Toward Ethical AI Integration in Indonesian Design Education: Lessons from Global Practices

Muhammad Hajid An Nur, Putri Kholida, Susi Susyanti, Maria Veronika Br Halawa, Candra Pray

Abstract


Abstract: The rapid advancement of Generative AI is reshaping higher education and presents both opportunities and risks for design education, which depends on creativity, originality, and practice-based learning. This study maps and analyses institutional policies and ethical guidelines on AI use in design programmes at leading universities to answer: (1) How are AI policies applied in design education? (2) What institutional typologies—permissive, conditional, restrictive—exist? and (3) How can global practices inform Indonesian policy? Using mixed-methods document analysis guided by a 5W1H framework and a policy-typology classification, we combined systematic text chunking with an iterative, human-in-the-loop ChatGPT workflow for seed-code generation, batch coding, and preliminary synthesis; all model outputs were archived and human-validated (see Appendix A).

Preliminary synthesis of campus summaries identifies four recurrent themes—ethics/academic integrity, implementation controls (approved tools, vendor security, data restrictions), pedagogy (AI literacy, assessment redesign, studio guidance), and governance (disclosure, sanctions). Institutions fall along a continuum: design/art schools tend toward permissive, experimentation-friendly approaches with faculty discretion and disclosure requirements, while research-intensive institutions favor restrictive, legal/contractual safeguards for research and publication.

For Indonesia, we recommend a three-tier policy architecture: (i) permissive studio experimentation with mandatory process documentation; (ii) conditional rules and standardized disclosure for assessed work; and (iii) restrictive controls for research/publication and high-risk data—supported by institutional tool provisioning, equity measures, and staff training. Limitations: document-based analysis and the non-deterministic nature of LLM assistance; human review underpins reported findings.

Keywords: AI in education; ethical policy; design education; policy analysis; Generative AI.

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References


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ISSN: 2598-0653