Artificial intelligence · IMD Specializations

Faculty Working Draft
Prepared September 2026

Purpose

This document uses artificial intelligence as a second case for defining what an IMD specialization could be. It is not a final curriculum proposal or a request to add a fixed set of courses. It demonstrates how an AI specialization might connect competencies, existing courses, project continuity, portfolio development, facilities, faculty expertise, responsible practice, and career preparation across the current B.A. and B.S. track structure.

The case is organized around a central principle:

An IMD specialization in AI should prepare students to build, critique, and creatively shape intelligent media, not simply use the current generation of AI products.

1. Why AI Is a Useful Specialization Case

AI already appears across IMD and the broader university in multiple forms:

The Computing B.S. provides technical preparation in programming, algorithms, software systems, artificial intelligence, machine learning, deep learning, computer vision, HCI, robotics, and related areas.
The Emerging Creatives B.A. provides preparation in visual and spatial design, digital art, creative coding, critical theory, media culture, and artist-led experimentation.
IMDM350 addresses artificial intelligence and machine learning within their broader social, political, cultural, environmental, and ethical contexts.
The shared IMDM sequence provides interaction design, collaborative production, experiential media, project development, critique, documentation, and public exhibition.
Special topics, research, independent study, and Capstone can support experimental AI work.
UMD is expanding its institution-wide AI curriculum, including new undergraduate degrees, interdisciplinary courses, research infrastructure, and responsible-use initiatives.

The case also presents a strategic challenge. IMD should not duplicate a computational AI degree or a general human-centered AI program. Its contribution should arise from the program's distinctive combination of art, computation, embodiment, immersion, sound, interaction, and public creative practice.

2. Proposed Identity

Working Title

Creative AI and Intelligent Media

Other possible titles include AI for Immersive Media, Creative and Interactive AI, or Intelligent Creative Systems. A broad public-facing label such as AI Specialization may be useful for clarity, but the formal title should communicate the specialization's distinctive IMD focus.

Distinctive IMD Position

The specialization should center the use and study of AI in:

interactive and responsive media;
generative image, animation, sound, music, text, and 3D content;
games, agents, simulation, and adaptive systems;
XR, spatial computing, installation, performance, and physical interfaces;
computer vision, sensing, and multimodal interaction;
creative tools and human-AI collaboration;
artist-led datasets, archives, and alternative models;
critical, cultural, and speculative inquiry; and
public-facing creative production.

The specialization should not be organized around prompt writing or mastery of a particular commercial platform. Tools and models will change rapidly. Students need durable concepts, adaptable production methods, and the ability to evaluate what an AI system does, where its behavior comes from, when it fails, and what consequences follow from using it.

3. Specialization Learning Outcomes

Students completing the specialization should be able to:

Explain foundational AI and machine-learning concepts at a level appropriate to their degree track and creative role.
Distinguish among rule-based systems, classification and prediction, generative models, computer vision, language models, agents, and other forms of intelligent computation.
Prototype creative and interactive systems that use AI intentionally rather than ornamentally.
Integrate AI with visual, auditory, spatial, physical, or real-time media.
Work critically with data, including collection, selection, labeling, consent, licensing, representation, and documentation.
Evaluate model behavior through testing, comparison, failure analysis, and attention to bias, uncertainty, and limitations.
Make responsible decisions concerning authorship, attribution, privacy, labor, accessibility, environmental cost, and potential harm.
Collaborate across technical, creative, and critical roles.
Document AI-assisted and AI-driven work with sufficient transparency to explain human contributions, system behavior, tools, sources, and limitations.
Present a coherent body of work that demonstrates technical or creative depth and prepares the student for professional practice, research, entrepreneurship, or graduate study.

4. Shared Competency Framework

All students in the specialization should encounter the following competencies, although the depth and form may differ by degree track.

