Why generative AI is different from designed technology regarding task-relatedness, user interaction, and agency
Bibliographic Data
| ID | 16899263 |
|---|---|
| Authors | Ingo Schulz-Schaeffer (0000-0002-5867-0926, RWTH Aachen University, corresponding author) |
| Year | 2025 |
| Volume | 12 |
| Issue | 3 |
| Publication date | 2025-09-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Big Data & Society (JOURNAL) |
| Journal identifiers | ISSN: 2053-9517 • E-ISSN: 2053-9517 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/20539517251367452 |
| OpenAlex | W4414121839 |
| Language | EN |
| Citations received | 3 |
| References cited | 33 |
Generative AI requires us to rethink what constitutes technology, how technology is socially constructed, and how technology is used. Our common understanding of technology is mainly derived from designed technology. Generative AI, in contrast, is a learned technology and, moreover, a technology that mainly relies on unsupervised learning. The paper argues that this has far-reaching consequences with respect to the task-relatedness of generative AI systems, the user interaction with generative AI systems, and the agency of those systems. Designed technological behavior is usually designed with respect to particular tasks. AI systems based on unsupervised learning, in contrast, are not task-related is such a manner. Consequently, generative AI systems cannot be operated in the same way as designed technology. Rather than instructing them, operating them requires what can be described as strategic interaction. Interacting with generative AI systems leads to new actor roles and role relationships. The agency of technological artifacts that are designed and used for particular tasks tends to be the agency of a tool. In contrast, the new agency of generative AI systems lies in their capability to mobilize machine-learned versions of human experiential knowledge and thereby to become in some respects similar to a human interaction partner
Experiential knowledge · Experiential learning · Generative Design · Generative grammar · Generative model · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI
Science in action
Shaping technology/building society
Generative AI
Big Data's Disparate Impact
A Brief Overview of ChatGPT
Pre-train, Prompt, and Predict
Deep learning
Affordance, Role, and Script as Complementary Concepts of Artefact-User Interaction, Illustrated by the Example of an Egg Separator
Big data
Is explainable AI responsible AI
Human Behavior and Social Processes
On the Philosophy of Unsupervised Learning
The Social Construction of Facts and Artefacts
Good models borrow, great models steal
Thinking critically about and researching algorithms
Foundation models are platform models
How the machine 'thinks
Configuring the User as Everybody
Mixing Humans and Nonhumans Together
Beyond Human
| Citations per year | 3 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |