BE OPEN Academy

PART 2. From Process to Trust

The Rise of the AI-Use Log

One practical change in future portfolios may be the introduction of an AI-use log: a short explanation of how AI was used in a project.

For example: AI was used for early moodboard exploration; to test alternative compositions; to generate visual references later manually developed; to summarise user interviews, followed by human review; or to create options that were rejected because they did not fit the ethical or cultural direction of the project.

This kind of documentation helps answer an increasingly important question: what exactly is the student’s contribution?

It also teaches transparency. As AI becomes more present in creative industries, designers will need to explain how images, prototypes and concepts were produced. Content Credentials and provenance technologies are already emerging as ways to provide information about who created a piece of content, when it was produced and which tools or editing processes were involved.

A transparent portfolio says: this is how I worked, this is what the tool did, this is what I did, and this is why the final decision was mine.

Source:
https://contentauthenticity.org/how-it-works

What Admissions Tutors and Employers Need to See

Different schools, studios and employers will evaluate portfolios in different ways. Yet across disciplines, evaluators want to understand potential.

University of the Arts London describes a portfolio as a collection of work that shows how creativity has developed over time, including research, planning, experimentation and even mistakes made along the way. The University for the Creative Arts similarly advises students to document their process, tell a story from research to outcomes, and show unfinished work or failed experiments when they reveal learning.

These points are especially important in an AI-shaped environment. A portfolio that only displays final images may show taste, but not growth; style, but not resilience. A process-led portfolio can reveal curiosity, independence, adaptability, critical thinking, ethical awareness, collaboration and capacity to learn.

Sources:
https://www.arts.ac.uk/study-at-ual/apply/portfolio-advice
https://www.uca.ac.uk/blogs/creating-a-product-design-portfolio-for-university/

The New Portfolio Structure

If the portfolio is becoming more process-led, students can organise each project as a short story rather than a sequence of final images.

A useful structure might include the question, the context, the research, the experiments, the role of AI or digital tools, the decision points, the outcome and the reflection: what was explored, why it mattered, what was tested, what changed, what was made and what remains unresolved.

This structure does not make portfolios less visual. It gives images more meaning.

Failure, Position and Systems Thinking

Many students worry that showing failed experiments will make their work look less professional. In reality, carefully selected failures can make a portfolio stronger if the student explains what did not work, what was learned, and how that learning shaped the next decision.

Portfolios have also often been used to show personal style. Style still matters, but in the AI era it may become easier to imitate. What may matter more is position: what the designer cares about, what values shape their decisions and what futures they want to help create.

The strongest portfolios may also show an ability to think beyond individual objects. Many of today’s challenges are systemic: climate change, inequality, ageing populations, urban density, waste, mobility, digital trust and public health. Design Council’s Systemic Design Framework describes systemic design as a way of acknowledging complexity and interconnectedness.

For students, this means portfolios can show not only the object or image, but the system around it: stakeholder maps, lifecycle thinking, service journeys, material flows, community feedback or unintended consequences.

Source:
https://www.designcouncil.org.uk/resources/systemic-design-framework/

Ethics, Authorship and Responsibility

As AI becomes part of design education, portfolios will also need to address ethical responsibility.

Students should be prepared to explain where images came from, how tools were used, whether data or references were appropriate, and how they considered bias, accessibility, inclusion and environmental impact.

UNESCO’s guidance on generative AI in education and research highlights the need for human-centred, safe, equitable and meaningful use of these technologies. For design students, using AI well is not only a question of technical skill. It is a question of responsibility.

A future portfolio may include short ethical reflections: Were the references culturally sensitive? Were users or communities represented fairly? Did the AI output contain stereotypes? Were accessibility needs considered? Could the design have unintended negative consequences? Was the student transparent about the process?

In the AI era, authorship is no longer just about who made the final image. It is about who made the decisions.

Source:
https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research

Key Takeaways for Design Students

• Do not treat your portfolio as a gallery of final images only.
  · Show the journey from research to outcome.
  · Document experiments, prototypes, feedback and failures.
  · Explain where and why AI tools were used.
  · Make your own contribution clear.
  · Use short annotations to reveal decision-making.
  · Show how your ideas changed over time.
  · Include ethical and contextual reflection where relevant.
  · Demonstrate curiosity, adaptability and critical thinking.
  · Remember that process is not separate from design. It is design.

Looking Ahead

Generative AI is changing the way creative work is produced, but it is also changing the way creative work must be explained.

For design students, the challenge is no longer simply to produce impressive outcomes. It is to show the intelligence behind those outcomes.

The strongest portfolios will reveal not only skill, but judgment. Not only style, but position. Not only what was made, but why it matters.