Competency	Introduced	Practiced	Advanced	Demonstrated
AI and Model Literacy	Core terms, system types, training, inference, and limitations	Comparison of models and approaches	Selection and adaptation of systems for complex purposes	Clear technical and conceptual account of a substantial system
Computational Foundations	Logic, variables, data, functions, and basic algorithms	APIs, model inputs and outputs, data handling, and integration	Training, fine-tuning, retrieval, agents, pipelines, optimization, or custom tools	Stable implementation appropriate to the student's role
Data and Dataset Practice	Data sources, labels, consent, licensing, and representation	Dataset collection, cleaning, organization, and documentation	Curated, domain-specific, artist-led, or research datasets	Dataset statement, provenance record, and critical justification
Generative and Intelligent Media	Text, image, audio, video, 3D, and multimodal possibilities	Controlled experiments and iterative workflows	Coherent generative systems or intelligent behaviors	Integrated creative work with documented methods and limitations
Interaction and Human-AI Collaboration	Affordance, feedback, agency, control, and uncertainty	Interfaces and workflows that support human judgment	Adaptive interaction, co-creation, agents, or multimodal systems	Tested experience demonstrating intentional human-AI relations
Evaluation and Failure Analysis	Basic comparison, error, bias, and uncertainty	Structured testing across prompts, inputs, users, or conditions	Quantitative and qualitative evaluation appropriate to the project	Evidence-based account of performance, failures, and revisions
Critical and Cultural Context	Historical and contemporary debates about automation and intelligence	Analysis of AI systems as cultural and political objects	Research into labor, power, authorship, representation, and institutions	Critical framing integrated into creative or scholarly work
Responsible Practice	Transparency, privacy, consent, provenance, and harm	Project-specific risk identification and mitigation	Complex decisions involving people, data, public deployment, or sensitive contexts	Responsible-practice statement supported by project evidence
Collaboration and Production	Roles, scope, attribution, and shared workflow	Versioning, documentation, milestones, and critique	Sustained interdisciplinary production and leadership	Publicly presented work with clear credits and process record
Professional and Research Communication	Project descriptions and basic documentation	Process writing, demonstrations, and portfolio records	Case studies, papers, technical documentation, or public explanation	Professional presentation of methods, contributions, and implications

5. One Specialization, Two Track-Specific Routes

The specialization should have a shared conceptual, creative, and responsible-practice core while allowing students to develop depth through the existing degree tracks.

Shared Core

All participating students should develop competency in:

foundational AI and model literacy;
data and dataset practice;
creative experimentation across more than one type of AI system;
interaction and human-AI collaboration;
evaluation, testing, and failure analysis;
critical context and responsible practice;
collaborative production; and
transparent documentation and portfolio presentation.

Computing B.S. Route

The B.S. route would emphasize:

artificial-intelligence algorithms and architectures;
machine learning and deep learning;
computer vision, language, agents, or robotics;
model training, fine-tuning, retrieval, evaluation, and deployment;
data pipelines and software integration;
performance, reliability, privacy, and security; and
technical leadership within interdisciplinary teams.

Relevant existing courses may include CMSC421, CMSC422, CMSC426, CMSC434, CMSC472, CMSC473, CMSC477, and other approved AI, HCI, vision, robotics, graphics, or data-oriented electives.

Emerging Creatives B.A. Route

The B.A. route would emphasize:

generative and computational art;
AI-assisted and AI-driven image, animation, text, sound, music, 3D, and performance;
artist-led datasets, archives, and alternative forms of training or curation;
interactive, spatial, embodied, and installation-based AI;
creative direction and human-AI collaboration;
critical study of authorship, labor, aesthetics, representation, and power;
speculative and research-based practice; and
creative leadership within interdisciplinary teams.

IMDM127, IMDM227, IMDM350, ARTT370, ARTT47x, collaborative studios, and approved special topics provide important existing components.

Shared Standards, Different Depth

The two routes should not imply that B.S. students only build models or that B.A. students only enter prompts into finished products. Every student should understand system behavior, data, evaluation, provenance, and critical context. The track-specific route identifies the student's principal area of depth, not the limits of their participation.

6. Relationship to UMD's Broader AI Curriculum

UMD's new undergraduate AI degrees make differentiation especially important. The Human-Centered AI B.A. combines AI with ethics, policy, design, and the humanities, while the computational B.S. develops greater depth in algorithms, models, and systems.

An IMD specialization should complement these programs by focusing on the design and production of creative, interactive, embodied, spatial, sonic, and experiential AI systems.

Possible collaboration could include:

approved electives across programs;
shared lectures or workshops;
co-taught courses;
interdisciplinary student teams;
joint access to research and computing infrastructure;
faculty affiliates and project advising;
public symposia, exhibitions, and critiques; and
partnerships with the Artificial Intelligence Interdisciplinary Institute at Maryland.

The IMD specialization should be legible as a distinctive pathway while participating in the university's larger AI ecosystem.

