Syllabus

Instructor: Joel Salisbury

Email: joel@uconn.edu

Meeting: Tuesdays & Thursdays, 9:30 AM–10:45 AM

Location: Art Building 228

Office Hours: By appointment, via Teams

I’m Joel Salisbury. I’m the Director of Institutional Insights & Innovation (i3) at UConn, where I lead a multidisciplinary team working across digital product design and development, institutional data, research, and emerging technology.

I also teach in Digital Media & Design, and much of my professional work involves designing, building, and making decisions about digital systems that real people need to use. That makes accessibility relevant to me as a design and technology practice (as well as a legal requirement due to the nature of the work).

In this course, we’ll approach accessibility in much the same way: by investigating real problems, using evidence, testing assumptions, and making decisions we can defend.

DMD 3998 examines accessibility and inclusion in interactive media through critique, applied production, and collaborative challenge work. Students build fluency in standards, policy, and practical design decisions while learning how to make persuasive evidence-based accessibility judgments.

Accessibility here is not treated as a side topic. It is part of the course's core design process, and it is revisited through reading, testing, research, making, revision, and critique across the semester.

The course is organized around modules, sessions, challenges, and class work rather than fixed calendar weeks. A module may take one, two, or more class sessions depending on class progress, holidays, cancellations, particularly productive investigations, and other instructional needs.

The course website is the primary instructional hub for today/current course activity, modules, sessions, challenges, the Field Guide, readings and authoritative resources, accessibility tools and demonstrations, sample interactive experiences, assignment information, archived class work, and course information.

HuskyCT/Blackboard remains the official location for grades, assignment submission, quizzes, the official syllabus, and other official LMS functions. The Laravel site complements that system; it does not replace it.

• Explain core accessibility frameworks and inclusive design concepts.
• Analyze interfaces using legal, standards, and institutional perspectives.
• Communicate remediation priorities with clear evidence and rationale.
• Use accessibility tools, standards references, and AI critically rather than uncritically.
• Distinguish accessibility claims from unsupported assumptions, opinions, or automated outputs.

Students study accessibility through multiple sources of authority: legal requirements, technical standards such as WCAG, and institutional policy. The course emphasizes distributed inquiry followed by collective synthesis so that students can explain not only what a standard says, but why it matters and what it can and cannot prove.

Module 01 introduces the Legal Lens, Standards Lens, and Institutional Lens. Later modules continue that pattern by connecting visual design, media, interaction, structure, cognition, and AI practice to standards-based evaluation and remediation.

In legal and policy terms, Section 504 remains relevant to federally funded educational programs and activities, ADA Title II applies to public entities such as public universities, and Section 508 governs federal-agency information and communication technology. These frameworks are related but not interchangeable, and UConn's Digital Accessibility Policy operationalizes relevant requirements within the institution's context.

The course website is designed as a working studio and reference library. It includes Today, Modules, Sessions, Challenges, the Field Guide, sample experiences, and archived class work so students can revisit the course structure while studying or preparing for quizzes and the final project.

The site also hosts demonstrations, study references, and sample interactive experiences used during accessibility investigation. These are instructional materials, not official grade records or the source of formal course policy.

• Today / current course activity
• Modules
• Sessions
• Challenges
• Field Guide
• Readings and authoritative resources
• Accessibility tools and demonstrations
• Sample interactive experiences
• Archived Class Work / team deliverables

The class uses persistent teams for distributed inquiry followed by collective synthesis. Different teams may investigate different dimensions of a shared problem, such as legal requirements, technical standards, institutional policy, user impact, technical implementation, or testing.

Team membership is assigned and managed in HuskyCT/Blackboard. The course website may refer to team identities and team challenge work, but it is not the source of team membership records.

The course includes 9 challenges total. These are module challenges and cumulative assessment moments, not weekly challenges, and they are not tied to a rigid calendar schedule. Teams investigate, test, discuss, and then produce a concise deliverable that can later be archived as shared study material in the module's Class Work area.

Teams generally do not give formal presentations for each challenge. Instead, challenges are used to support investigation, critique, and synthesis.

Challenge 05: Accessibility Review

Challenge 05 is an individual, 10-point comprehensive accessibility review cumulative across Modules 01–05. Students identify all deliberate issues in a custom interactive experience using visual, media, keyboard, semantic, form/error, screen-reader, and assisted testing, then submit evidence-based findings, recommendations, verification plans, and priorities.

The course includes 3 individual quizzes. These quizzes emphasize application, reasoning, accessibility scenarios, WCAG concepts, remediation, distinguishing accessibility from compliance, identifying unsupported claims, and interpreting markup or interface behavior.

Quizzes are checkpoints for reading and reasoning, not memorization exercises. They ask students to explain how evidence supports an accessibility judgment.

Quiz 2 is an at-home assessment on Thursday, October 29, 2026. It covers existing course material and practical accessibility evaluation skills consistent with the curriculum. Check HuskyCT for official quiz information.

The final project receives approximately the final two weeks of instructional time. Students independently apply the course process: Investigate → identify barriers → establish evidence → prioritize → redesign → test → defend.

The point of the project is not merely to check accessibility boxes. Students are expected to make an experience meaningfully better and provide convincing evidence for that work.

The Final Project will be released Thursday, October 29. Its existing assessment structure and grading remain unchanged; further project information will be shared through the official course channels.

The module sequence and pacing are subject to change based on class progress, emerging technologies, cancellations, and particularly productive areas of investigation. Modules are not tied to specific calendar weeks. All substantive changes will be communicated to students.

Note: Team challenge grades normally apply to all members of the team. Individual grades may be adjusted when there is clear evidence that a student did not meaningfully participate.

AI output is not evidence that something is accessible.

Generative AI may be used unless an assignment says otherwise. Students must disclose meaningful use, verify important claims, and be able to explain their process when asked. AI can help with research, explanation, critique, drafting, and exploration, but it cannot replace evidence, testing, or the student's responsibility for the work.

Privacy and data considerations still apply. Students should not paste sensitive or private information into external tools unless explicitly permitted by the assignment or instructor guidance. If an assignment limits AI use or requires a particular method, those instructions take precedence.

The course maintains academic-integrity expectations around sources, process, and authorship. When AI use is unclear, students may be asked to explain how it was used, what sources informed the work, and what testing supports the final submission.