Student Perspectives: Hopes and Concerns in the AI Design Classroom

To gauge student perceptions of AI, two surveys were conducted for the CT380 AI-Assisted Design course: one before the semester began and an exit survey at the end. Analysis of these responses revealed notable shifts in student understanding, concerns, and expectations.

1. Improved Understanding of AI Tools Students demonstrated a significant increase in their self-rated understanding of how AI tools work. On average, their understanding score rose from 3.0 to 4.3 by the end of the semester. For instance, one student's rating went from "1" (low) to "5" (high).

2. Evolving Perceptions of AI’s Impact on Creativity While a majority consistently believed AI could enhance creativity, the exit survey provided more nuanced and critical reflections.

• Enhancement: Students noted that AI assists by helping them "think beyond conventional ideas" and "boost the quality" of their output. They described AI as a "support rather than a solution" and appreciated how it can "articulate ideas faster" and "save time". Examples from pre-class surveys included desires for AI to "speed up some processes to make it efficient" and "Help source inspiration better".

• Limitation: Concerns became more defined, with some students fearing that over-reliance on AI might "replace creativity" or reduce the emotional depth and craftsmanship of their work. One student explicitly stated, AI "takes away from the emotion, hard work, and skills artists spent years perfecting". Another emphasized the importance of setting boundaries, saying AI "can definitely limit creativity if you over-rely on it…you have to preserve your creative flow".

3. Specific Concerns About AI in Art and Design Compared to the pre-class survey, exit responses showed more specific and thoughtful concerns.

• Over-Reliance: This concern was present from the start and became more prominent, with students emphasizing that AI could "take away personal thinking" and raising questions about "how much of the job we are giving to AI".

• Ethical Issues and Copyright: These were persistent concerns, including AI’s tendency to "copy style and work" and uncertainty over whether AI-generated concepts might be unintentionally plagiarized. Students called for "protections that assure artists their work won't be taken without permission" and expressed fears about AI lowering the barrier to entry, potentially disadvantaging trained creatives.

• Environmental Impact: A notable new concern that emerged was AI’s environmental footprint, with students asking about "AI’s energy consumption" and "water usage" and the need for transparency. This was also present in pre-class concerns.

• Other Concerns: Students also mentioned the oversaturation of certain visual trends, the publishing of low-quality AI-generated work, and fears that innovation might slow due to homogenization in design aesthetics.

4. Desired Improvements for AI Tools By the end of the course, student suggestions for improving AI tools became more sophisticated. Requests included "more accurate image generation," "easier partial editing," and better contextual understanding of prompts. Some wished generative AI could "match the vibe and aesthetic of original pieces" and better integrate with design software like Adobe. Students also desired AI tools to be more accessible and for platforms to provide free learning resources. Several students hoped AI would better "understand the feelings and ideas" behind creative work, acting more as a collaborator than a replacement.

5. The Value of Learning AI in School Across both surveys, students overwhelmingly agreed that learning about AI is essential. One student suggested it should be a "foundation course for all students at FIT and faculty". The exit survey also revealed that most students had independently explored additional AI tools not covered in class, demonstrating increased initiative and engagement. The comparative analysis shows that the CT380 course significantly improved student understanding of AI tools while fostering a more nuanced, critical, and reflective mindset, preparing them to navigate the evolving landscape with confidence and responsibility.

Cultivating Creative Leadership: The Future Pedagogical Framework

To prepare the next generation of designers for an age increasingly shaped by AI, design education must prioritize three core pedagogical focus areas that represent a critical intersection of human creativity, strategic thinking, and ethical responsibility. This approach aims to cultivate judgment, adaptability, and creativity rather than just tool mastery, empowering designers to lead in a hybrid human-machine future.

1. Problem Framing This is the essential first step in any design process. It requires empathy and active listening to deeply understand the context, values, and human motivations behind a design challenge. Instead of jumping to superficial solutions, students must learn to ask the right questions, identify root causes, and define meaningful opportunities. In a world where AI can generate endless visual iterations, the ability to frame the right problem becomes a designer’s most strategic skill. This human-centered approach ensures that design delivers solutions that are relevant, resonant, and socially responsible, grounded in real human needs rather than just appearances.

2. Research-Driven Innovation Building on Problem Framing, this pillar equips students with the tools to explore, test, and validate their ideas. It involves analytical thinking, systems thinking, and curiosity – vital skills for interpreting data, mapping user journeys, identifying patterns, and discovering unexpected insights. While AI can assist in synthesizing data or generating ideas, it is the human designer who must critically interpret findings, recognize nuances, and connect disparate dots to generate new value. Research, in this context, becomes a driver of innovation that ensures originality, strategic alignment, and user relevance, firmly anchoring design in problem-solving.

3. Art Direction with Final Execution This is the stage where the designer’s unique voice, vision, and judgment come into full play. While generative AI can assist in producing visual concepts, it often lacks the intuition, cultural sensitivity, and emotional intelligence needed to make final creative decisions. This phase demands not just visual literacy but also the ability to assess tone, clarity, context, and user impact. Students must learn how to guide and critique AI-generated outcomes, refine and curate them thoughtfully, and elevate them into finished pieces that are aesthetically refined, conceptually rich, and ethically sound. Crucially, this stage moves design beyond purely aesthetic concerns to ensure that every creative decision contributes to meaningful, effective solutions that address real problems and user needs.

By centering education around these three pillars, design moves away from a narrow focus on aesthetics alone, embracing a holistic, solution-oriented practice. This approach not only enhances the value of design work but also empowers future designers to collaborate with AI tools thoughtfully, leveraging technology to create purposeful and impactful outcomes. The role of design educators is to proactively prepare students to harness AI with purpose, integrity, and human insight, ensuring their value lies in directing technology, not just mastering it.