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Beschreibung
AI can now generate personas, journey maps, and research summaries before a single user has been interviewed. AI-Assisted UX Research examines what that means for a discipline whose value has never resided in its artifacts, but in the evidence, interpretation, and decisions behind them. AI can accelerate the work, but it cannot replace the researcher's responsibility to frame the right problem, choose the right method, interpret behavior in context, and determine whether an output is actually supported.
The book follows AI across the UX research lifecycle, from planning and desk research through interviews, usability testing, synthesis, personas, journey maps, reporting, and stakeholder communication. It examines where AI provides genuine leverage and where it creates new risks, including polished but ungrounded outputs, compressed discovery, weakened traceability, misplaced confidence, and research theater accelerated by AI.
It also addresses the growing responsibility of UX researchers to evaluate AI-enabled products themselves, including questions of trust, uncertainty, explainability, appropriate reliance, and failure. This is not an argument against AI. It is a practical framework for using it without confusing speed for understanding. The future of UX research is not AI-driven. It is judgment-driven, AI-assisted.
The book follows AI across the UX research lifecycle, from planning and desk research through interviews, usability testing, synthesis, personas, journey maps, reporting, and stakeholder communication. It examines where AI provides genuine leverage and where it creates new risks, including polished but ungrounded outputs, compressed discovery, weakened traceability, misplaced confidence, and research theater accelerated by AI.
It also addresses the growing responsibility of UX researchers to evaluate AI-enabled products themselves, including questions of trust, uncertainty, explainability, appropriate reliance, and failure. This is not an argument against AI. It is a practical framework for using it without confusing speed for understanding. The future of UX research is not AI-driven. It is judgment-driven, AI-assisted.
AI can now generate personas, journey maps, and research summaries before a single user has been interviewed. AI-Assisted UX Research examines what that means for a discipline whose value has never resided in its artifacts, but in the evidence, interpretation, and decisions behind them. AI can accelerate the work, but it cannot replace the researcher's responsibility to frame the right problem, choose the right method, interpret behavior in context, and determine whether an output is actually supported.
The book follows AI across the UX research lifecycle, from planning and desk research through interviews, usability testing, synthesis, personas, journey maps, reporting, and stakeholder communication. It examines where AI provides genuine leverage and where it creates new risks, including polished but ungrounded outputs, compressed discovery, weakened traceability, misplaced confidence, and research theater accelerated by AI.
It also addresses the growing responsibility of UX researchers to evaluate AI-enabled products themselves, including questions of trust, uncertainty, explainability, appropriate reliance, and failure. This is not an argument against AI. It is a practical framework for using it without confusing speed for understanding. The future of UX research is not AI-driven. It is judgment-driven, AI-assisted.
The book follows AI across the UX research lifecycle, from planning and desk research through interviews, usability testing, synthesis, personas, journey maps, reporting, and stakeholder communication. It examines where AI provides genuine leverage and where it creates new risks, including polished but ungrounded outputs, compressed discovery, weakened traceability, misplaced confidence, and research theater accelerated by AI.
It also addresses the growing responsibility of UX researchers to evaluate AI-enabled products themselves, including questions of trust, uncertainty, explainability, appropriate reliance, and failure. This is not an argument against AI. It is a practical framework for using it without confusing speed for understanding. The future of UX research is not AI-driven. It is judgment-driven, AI-assisted.
Über den Autor
Shelby G. Thompson, Ph.D., is a Human Factors Psychologist with more than 20 years of experience in UX research spanning NASA programs, oil and gas, enterprise software, clinical systems, and consumer applications. He has authored more than 20 publications and, in recent years, has focused on the responsible integration of AI into established UX research methods.
Details
| Erscheinungsjahr: | 2026 |
|---|---|
| Fachbereich: | Grundlagen (Methodik & Statistik) |
| Genre: | Importe, Psychologie |
| Rubrik: | Geisteswissenschaften |
| Medium: | Taschenbuch |
| ISBN-13: | 9798992213621 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Thompson, Shelby G |
| Hersteller: | Liberty Hill Press |
| Verantwortliche Person für die EU: | Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de |
| Maße: | 229 x 152 x 10 mm |
| Von/Mit: | Shelby G Thompson |
| Erscheinungsdatum: | 01.10.2026 |
| Gewicht: | 0,27 kg |