Empowering Future Designers: Embedding Responsible Entrepreneurship with Ethical and Sustainable Thinking in Graphic Design Education
Bibliographic record
Abstract
To integrate sustainability and the responsibilities of future graphic designers into design education, this paper explores an innovative approach rooted in Education through Responsible Entrepreneurship. This pedagogical framework fosters an entrepreneurial mindset, empowering students to initiate their own projects while evaluating their broader impact. As part of a doctoral research, the study focused on designing a five-day creative retreat for graphic design students at Cégep de Sainte-Foy in Québec City, Canada. The retreat combined theoretical modules with practical individual and team workshops, inspiring students to explore concepts of responsible entrepreneurship alongside ethical and sustainable decision-making. Through a mixed-method research methodology and the application of Learning Experience Design (LXD), the study highlights the potential of developing a learning experience that empowers students by enhancing their agency and incorporating social, environmental, and economic considerations into their work. The discussion highlights the importance of integrating sustainability-oriented content, learner-centered strategies, co-designed learning experiences, and pedagogical innovation into design education. Drawing from this approach, the paper presents concrete, evidence-based principles and methods to foster graphic design students’ agency and sense of responsibility.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".