Notes.

The conclusion of the preceding note (qualitative-quantitative-design-research) -- that the exploratory character of design is epistemologically consonant with qualitative research -- occupies a mainstream position in design research since Frayling (1993), Cross (2006), and Dorst (2011). A bibliometric analysis of fifteen years of Design Studies (Chai & Xiao 2012) corroborates the predominance of qualitative methods. Three countervailing tensions nevertheless persist: the quantitative orientation of HCI (the experimental norms of CHI), the quantitative tradition of evidence-based design, and the call by Gaver (2012) and Koskinen et al. (2011) to transcend the qualitative-quantitative dichotomy altogether. A research agenda centered on 'how evaluation affects people's willingness to try again' and 'designing conditions that enable a second attempt' harbors a methodological tension: qualitative methods are needed to describe those conditions, yet some form of empirical verification is required to assess whether altered conditions produce the intended effects. This tension cannot be bridged by mixed-methods pragmatism alone; a mechanism-oriented epistemology such as critical realism (Bhaskar 1975) emerges as a candidate framework.

An overview of the epistemological foundations, strengths, and limitations of qualitative and quantitative research, along with guidelines for methodological choice in design research. Design is an exploratory activity that envisions and realizes what does not yet exist (Simon 1969; Schon 1983; Cross 2006). Because of this character, qualitative methods (ethnography, protocol analysis, case studies, research through design) are called for at the exploratory stage, while quantitative methods (usability testing, surveys, experiments) are needed at the evaluative stage. Mixed methods research provides a framework that methodologically secures the continuity between these two stages within a single study.

An integrated summary of 'AI and design' developments over the past seven days, collected in three tiers: T1v vendor primary sources, T2 public institutions and research, and T3 expert opinion. This was a week in which the design shift toward 'treating images as collections of objects,' shown separately by Adobe and Wroblewski, and the regulatory move by Korea's intellectual property authority to require records of human contribution in design applications lined up as the technical and institutional faces of the same movement: decomposing artifacts into elements and giving them units.

A monthly scholarly watch collecting 30 peer-reviewed papers and preprints on 'AI and design' from June through early July 2026. ACM DIS 2026 alone accounts for 13 papers addressing the alignment of generative AI with design intent, while two empirical studies from the same period report that generative AI narrows divergent thinking and lowers functional success rates. This conflict is not resolved within the present corpus.

An integrated summary collecting the past 7 days of 'AI and design' developments in three tiers: T1v vendor primary sources, T2 public institutions and surveys, and T3 expert commentary. This week's three observation points: the commoditization of execution seen in GPT-5.6's same-week integration into tools, experts converging on judgment, taste, and critique as the scarce resources, and regulation moving toward mandatory disclosure of AI-generated content.

Analyzes human competency measurement premised on AI use, drawing on 28 academic sources and 16 industry sources. Three structural findings: (1) AI compresses the productivity distribution (43% improvement for low-skill workers vs 17% for high-skill workers, Dell'Acqua 2026 N=758), yet the higher the task complexity, the more existing expertise is amplified; (2) articulation ability and proactiveness indirectly determine the effectiveness of AI use (Power Users experiment 68% more frequently and persist 30% more often after failure, Microsoft 2024 N=31,000); (3) existing AI literacy scales show divergence between self-report and objective assessment (Zhang et al. 2026). What should be measured is not 'whether one can use AI' but 'which cognitive functions are amplified through collaboration with AI.'

A cross-sectional analysis of the phenomenon in which the designer's role shifts from maker to editor/curator, drawing on 19 academic sources and 15 industry sources. Three structural findings: (1) the transition is empirically confirmed, with 71% spending more time on evaluation/curation than original production (Rivera & Russi 2026, 217 practitioners across 43 countries); (2) new judgment typologies have emerged alongside the transition (agency allocation judgment, trustworthiness judgment); (3) behind the efficiency narrative, 'AI management labor' has surfaced as a new form of cognitive burden. Industry data reveal a structural divergence between 90% adoption and only 10% approval.

Analyzes the structural transformation whereby design systems (component libraries, design tokens, structured specifications) function as foundational infrastructure for AI agents, drawing on 27 academic papers and first-party information from 5 vendors. Provision of formal specifications reduces agent navigation by 33-44% and achieves 100% accuracy (Jin 2026); leveraging Figma JSON metadata outperforms pixel-only conversion (Gui 2026, ICLR); 5 companies simultaneously implement design systems as AI context layers. The meaning of investing in design systems has shifted from 'maintaining consistency' to 'determining the accuracy ceiling of AI agents.'

Analyzes AI-era design education curriculum reform drawing on 22 academic publications and information from 8 educational institutions plus public bodies. Three structural findings: (1) research concentrates on creativity development (35.9%), assessment (27.6%), and curriculum design (22.4%), with 66.7% not reporting outcomes (Musiienko 2026); (2) four newly required skills are identified (agency, domain knowledge, imagination, and aesthetic judgment); (3) no update to NASAD's AI standards has been confirmed. Reflective practice is being reconceived as 'learning by co-doing.'

Analyzes the impact of EU AI Act Article 50 (transparency obligations), effective August 2, 2026, on design practice through a corpus of 30 academic publications. Three structural problems are identified: (1) the scope of disclosure obligations is ambiguous, leaving the boundary between design editing and regulated manipulation undefined; (2) watermark and labeling implementation rates fall far short of regulatory requirements (38% / 18%); (3) greater disclosure granularity paradoxically erodes trust rather than improving user judgment. Designers confront a 'disclosure dilemma' — disclose AI use and lose client trust, or conceal it and incur legal risk — while gaps in copyright protection undermine the basis for billing.