In a world where images can be generated quickly, the designer’s process becomes a source of trust. And the portfolio becomes something more than a presentation of work. It becomes a portrait of a mind at work.

PART 1. From Finished Work to Visible Thinking

As generative AI makes polished images easier to produce, design students need to rethink how they present their work. The strongest portfolios of the future will not simply showcase beautiful outcomes. They will reveal how designers think, research, question, experiment, collaborate and make responsible creative decisions.

For generations, portfolios have helped students enter education, employment and professional life. They have often been judged by final outcomes: the refined image, elegant product, strong campaign, impressive rendering or memorable visual identity.

But generative AI is changing this logic. Today, AI tools can produce images, layouts, product concepts, moodboards, type treatments, 3D directions and storyboards with extraordinary speed. This raises a difficult question: if polished outputs become easier to generate, what will make a designer’s portfolio stand out?

The answer is increasingly clear. The future design portfolio will need to show more than what a student made. It will need to show how they thought.

From Portfolio as Gallery to Portfolio as Evidence

A portfolio has often been treated as a gallery: a carefully arranged selection of finished work. That model is not disappearing. Final work still matters. Craft, clarity, material intelligence and visual confidence remain important parts of design.

But the gallery model is no longer enough. In the age of AI, a finished image can hide too much. It may not reveal whether the student understood the problem, developed the idea independently, used AI responsibly, tested alternatives, considered users or made thoughtful decisions along the way.

A stronger portfolio acts less like a gallery and more like evidence. It shows the journey behind the outcome: the brief, research, constraints, iterations, failures, prototypes and reasoning that shaped the final result.

A good portfolio should answer not only “What did you make?” but also: what problem were you trying to solve, why did it matter, what changed during the process, and what did you learn?

In this sense, the portfolio becomes a record of design intelligence.

BE OPEN Insight

When images become easier to generate, thinking becomes harder to fake.

The future portfolio will not be defined only by the beauty of its final outcomes. It will be defined by the clarity of the process behind them. For young designers, the ability to explain how an idea developed, why decisions were made and what values guided the work may become one of the most important signs of creative maturity.

Why Final Output Is Losing Its Monopoly

For a long time, the final output carried most of the communicative weight in a portfolio. A strong poster, object, interface or installation could suggest that the student had the skills required to produce it.

That assumption is becoming weaker. Generative tools can produce a high level of visual finish even when the underlying concept is shallow. A student may create a striking image without a meaningful brief, original research, user understanding or contextual testing.

The viewer needs to know what the student contributed. Did they define the direction? Curate the references? Write the prompts? Combine AI-generated material with drawing, modelling, photography or physical experimentation? Reject weaker options?

The value of the portfolio shifts from surface to authorship. AI may help produce options. The designer must show judgment.

Process as a Creative Skill

Process is sometimes misunderstood as something messy that happens before the “real” work begins. In fact, process is where much of design value is created.

It is where students learn to observe, question and reframe; discover that the first idea is rarely the best one; test materials, encounter constraints, receive criticism and change direction.

Showing process does not mean including every sketch, screenshot or unfinished experiment. A portfolio still needs editing. But it should reveal enough of the journey to make the student’s thinking visible: research notes, references, user observations, sketchbook pages, material tests, prototypes, failed experiments, prompt variations, AI outputs, feedback and short reflections on what changed and why.

The goal is not to show chaos. The goal is to show how an idea became stronger.

Case Study: Adobe and Parsons Explore Creative AI in Education

A collaboration between Adobe and Parsons School of Design shows how design education is beginning to rethink creative practice in the age of AI.

Students worked with tools such as Adobe Firefly, Photoshop, Lightroom and Premiere, and explored Content Credentials through the Content Authenticity Initiative. AI was not treated simply as a shortcut for final images. It was used for ideation, exploration, visual development and reflection.

For portfolio thinking, the lesson is significant. If students use AI, they should explain how it helped generate directions, how outputs were selected or rejected, and where creative control remained human. This transparency can make AI-assisted work more credible, not less.

Source:
https://adobe.design/ideas/creativity-in-the-age-of-ai

PART 2. From Judgment to Responsibility

Critical Thinking Becomes Essential

As AI-generated content becomes more sophisticated, critical thinking becomes essential. AI can produce convincing solutions that appear innovative while containing hidden flaws, biases, inaccuracies or unintended consequences.

Designers must learn to ask: Does this solve the real problem? Who benefits? Who might be excluded? What assumptions are embedded in the output? What should remain under human control?

Rather than accepting AI-generated ideas at face value, designers must become skilled evaluators and editors.

Systems Thinking for Complex Challenges

The major challenges of the twenty-first century, from climate change and urbanisation to public health and waste management, are interconnected.

