Generative Artificial Intelligence in Graphic Design Education: A Student Perspective
Bibliographic record
Abstract
Generative Artificial Intelligence (GenAI) is re-defining the way higher education design is taught and learned. The explosive growth of GenAI in design practice demands that design educators ensure students are prepared to enter the design profession with the knowledge and experience of using GenAI. To facilitate GenAI’s introduction in a project-based context, it is suggested that design educators use critical engagement as a starting point to assure students understand the strengths and weakness of GenAI in the creative design process. There is little guidance on how to systematically integrate GenAI in design studio practice while maintaining a critical perspective of the ethical issues it has engendered. This research explores student attitudes toward GenAI, frequency of its use, and student perception of its impact on their future design careers. A survey of a representative cohort of graphic design students (n = 17) reveals a pragmatic acceptance that GenAI will change how design is practiced and a concurrent willingness to learn more on how to use it effectively and ethically. The survey validates the need for design educators to engage and guide students critically in their understanding and use of GenAI within studio and professional practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".