{"id":172470,"date":"2026-02-20T15:09:23","date_gmt":"2026-02-20T21:09:23","guid":{"rendered":"https:\/\/tecscience.tec.mx\/en\/?post_type=sciencecommunication&p=172470"},"modified":"2026-02-23T16:28:26","modified_gmt":"2026-02-23T22:28:26","slug":"ai-art-and-design","status":"publish","type":"sciencecommunication","link":"https:\/\/tecscience.tec.mx\/en\/science-communication\/ai-art-and-design\/","title":{"rendered":"From Sketch to Prompt: Learning Design in the Age of Generative AI"},"content":{"rendered":"\n

By Juan Carlos Rojas<\/a><\/strong><\/p>\n\n\n\n

Design education is at a turning point. The industry demands faster, more visual processes with strong technological application, while many classrooms struggle to keep pace or remain anchored in traditional dynamics.<\/p>\n\n\n\n

Research suggests that design training must systematically connect projects, curricula, and emerging technologies. At the same time, the industry increasingly requires designers who can work within digital and data-driven workflows, fueling the rise of \u201ctechnology-driven\u201d design processes in both education and professional practice.<\/p>\n\n\n\n

In this context, learning becomes more meaningful when it connects directly to present and future projects\u2014and when tools allow students to communicate results clearly and quickly.<\/p>\n\n\n\n

Yet educators need gradual pedagogical pathways to introduce new methods and technologies without sacrificing conceptual depth. The question is no longer whether AI will be part of a designer\u2019s professional reality, but how to prepare students to coexist creatively with it.<\/p>\n\n\n\n

This study addresses that agenda by exploring the use of generative AI in design education. For this case, we integrated a pedagogical exercise that combined a traditional creative dynamic with guided use of AI-powered image-generation tools.<\/p>\n\n\n\n

Creativity with AI<\/h2>\n\n\n\n

The exercise took place during the 2023\u20132024 academic year in two senior-level product design courses, involving 13 teams of three to four students each. It unfolded in two complementary stages.<\/p>\n\n\n\n

1. Creating a fictional design firm.<\/strong>
Over two weeks, each team developed the identity of a design studio for their final project: overall positioning (approach, target audience), mission, vision, and values, along with branding elements such as logo, color palette, and typography.<\/p>\n\n\n\n

This type of activity links form, style, visual language, and brand strategy. Teams produced rich, visually grounded descriptions that later served as input for the second phase of the exercise.<\/p>\n\n\n\n

2. Designing a first product with GenAI.<\/strong>
In a 2\u20133 hour session, teams revisited their firm\u2019s identity and were tasked with designing their first product: either a lamp or a chair.<\/p>\n\n\n\n

At this stage, generative image tools such as DALL\u00b7E, Midjourney, and similar platforms were introduced, following models of AI integration in creative education. To structure their prompts and generate a visual rendering, students used a taxonomy of text modifiers proposed by Oppenlaender, which includes elements such as subject, style, quality parameters, and specific instructions.<\/p>\n\n\n\n

Students had to translate their brand\u2019s mission, vision, values, and visual traits into precise textual descriptions that the AI tool could interpret.<\/p>\n\n\n\n

GenAI as an Idea Accelerator<\/h2>\n\n\n\n

The exercise did not aim to replace the human creative process, but to position generative AI as an assisted tool. During the session, each team developed an initial prompt based on its brand identity, generated a first product image, and evaluated its coherence with the brand\u2019s values, style, and target audience.<\/p>\n\n\n\n

They iterated the prompt four or five times, refining it with each round until they achieved a result they considered satisfactory.<\/p>\n\n\n\n

This cycle of repetition, reflection, and correction aligns with other studies showing that AI can expand exploration, understanding, and idea generation when critically integrated into educational settings.<\/p>\n\n\n\n

Although this was an initial, primarily qualitative study, clear trends emerged. In every team, the process culminated in a final product image consistent with the students\u2019 original authorship. Some teams iterated more than five times to reach a satisfying outcome.<\/p>\n\n\n\n

As the designs evolved, brand identity and product design became increasingly coherent\u2014in color, materials, formal language, and intended user.<\/strong> AI reduced the time between idea and visualization, accelerating the traditional sketch\u2013render\u2013presentation workflow described in design education literature.<\/p>\n\n\n\n

These findings align with broader research reporting that AI platforms can enhance inspiration, reflection, and exploration in creative processes.<\/p>\n\n\n\n

On an emotional and motivational level, the results mirror experiences from other intensive courses where technology boosts perceived creativity and engagement. Students reported feeling more confident when they saw their ideas materialize quickly\u2014and less afraid of making mistakes, since errors became opportunities for refinement rather than endpoints.<\/p>\n\n\n\n

Less Creativity? Quite the Opposite<\/h2>\n\n\n\n

A common concern in creative disciplines is that AI will \u201cdo the work for the student.\u201d Both the classroom experience and existing research suggest a different outcome: when thoughtfully designed, AI does not replace creativity. It amplifies key processes such as experimentation, comparison of alternatives, and informed decision-making.<\/p>\n\n\n\n

In this pedagogical exercise, the quality of the outcome depended directly on the conceptual clarity with which each team had built its firm\u2019s identity. The most effective prompts were those that integrated brand concepts, user attributes, and formal references\u2014echoing findings from other studies on AI-assisted inspiration.<\/p>\n\n\n\n

The tool functioned as a mirror of the team\u2019s thinking: vague inputs produced vague images. In that sense, AI becomes an accelerator of thought, not a substitute for the designer. Creativity remains human; the tool simply extends its reach.<\/p>\n\n\n\n

The results reinforce what many voices in design and education have been arguing: the future of design will necessarily be hybrid\u2014human plus technological. The key question is not whether AI will be present, but how to prepare designers to co-create with it in ways that are critical, ethical, and imaginative.<\/p>\n\n\n\n

.<\/p>\n\n\n\n

This article is based on research the author published in  <\/em>\u201cA Pedagogical Exercise of Integrating AI Image Generator Tool: A Pilot Experience in Design Product Education\u201d<\/em><\/a><\/em>, included in the Proceedings of the American Society of Mechanical Engineers 2024 International Mechanical Engineering Congress and Exposition.<\/p>\n\n\n\n


Referencias  <\/h5>\n\n\n\n
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    .
    <\/p>\n\n\n\n

    Author<\/h5>\n\n\n\n

    Juan Carlos Rojas<\/strong>. Ph.D. in Design, Manufacturing, and Industrial Project Management. He is a research professor at the School of Architecture, Art and Design (EAAD) at Tecnol\u00f3gico de Monterrey and a researcher at the Institute for the Future of Education. His academic and research interests include leadership in product design; emotional and affective design research through neurotechnologies; and educational innovation in design.<\/p>\n\n\n\n

    <\/p>\n","protected":false},"excerpt":{"rendered":"

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