by teams of three or four students who possess varied academic and professional backgrounds.