Shuichiro Ogawa

Background

Background and skills

After working as a teacher and an engineer, I built new businesses and generative AI products at product companies and consulting firms.

Skills

Design

User and stakeholder research / Value discovery and definition / Digital product design

Business transformation

Business strategy and commercialisation / Hypothesis testing / Outcome metrics

Data utilization

Generative AI and LLMs / Statistics and visualisation / Data platforms

Technology

Cloud / Serverless and containers / Front-end

Skill names map to the common skill list in the Digital Skill Standard ver.2.0 (METI and IPA).

Track record

Work

Across finance, education and healthcare, I have covered the stages from planning through to operation.

5 selected projects, newest first

• UX redesign of a financial institution's BtoC contract process

  2025–2026

  I led the redesign of the contract process, from the three-to-five-year vision through per-touchpoint screen design.

  Quantitative outcomes are confidential.

  • Planning
  • Requirements definition
  • Design
  • Deployment and operation

• Generative AI in education R&D

  2023–2025

  I tested, alone, the conditions under which generative AI works in education, from the technology survey through impact measurement.

  Quantitative outcomes are confidential. The published portion appeared in a peer-reviewed paper (STEM Education Research Vol. 7).

  • Research
  • Proof of concept
  • Design
  • Implementation
  • Measurement

• Behavioral design R&D for learning retention

  2021–2022

  I researched what keeps learners going, framed as behavioral design, and built a system that uses generative AI (digital human technology) to propose individual interventions.

  Quantitative outcomes are confidential.

  • Design
  • Implementation

• Multimodal emotion analysis system

  2020–2021

  I built a system that predicts instructor ratings from voice and facial data and integrated it into the production service.

  Cut instructor management effort by 40% year on year and raised customer satisfaction by 2 points year on year.

  • Research
  • Proof of concept
  • Implementation
  • Measurement
  • Deployment and operation

• IoT / LPWA new business development

  2018–2019

  I drove a new business built on IoT and low-power wide-area (LPWA) networks myself, from the technology survey through device and platform development to business validation.

  Quantitative outcomes are confidential.

  • Planning
  • Research
  • Proof of concept
  • Design
  • Implementation

See the projects →

* Project visuals are withheld due to confidentiality agreements.

Record

Notes

Publishing AI × Design research openly, including the process itself.

• AI and Design Weekly Watch (2026-10-05 to 10-12)

  2026-10-12

  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 T3 expert opinion. Figma made the Figma agent generally available, and the agent now reads guidelines stored in a design library on every prompt. Google made Nano Banana 2.1 generally available, Anthropic released Haiku 5.5 and halved the cache-read price of Sonnet 5.5, and OpenAI added an Ultrafast mode to GPT-6.1 Sol. In T2, a UK Skills England assessment of the creative industries says roles are shifting from 'makers' to 'directors'; no survey qualified within the window. In T3, Jakob Nielsen argued that synthetic users suit divergent exploration while validation belongs with real users, and Luke Wroblewski argued that chat threads cannot track agents at scale.

• What Should Creators Be Able to Take Over When Generative AI Does the Making? (Lessons from Aviation Automation Research)

  2026-10-11

  This note rereads, for creative work, a Japanese article that used aviation regulations to ask which skills software engineers should keep when AI agents write their code (Shimitaro 2026). What aviation regulations and research have protected is a state in which pilots use the autopilot but can take back the aircraft when it disengages. The FAA treats manual flight as the foundation of other flying skills and leaves the decision to fly manually to the pilot in command rather than to fixed quotas. In the research, the more successful an automated system is, the more investment in operator training it may require (Bainbridge 1983); people can sustain monitoring of a rarely changing source for only about half an hour; and automation bias cannot be prevented by training or instructions (Parasuraman and Manzey 2010). Cognitive skills decline more readily than manual skills: in simulator flights by 16 airline captains and first officers, instrument scanning and aircraft control were largely retained, whereas tracking position without a map display and recognizing instrument failures showed more frequent problems (Casner et al. 2014). Studies of generative AI report a shift from production to evaluation (Simkute et al.), a self-reported association between higher confidence that AI can perform a task and less critical thinking (Lee et al., 319 knowledge workers), and a 19% lower likelihood of correct solutions on a task outside the AI frontier (Dell'Acqua et al., 758 participants). In creative work, AI errors appear as plausible mediocrity without any alarm. Ideating with AI images strengthened fixation on an initial example (60 participants), and using AI ideas made individual works better but more similar to one another (Doshi and Hauser; Anderson et al., 36 participants). Based on this evidence, the note defines the capacity to take over in creative work as reframing the question, discerning the quality of options, and redirecting the work, rather than correcting errors in AI output. It then lists practices: generating the first ideas without AI, writing evaluation criteria before seeing AI output, returning to manual work at the core of a piece rather than following fixed quotas, trying arrangements in which AI serves as the monitor, bringing cases in which AI surprised the team back into critique and debriefs, and keeping hands-on practice. No study was found that measured skill decline among creative practitioners, and the listed practices are analogies from other fields whose effects have not been tested.

• What Is the Philosophy of Design? (Academic Research)

  2026-10-11

  This note examines, through the literature, what the philosophy of design asks, where it came from, which currents it has passed through, and where it is heading. The philosophy of design announced itself as a young field around 2000, in articles and a special issue of Design Studies. Galle characterized it as the pursuit of insights about design by philosophical means, that is, by rational reflection rather than empirical observation. Love distinguished it from "design philosophy", a term tied to the study of design methods, and placed it alongside the philosophy of science and the philosophy of technology. The papers in the special issue dealt with conceptualizations of design, methodology, criteria for quality, the dual nature of technical artefacts (a physical structure and an intended function), and phenomenology. The field originated in two waves of attempts to make design scientific (the modernist search for objectivity in the 1920s and the design methods movement of the 1960s) and in the reaction against them. In the 1970s, some pioneers of the movement turned away from methods, Rittel and Webber called planning problems wicked problems that have no definitive formulation, and Schön criticized Simon's "science of design" for assuming well-formed problems. From there, Cross set up design as a discipline with its own forms of knowledge rather than as a science, and Buchanan argued that design should be recognized as a new liberal art of technological culture. From the 2000s, the object of inquiry widened from the designer's process to how designed things shape people and the world. Verbeek argued that because technologies coshape human actions, designers are doing ethics by other means, and ontological designing holds that design shapes the world and that this world in turn designs us. Critical design, design justice, transition design, and posthumanist and more-than-human design each question the assumptions of human-centeredness or universalism. On generative AI, the literature has begun to ask what happens when design decisions are substituted by machines (Verganti et al.), what is actually generated by generative AI (Bordas et al.), and whether generative AI makes design converge on a dominant way of knowing (Vogel and Quadflieg). The Design Philosophies track of DRS 2026 received 34 submissions, and six papers dealing with ontology, epistemology, cognition, ethics, and aesthetics remained in the track. Whether design knowledge requires a distinct epistemology is still contested between Cross's axiom and Digby's critical rationalism, which regards a separate epistemology as unnecessary. The 2026 volume edited by Secomandi and Verbeek discusses a shift from a "philosophy of design", in which philosophers discuss design from outside, toward "a designerly way of doing philosophy".

See the notes →

Get in touch

Contact

Tell me about the project and when you need it, and I will take it from there.