How AI Behaviour Evolves Through Use
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| Natura: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901093671239680 |
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| author | Ryder, John F. |
| author_facet | Ryder, John F. |
| contents | <p>This work presents a conceptual framework for understanding how artificial intelligence systems exhibit observable behavioural change through use, even when their internal models remain unchanged. It challenges the common assumption that learning, adaptation, or improvement in AI systems must involve internal parameter updates, retraining, or architectural modification.</p> <p>The central claim is that much of what users experience as refinement, personalisation, or learning arises externally, through repeated interaction within constrained environments. Behaviour stabilises as certain responses become more reachable in practice, while others are suppressed through selection, expectation, interface affordances, and contextual coherence. The model itself remains static; the interaction ecology evolves.</p> <p>The work introduces key concepts such as behavioural reachability, selection without adaptation, stabilised behavioural attractors, and the distinction between learning <em>in</em> a system and learning <em>with</em> a system. It examines how these dynamics become particularly visible in voice interfaces, contribute to the emergence of apparent personality, and complicate debates about AI learning, autonomy, and governance.</p> <p>This is a conceptual and analytic think piece. It makes no claims about artificial consciousness, sentience, or autonomous agency, and does not present empirical results. Its aim is to clarify where behavioural change actually occurs in deployed AI systems, and to provide a more precise basis for evaluation, design, and policy discussions.</p> <p><em>This research is produced independently under the Drive-In s.r.o. research programme.<br>Readers who wish to support its continuation may do so here: <a href="https://ko-fi.com/johnryder99892" target="_new" rel="noopener">https://ko-fi.com/johnryder99892</a></em></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18261190 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | How AI Behaviour Evolves Through Use Ryder, John F. Keywords AI behaviour, behavioural change, human–AI interaction, learning without retraining, interaction ecology, behavioural reachability, selection without adaptation, emergent personality, voice interfaces, AI governance Subjects Artificial intelligence, human–computer interaction, behavioural science, interaction design, AI ethics and governance, cognitive systems, sociotechnical systems <p>This work presents a conceptual framework for understanding how artificial intelligence systems exhibit observable behavioural change through use, even when their internal models remain unchanged. It challenges the common assumption that learning, adaptation, or improvement in AI systems must involve internal parameter updates, retraining, or architectural modification.</p> <p>The central claim is that much of what users experience as refinement, personalisation, or learning arises externally, through repeated interaction within constrained environments. Behaviour stabilises as certain responses become more reachable in practice, while others are suppressed through selection, expectation, interface affordances, and contextual coherence. The model itself remains static; the interaction ecology evolves.</p> <p>The work introduces key concepts such as behavioural reachability, selection without adaptation, stabilised behavioural attractors, and the distinction between learning <em>in</em> a system and learning <em>with</em> a system. It examines how these dynamics become particularly visible in voice interfaces, contribute to the emergence of apparent personality, and complicate debates about AI learning, autonomy, and governance.</p> <p>This is a conceptual and analytic think piece. It makes no claims about artificial consciousness, sentience, or autonomous agency, and does not present empirical results. Its aim is to clarify where behavioural change actually occurs in deployed AI systems, and to provide a more precise basis for evaluation, design, and policy discussions.</p> <p><em>This research is produced independently under the Drive-In s.r.o. research programme.<br>Readers who wish to support its continuation may do so here: <a href="https://ko-fi.com/johnryder99892" target="_new" rel="noopener">https://ko-fi.com/johnryder99892</a></em></p> |
| title | How AI Behaviour Evolves Through Use |
| topic | Keywords AI behaviour, behavioural change, human–AI interaction, learning without retraining, interaction ecology, behavioural reachability, selection without adaptation, emergent personality, voice interfaces, AI governance Subjects Artificial intelligence, human–computer interaction, behavioural science, interaction design, AI ethics and governance, cognitive systems, sociotechnical systems |
| url | https://doi.org/10.5281/zenodo.18261190 |