7. Current Assets and Current Gaps

Current Assets

Need	Existing or Potential Support
Creative-coding foundation	IMDM127, IMDM227, and related project work
Technical computing foundation	CMSC core and upper-level electives
AI and machine learning	CMSC421, CMSC422, CMSC472, CMSC473, and related courses
Computer vision and sensing	CMSC426, IMDM327, and project-based work
HCI and interaction	IMDM227, IMDM327, IMDM390, and CMSC434
Critical and cultural context	IMDM150 and IMDM350
Visual and digital-art practice	ARTT foundations, ARTT255, ARTT370, and ARTT47x
Sound, performance, and spatial media	IMDM227, IMDM290, IMDM327, special topics, and project-based work
Collaborative production	IMDM101, IMDM290, IMDM390, and Capstone
Experimental depth	IMDM298, IMDM398, IMDM399, IMDM498, and IMDM499
Public demonstration	IMDM491 and the Senior Capstone Showcase
Institutional AI ecosystem	AIM, new AI degrees, cross-campus courses, research labs, and computing initiatives

Current Gaps

No shared AI foundation designed for both IMD tracks. Students need an accessible course that combines model literacy, data, creative experimentation, evaluation, and responsible practice without assuming an upper-level CS background.
Unequal access to technical AI coursework. The clearest AI and machine-learning sequences require substantial computing prerequisites.
Creative AI is not yet a visible curricular progression. Experiments may occur in individual courses or projects without a stable sequence from introduction through advanced production.
Responsible-practice expectations are not standardized. Provenance, disclosure, consent, rights, privacy, bias testing, and documentation may vary across instructors and projects.
Advanced intelligent-media production is not guaranteed. Students may pursue AI in studios, special topics, research, or Capstone, but the experience depends on course availability and individual initiative.
Infrastructure is uneven and potentially costly. API access, cloud services, local models, GPU computing, data storage, software licensing, and technical support require deliberate planning.
Tool change can outpace curriculum revision. The specialization needs durable outcomes that are not tied to one vendor, model, or moment in the technology cycle.
Faculty ownership is distributed. The program needs coordinated leadership connecting creative practice, technical knowledge, critical inquiry, facilities, policy, and external partnerships.
Career claims can become inflated. Students need language that distinguishes creative AI practice, AI-enabled production, human-centered prototyping, and technical AI engineering.

8. Illustrative Curricular Model

The following is a conceptual structure. Course numbers, credit totals, and formal requirements would require a separate curriculum audit and approval process.

Stage 1: Foundation

Students develop foundational ability in creative practice, computation, media, critique, collaboration, documentation, and critical analysis through the existing B.A. or B.S. requirements and the shared IMDM core.

Relevant courses: IMDM101, IMDM127 or CMSC preparation, IMDM150, IMDM227, ARTT foundations, and related track requirements.

AI evidence: a rule-based or generative system, algorithmic artwork, data-driven experiment, interactive agent, critical analysis, or small project examining automation and authorship.

Stage 2: Shared Creative AI Foundations

Students complete an accessible course focused on durable AI concepts and creative experimentation.

Provisional course concept: IMDM2XX: Creative AI Foundations

Core content:

histories and definitions of artificial intelligence;
rule-based systems, machine learning, neural networks, and generative models;
training, inference, prompts, embeddings, retrieval, agents, and multimodal systems at an appropriate introductory level;
data collection, curation, labeling, consent, licensing, and provenance;
creative experiments with text, image, sound, video, interaction, or other media;
APIs, local models, and model integration;
evaluation, uncertainty, bias, and failure analysis;
authorship, labor, privacy, environmental cost, and public impact; and
transparent project documentation.

The course should require more than tool demonstrations. Students should compare systems, investigate limitations, and make creative decisions that could not be reduced to generating isolated outputs.

This course could initially run as a recurring IMDM298 or IMDM398 topic before formal course approval.

Stage 3: Track-Specific Depth

Students select technical, creative, or critical coursework aligned with their intended roles.

Possible B.S. depth: artificial intelligence, machine learning, deep learning, computer vision, HCI, robotics, data visualization, graphics, or related technical fields.

Possible B.A. depth: IMDM350, ARTT370, ARTT47x, and approved work in generative art, sound, animation, 3D, performance, creative coding, interaction, critical media, or related fields.

Cross-track expectation: Students should complete meaningful collaborative work with peers whose principal expertise differs from their own.

Stage 4: Advanced Intelligent-Media Production

Students apply their areas of depth within a sustained creative or research-based production.

This could occur through:

an AI-focused section or project pathway within IMDM390;
a recurring advanced IMDM398 or IMDM498 topic;
a provisional IMDM4XX: Intelligent Media Studio course;
an approved research or independent-study experience; or
an interdisciplinary project developed with another AI program or research unit.

