Generative AI in Design Thinking Pedagogy: Enhancing Creativity, Critical Thinking, and Ethical Reasoning in Higher Education

Synthesis: Mixed-methods analysis of 112 student reflections from a 12-week course examining how Generative AI tools (ChatGPT, DALL·E) operate across Design Thinking's five stages. Four themes emerged: Perceived Benefits (enhanced Creativity and Accessibility), Ethical Concerns (bias and authorship ambiguity), Hesitance & Acceptance (evolution from skepticism to strategic adoption), and Critical Validation (development of epistemic vigilance). Sentiment analysis showed 86% positive responses, while ethical concerns generated significant negative sentiment (62%). The authors conclude that Generative AI, when pedagogically scaffolded, augments rather than replaces human judgment — students evolved from passive users to critical evaluators. Published in JUTLP, DOI https://doi.org/10.53761/tjse2f36.

Key Findings

• Four themes across the design process. Thematic analysis of 112 student reflections surfaced four central themes — Perceived Benefits, Ethical Concerns, Hesitance & Acceptance, and Critical Validation — each mapping to distinct dimensions of Generative AI integration within Design Thinking Pedagogies and Teaching Strategies.
• Perceived Benefits (n=94) with 86% positive sentiment. Students reported enhanced Creativity, broader ideation, and greater accessibility, positioning GenAI as a catalyst for divergent thinking during the ideation stage rather than a simple answer-provider.
• Ethical Concerns (n=66, 59%) drove 62% negative sentiment. Central issues were algorithmic bias (e.g., DALL·E reproducing gender stereotypes) and authorship/ownership ambiguity — but the negative sentiment reflected active ethical engagement, not rejection of the technology.
• Hesitance & Acceptance (n=56, 50%). Students moved from skepticism (seeing AI as "cheating") toward conditional, strategic adoption, reaching 72% positive orientation by course end — a shift that required instructor-led Scaffolding and peer dialogue.
• Critical Validation. Students developed epistemic vigilance, learning to verify outputs, detect bias, and validate sources — evolving from passive users into critical evaluators of AI-generated content.
• Augment, don't replace. When pedagogically scaffolded within Design Thinking, Generative AI augments rather than replaces human judgment, supporting Creativity while preserving learner Learner Agency.

Study Design & Method

The study was conducted across a 12-week undergraduate Design Thinking course at a metropolitan Australian university, with 112 students drawn from business (60%), education (19%), law (12%), and interdisciplinary programs (9%). None had prior formal training in Design Thinking or Generative AI, providing a consistent baseline. GenAI tools were embedded at each of the five design stages: ChatGPT for empathise (persona Simulation), AI summarization for define, DALL·E and ChatGPT for ideate, DALL·E for prototype, and AI analytics for test.

The study adopted a mixed-methods paradigm guided by a Constructivism epistemology grounded in Experiential Learning, Vygotsky's sociocultural theory, and Dewey's pragmatism. Qualitative analysis used Braun and Clarke's six-phase thematic analysis framework with NVivo 14, achieving high intercoder reliability (Cohen's κ = 0.82 on a 20% subsample). Sentiment analysis was implemented via a fine-tuned DistilBERT model (validated at 89% accuracy, F1 = 0.85), cross-checked with manual coding (88% concordance) and the VADER lexicon.

Implications for AI in Education

• Scaffolded integration beats prohibition or laissez-faire use. Acceptance of Generative AI emerged not from exposure alone but from intentional guidance, critical framing, and structured team-based learning.
• AI Literacy must be a multidimensional competence. The authors challenge traditional cognitive taxonomies, calling for AI Literacy encompassing Creativity, Ethics, and critical reasoning — not just technical proficiency.
• Teach students to interrogate outputs. Critical Thinking in GenAI-mediated environments now requires output verification, task stewardship, and bias detection rather than passive acceptance.
• Address ethical blind spots explicitly. Algorithmic bias, authorship ambiguity, and data provenance should be introduced alongside tool use to prevent blind spots, echoing broader Equity concerns.
• Faculty development matters. Educators need support to design GenAI-integrated Higher Education learning environments that promote agency rather than automation, countering Cognitive Offloading and automation bias.
• Reframe the human-AI relationship. GenAI functions best as a co-creator and collaborative partner within Human AI Collaboration, preserving human judgment at the center of the process.

Connected Concepts

• Design Thinking
• Generative AI
• Creativity
• Critical Thinking
• Ethics
• Scaffolding
• Higher Education
• AI Literacy

Connected Articles

• AI-Enhanced Problem-Based Learning Framework: Integrating ChatGPT as Adaptive Scaffolding to Improve Critical Thinking and Personalized Learning
• Thoughtless Use of Generative Artificial Intelligence and College Students' Self-Directed Learning: A Multi-Group SEM Analysis of Gender Differences
• The LEARN Framework for Responsible Use of Generative AI in Education: A Neuroscience-Informed Model for Problem-Based Learning
• Promoting Critical Thinking in Biological Sciences in the Era of Artificial Intelligence: The Role of Higher Education
• Factors influencing university students' intention to use and reliance on generative artificial intelligence

Citation

Rana, V., Verhoeven, B., & Sharma, M. (2025). Generative AI in design thinking pedagogy: Enhancing creativity, critical thinking, and ethical reasoning in higher education. Journal of University Teaching and Learning Practice, 22(4).