That is why systems thinking is becoming central to design education. AI can help analyse information and identify patterns, but understanding complex systems still requires human judgment, interdisciplinary thinking and sensitivity to context.

Case Study: Kia, Autodesk and AI-Assisted Concepts

A research collaboration between Kia and Autodesk focused on generative AI tools for wheel concept design. Rather than automating the designer’s role, the system helped teams move faster from inspiration to concept generation. Designers still defined the creative direction, selected promising outcomes and refined final proposals.

For students, the lesson is clear: future value may lie not in producing the first idea, but in identifying the most meaningful one among many alternatives.

Source:
https://www.autodesk.com/autodesk-university/class/Streamline-Conceptual-Design-with-Generative-AI-A-Research-Collaboration-with-Kia-2024

Ethics Is No Longer Optional

The integration of AI into design raises ethical questions. Who owns AI-generated content? How should designers address bias in training data? How can transparency and accountability be maintained?

Future designers will need a strong understanding of ethics, not only in relation to AI but also regarding sustainability, accessibility, privacy, inclusion and social responsibility.

UNESCO’s guidance on generative AI in education and research emphasises human-centred, safe, equitable and meaningful use. For design schools, this affects assignments, studio critique, assessment, authorship, data use and the way students explain their creative process.

Source:
https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research

Human Skills Remain Irreplaceable

Ironically, the growth of artificial intelligence is making distinctly human abilities more valuable. Empathy, communication, storytelling and creativity remain essential for understanding people, collaborating across disciplines and imagining futures that do not yet exist.

Case Study: The Elbo Chair and Human Judgment

One well-known example of generative design is Autodesk’s experimental Elbo Chair project. Designers established objectives and constraints related to structure, materials and performance; the software generated hundreds of possible solutions.

Yet the final design did not emerge automatically from the algorithm. Human designers still needed to evaluate results, balance aesthetics with functionality, and select the most promising outcome.

Sources:
https://www.wired.com/2016/10/elbo-chair-autodesk-algorithm/
https://research.autodesk.com/projects/dreamcatcher/
https://www.autodesk.com/customer-stories/elbo-chair

How Design Education Is Evolving

Around the world, design schools are adapting their programmes. Students are increasingly encouraged to work across disciplines, combining design with technology, sustainability, entrepreneurship, social sciences and policy.

AI tools are being introduced not as replacements for design education but as instruments within the creative toolkit. The emphasis is shifting from mastering specific software towards adaptable thinking, problem-solving and lifelong learning.

Recent educator resources highlight the need to integrate AI into the full design cycle: research, ideation, experimentation, iteration and communication.

Sources:
https://www.qaa.ac.uk/membership/communities/art-design-and-art-history/using-ai-in-learning-and-teaching-in-art-and-design
https://altc.alt.ac.uk/blog/2026/03/values-led-generative-ai-in-design-education-a-toolkit-for-confident-critical-practice/

What This Means for Student Portfolios

If AI can produce polished images quickly, the student portfolio will need to show more than final outputs. It will need to show process: research, brief definition, prompt strategy, rejected directions, material experiments, feedback and the reasoning behind final decisions.

In an AI-assisted environment, the strongest portfolio is the one that reveals how the designer thinks, learns, edits, tests and takes responsibility for creative choices.

In an Adobe and Parsons School of Design collaboration, students used generative tools in early creative stages such as ideation, concepting and visual exploration, while remaining deliberate about where they wanted to preserve creative control and authorship.

Source:
https://adobe.design/ideas/creativity-in-the-age-of-ai

Designing the Future Together

Artificial intelligence is transforming the creative landscape, but it is not eliminating the need for designers.

The designers of the future will be strategists, facilitators, researchers, storytellers and systems thinkers. They will use AI to accelerate exploration while contributing uniquely human insight, responsibility and imagination.

For design students, the challenge is not simply learning how to use AI tools. It is learning how to work alongside them.

Key Takeaways for Design Students

• Learn AI tools, but do not rely on them exclusively.
  · Develop AI literacy, not only prompt-writing skills.
  · Strengthen critical thinking and systems thinking.
  · Build communication, collaboration and storytelling abilities.
  · Understand the ethical implications of emerging technologies.
  · Focus on solving meaningful problems rather than simply producing outputs.
  · Document how and why AI was used.
  · Treat AI as a collaborator, not as a replacement for human creativity.
  · Remember that empathy, imagination and judgment remain at the heart of great design.

Looking Ahead

The future of design will not be defined by technology alone. It will be shaped by the choices people make about how technology is used, whom it serves and what values guide innovation.

Artificial intelligence may change the way designers work. But the ability to ask meaningful questions, understand human needs and envision better futures remains uniquely human.