The advanced experience should include concept development, system design, data and model documentation, prototyping, evaluation, revision, responsible-practice review, and public presentation.

Stage 5: Demonstration

Students demonstrate the specialization through Senior Capstone, a substantial advanced project, or an approved portfolio of connected work.

The culminating evidence should include:

a working interactive, generative, adaptive, or research-based system;
a clear account of the student's role and contributions;
documentation of tools, models, data, sources, and dependencies;
evidence of evaluation and failure analysis;
a responsible-practice statement;
a portfolio case study;
appropriate credits and attribution; and
a reflective account connecting the work to critical, cultural, creative, or professional context.

9. Illustrative Credit Structure

A formal specialization should use existing degree requirements and elective space wherever possible. The following model is illustrative rather than prescriptive.

Component	Possible Requirement	Likely Status
Shared foundation	Existing IMDM and track requirements	Already required
Creative AI foundation	One accessible shared course	New or recurring special topic
Critical and responsible context	IMDM350 or an approved shared alternative	Existing for B.A.; access or alternative needed for B.S.
Track-specific depth	One or two approved electives	Largely existing, especially for B.S.
Advanced intelligent-media production	One sustained studio, research, or collaborative project	New, adapted, or recurring special topic
Demonstration	AI-focused Capstone or approved advanced portfolio	Existing structure, with added criteria

A realistic goal would be to create a specialization that adds little or no time to degree completion by using track electives, approved special topics, research opportunities, and existing Capstone requirements.

10. Project Continuity Case

The specialization should support development across courses while requiring new learning and more rigorous documentation at every stage.

Stage	Possible Student Work	Required New Development	Handoff Evidence
Foundation	Rule-based system, generative sketch, data artwork, critical essay, or interactive agent	Basic computation, media, context, and documentation	Source materials, output, reflection, and research notes
Creative AI Foundations	Several model experiments or one small intelligent-media prototype	Model literacy, data practice, comparison, evaluation, and responsible-use analysis	Working prototype, experiment log, provenance record, failure analysis, and next-step memo
IMDM290	Collaborative generative-media or AI-assisted production	Team workflow, integrated media, asset and data pipeline, and production planning	Build, repository, credits, data and tool record, critique notes, and documentation
Depth Course	Model, dataset, tool, visual system, sound system, installation component, or research project	Role-specific technical, creative, or critical expertise	Reusable system or body of work with documentation
IMDM390 or Advanced Studio	Sustained intelligent or adaptive experience	Integration, interaction, public testing, advanced evaluation, and responsible-practice review	Stable system, production archive, test evidence, provenance statement, and portfolio documentation
IMDM490	Capstone proposal and developed prototype	Substantial transformation, feasibility, research, risk analysis, and production planning	Approved proposal, prototype, model and data plan, schedule, resource plan, and evaluation design
IMDM491	Completed and publicly presented work	Refinement, exhibition, audience context, promotion, and professional presentation	Final system, case study, installation or deployment guide, credits, model and data documentation, and postmortem

Students would not be required to carry one AI project through the entire sequence. At each transition, they could continue a promising project, reuse a dataset or software component, transfer a research question, or begin new work informed by documented prior experience.

11. Responsible Practice and Documentation Standard

Responsible practice should be integrated throughout the specialization rather than isolated in one ethics unit.

Every substantial project should address the following areas when relevant:

System and Tool Disclosure

Name the models, services, software, versions, and major dependencies used.
Distinguish human-authored, AI-generated, AI-assisted, and externally sourced material.
Explain why AI was appropriate to the project rather than incidental.

Data and Provenance

Identify the source, ownership, licensing, and consent conditions of data and source materials.
Describe any collection, filtering, labeling, cleaning, augmentation, or curation.
Acknowledge when training data or model provenance is unavailable or uncertain.

Evaluation and Limitations

Test behavior across meaningful inputs, users, or conditions.
Document failures, bias, uncertainty, hallucination, inconsistency, or unwanted outputs.
Explain what the system should not be trusted or expected to do.

People and Potential Harm

Address privacy, consent, identity, representation, accessibility, and foreseeable misuse.
Consider the labor and creative work embedded in datasets, systems, and production pipelines.
Avoid using sensitive personal information without appropriate approval and safeguards.

Resources and Sustainability

Identify significant compute, storage, service, or financial dependencies.
Consider whether a smaller, local, open, or less resource-intensive method could meet the same goal.
Document dependencies that may prevent the project from remaining functional or reproducible.

These requirements should produce project-specific evidence rather than generic statements that an AI system was used “ethically.”

12. Portfolio Expectations

A student completing the specialization should graduate with evidence of experimentation, depth, critical judgment, and transparent practice.

Recommended Portfolio Evidence

At least two small studies comparing different AI methods, models, or creative questions.
Evidence of work with data, including a dataset statement or provenance record.
One project demonstrating model evaluation, failure analysis, or human-centered testing.
One substantial collaborative intelligent-media project.
One example of track-specific depth, such as a trained or adapted model, software tool, interactive agent, generative visual or audio system, installation, critical research project, or artist-led dataset.
A Capstone or equivalent advanced demonstration.
Clear credits identifying the student's role and contributions.
A concise case study explaining intent, system design, sources, human decisions, tests, failures, revisions, limitations, and outcomes.
A durable public record such as a working build, video demonstration, technical walkthrough, installation documentation, paper, or exhibition record.

The portfolio should make clear what the student understands and contributed. A collection of compelling outputs without process, provenance, evaluation, or authorship documentation would not be sufficient evidence of specialization.

13. Faculty Capacity and Institutional Partnerships

A formal specialization requires identifiable faculty leadership, but it should also draw on UMD's larger AI ecosystem rather than attempting to reproduce it inside IMD.

The program should identify a faculty lead or small coordinating group able to:

teach or coordinate the shared Creative AI Foundations course;
connect technical, creative, and critical faculty;
establish responsible-practice and documentation standards;
advise AI-focused studios and Capstones;
maintain relationships with AIM, Computer Science, Art, the iSchool, the humanities, and other relevant units;
coordinate access to computing resources and technical support;
organize visiting artists, researchers, critics, and industry practitioners; and
review the curriculum regularly as methods, policies, and infrastructure change.

A future hire in creative AI, human-centered AI, computational art, or intelligent media could strengthen the specialization. The case for a hire should be evaluated alongside existing faculty capacity, cross-campus partnerships, and the program's other hiring priorities.

14. Facilities and Infrastructure

The specialization would require more than access to consumer AI websites. Students need a stable environment for experimentation, integration, evaluation, and preservation.

Priority needs include:

development workstations capable of real-time media and selected local-model workflows;
managed access to cloud models and APIs, including a predictable instructional budget;
GPU or high-performance computing access for approved advanced projects;
secure storage and appropriate controls for project data;
local and open-source model options where feasible;
version control, experiment logs, and shared documentation systems;
support for integrating AI with game engines, XR, sound, sensors, cameras, and physical-computing systems;
classroom guidance for accounts, licenses, intellectual property, privacy, and changing service terms; and
methods for archiving projects whose external models or APIs may later change or disappear.

The Innovation Design Lab could support weekly work-in sessions, demonstrations, model and dataset clinics, critique, public discussion, and cross-cohort collaboration.

Infrastructure decisions should prioritize curricular outcomes and student access rather than allegiance to a particular vendor.

15. Career and Public Messaging

Career language should distinguish among creative AI practice, AI-enabled production, intelligent-system design, human-centered prototyping, and technical AI engineering.

Relevant Career and Practice Areas

creative technology and experiential design;
generative and computational media;
AI prototyping and innovation labs;
design technology and human-AI interaction;
interactive installation, performance, and media arts;
technical art and AI-assisted production pipelines;
intelligent games, simulation, training, and education;
computer-vision and sensor-based experiences;
creative tools and workflow design;
UX research, evaluation, accessibility, and responsible AI practice;
digital humanities, cultural institutions, and public-interest technology;
independent practice, entrepreneurship, and consulting;
research assistance and interdisciplinary laboratories; and
graduate study in HCI, digital media, computational arts, information science, AI, or related areas.

Students completing the B.A. route should not be presented automatically as machine-learning engineers. B.S. students seeking technical AI engineering roles will need sufficient mathematics, algorithms, software, and advanced ML depth. All students should be prepared to explain how their IMD abilities in prototyping, interaction, real-time media, critical analysis, and collaborative production complement technical AI knowledge.

16. Implementation Path

Phase 1: Make the Existing Path Visible

Publish an advising guide using current IMDM, ARTT, CMSC, and cross-campus courses.
Identify faculty, labs, and programs currently supporting AI-related work.
Add consistent syllabus language for AI disclosure, provenance, evaluation, and responsible practice.
Track AI-related projects in IMDM290, IMDM390, special topics, research, and Capstone.
Establish regular work-in, critique, and public-discussion sessions.

Phase 2: Pilot the Missing Shared Core

Offer Creative AI Foundations as a recurring special topic open to both tracks.
Establish common model-literacy, data, evaluation, responsible-practice, and portfolio expectations.
Pilot an Intelligent Media Studio through IMDM390, IMDM398, or IMDM498.
Establish instructional access to APIs, local models, storage, and appropriate computing resources.
Collect student work, enrollment, demand, cost, access, and progression data.

Phase 3: Formalize the Specialization

Confirm the credit structure within each degree track.
Approve permanent introductory and advanced courses where evidence supports them.
Establish a faculty lead, coordinating group, or appropriate new position.
Formalize partnerships and elective access across relevant UMD programs.
Define advising, electives, responsible-practice requirements, and demonstration standards.
Publish accurate specialization and career language in program materials.

Phase 4: Sustain and Review

Review approved tools, models, course content, costs, access, and policies annually.
Maintain public critiques, exhibitions, visiting practitioners, and cross-campus engagement.
Track whether projects remain accessible and reproducible after external services change.
Adjust electives and production practices as technologies, institutional offerings, and faculty expertise evolve.

17. Evidence of Success

The specialization should be evaluated using evidence that reflects curricular coherence, student learning, responsible practice, and sustainability:

student demand and enrollment;
progression from introductory experiments to advanced systems;
equitable participation by B.A. and B.S. students;
quality and variety of creative, technical, and critical work;
evidence that projects, datasets, systems, or research questions develop across courses;
student ability to explain model behavior, limitations, and their own contributions;
quality of provenance, evaluation, and responsible-practice documentation;
quality of portfolio case studies and public presentation;
participation in work-in sessions, critiques, exhibitions, research, and cross-campus activities;
internships, employment, independent work, entrepreneurship, graduate study, and adjacent professional outcomes;
affordability and equitable access to tools and infrastructure; and
faculty workload, course availability, technical support, and long-term sustainability.

Success should not be measured by the volume of AI-generated output or by allegiance to the newest commercial tool. It should be visible in the quality of the questions students ask, the systems they build, the judgments they make, and the clarity with which they explain the consequences of their choices.

18. Decisions Requiring Faculty Discussion

Should the public-facing name be AI Specialization, Creative AI and Intelligent Media, or another formulation?
How should the specialization distinguish itself from UMD's Human-Centered AI B.A. and computational AI B.S.?
Which AI concepts and responsible-practice competencies must every student demonstrate regardless of track?
Can a recurring special topic establish the shared foundation before permanent course approval?
What minimum programming, mathematics, and data literacy should be expected of B.A. students?
What creative, critical, and human-centered requirements should be expected of B.S. students?
Should advanced production occur within IMDM390, in a dedicated Intelligent Media Studio, through research, or through multiple approved options?
Which external courses can reliably count toward the specialization, and can IMD students access them?
What computing, storage, API, and technical-support commitments are financially sustainable?
What disclosure, provenance, consent, evaluation, and archiving standards should apply to student work?
What portion of the specialization can fit within existing electives without increasing time to degree?
What faculty expertise is already available, and what gaps require a designated lead, partnership, or future hire?
What constitutes an acceptable culminating demonstration: an AI-focused Capstone, a portfolio of connected work, a research project, or any approved option?
How will the curriculum remain durable as tools, models, regulations, and public expectations change?

Working Conclusion

AI is already latent within IMD. The program has technical foundations, creative coding, digital art, critical theory, collaborative studios, immersive-media facilities, and a public Capstone structure. UMD also offers an expanding AI ecosystem that IMD can join without duplicating.

What is missing is a shared foundation accessible to both tracks, a coherent creative and interactive identity, standardized responsible-practice expectations, a guaranteed advanced production experience, sustainable infrastructure, and coordinated faculty leadership.

The specialization should therefore be understood as an act of connection and differentiation:

Foundation → Creative AI Literacy and Experimentation → Track-Specific Depth → Intelligent Media Production → Portfolio and Capstone Demonstration

This pathway would prepare students not only to work with AI, but to understand, question, shape, and communicate the systems through which AI enters creative and public life.

Source Basis and Companion Documents

This case should be read alongside the IMD Current-State Curriculum Map, the IMD Faculty Syllabus Continuity Guide, and the IMD Game Design Specialization Case Example. Course availability, prerequisites, elective access, staffing, infrastructure, and credit applicability should be confirmed before any formal proposal is developed.