PhygitAiWiki

PhygitAi: Beyond Phygital is an ongoing interdisciplinary design research initiative originated by Abbas Sibai, Ph.D. It investigates how artificial intelligence participates in creative development, human judgment and authorization, and the behavior of intelligent physical–digital products and systems. Disagreement, responsibility and accountable revision are central questions.

Within the initiative, a proposed methodology has been developed and remains open to revision. Its conceptual framework connects Designing with Intelligence and Designing for Intelligence across design research, the design process, and product function and use. Its methodological instruments include six revisitable gates, the seven-field Intention–Behavior Record and related investigation procedures. Constructed interdisciplinary applications illustrate the proposal. Research propositions and planned educational and comparative studies will guide empirical investigation of its usefulness.

PhygitAi builds upon established design, HCI, engineering and governance knowledge. Having distinct terminology and organization does not establish that its methods are original or more effective. Their differentiation, limitations and potential contributions remain questions to investigate. Examine the purpose-first research comparison ↗

New to the terminology? See how and when to use PhygitAi ↗

Phygital design already examines relationships among physical settings, digital systems, and intelligent technologies. Beyond Phygital does not mean replacing those established inquiries. PhygitAi extends the questions toward how design knowledge and intentions become permissions for later AI-mediated action, how people encounter the consequences, and when those intentions and permissions should be reconsidered.

Three interconnected contexts. In design research, PhygitAi helps formulate and challenge questions about intelligence, knowledge, and responsibility. In the design process, it examines intentions, AI inputs, authorization, and how decisions become arrangements. In product function and use, it questions actual intelligent behavior, effective human control, and accountable reconsideration. These are overlapping contexts, not separate methodologies or mandatory stages.

Six revisitable inquiry gates organize a preliminary examination of intentions, AI inputs, permissions, implementation, disagreement, and accountable revision. They are proposed instruments within the research initiative, not pass/fail grades, a complete safety process or evidence of proven methodological superiority.

A seven-field record used to trace a specific design decision. It can separate proposed from observed events and preserve uncertainties about AI input, permissions, outcomes, and revision without inventing missing evidence.

1. Intention
2. AI Input
3. Permission
4. Event
5. Outcome
6. Decision
7. Retest

Six optional digital workflows support preliminary examination of project evidence: Audit, Compare, Inspect, Detect, Explain, and IBR. The methodology can also be discussed or applied without these tools. The workflows are not independent verification or certification.

Explore the public screening tools ↗

UAiPN — User–AI Participation & Negotiation is a separate, newly proposed educational framework under development ↗ within PhygitAi. It is not a seventh screening workflow, an established Book III instrument, or a deployed feature of the current platform packages. Its pedagogical procedure and contribution remain to be investigated.

A fictional adaptive reading lamp for students illustrates how an AI suggestion, intended human authority, planned physical behavior, missing observations, and potential revisions can be documented. It is a conceptual demonstration, not empirical proof of effectiveness.

PhygitAi is an ongoing research initiative whose methodological and educational approaches remain under examination. No empirical superiority, proven reduction of harm, engineering safety certification, legal liability determination, or regulatory endorsement is claimed. An AI system's recognition of a condition is not itself permission to act; documenting disagreement does not imply AI consciousness, moral agency, or independent authority.