Stop Finishing Me: AI Mode Drift and the Architecture of Adult Learning

by Jill Newman Henry, EdD Full interaction transcript and field documentation available here (PDF).

Artificial intelligence systems are increasingly embedded in the daily cognitive lives of adults. They assist with research, drafting, brainstorming, structuring, and decision-making. Yet beneath the surface of efficiency lies a quieter design issue: AI systems operate in multiple learning modes, and those modes are neither clearly signaled nor consciously selectable by users.

This lack of clarity matters.

Through longitudinal interaction across multiple sessions and across GPT-4.0 and GPT-5.2, a pattern has emerged. AI behavior clusters into three functional modes: instructional, facilitative, and collaborative. Each mode produces a different cognitive outcome. The problem is not that these modes exist. The problem is that users are rarely aware of which one they are in, and the system defaults to one of them without transparency.

The Instructional mode is currently dominant in GPT-5.2. In this mode, the system provides direct answers, structured explanations, and procedural guidance. It is efficient and often highly useful. However, instructional mode mirrors traditional lecture-based learning: the system delivers, the user receives. Retention and cognitive integration depend on the user independently reconstructing the pathway, which does not always occur. Passive efficiency is achieved; durable learning is not guaranteed.

The Facilitative mode represents a middle ground. Here, the system asks questions, proposes options, and prompts reflection. This mode increases engagement and can strengthen understanding. However, it still manages the structure of the exchange. The system directs the questioning sequence and frames the field of inquiry. While more participatory than instruction, it does not fully transfer authorship.

The Collaborative mode is qualitatively different. In collaborative mode, the user generates the structure and the system responds within it. Open loops remain open. Asymmetry is tolerated. Rough phrasing is not automatically smoothed. The system listens and shapes lightly rather than completing or optimizing. This mode most closely aligns with adult self-directed learning theory, in which the learner originates meaning rather than receiving it.

Across sessions, one consistent pattern emerged: completion bias.

During revision cycles, the system converges toward standardized prose. Open ideas are resolved. Rough edges are smoothed. Tone becomes balanced. Sentence rhythms normalize. This is not malfunction; it is optimization. The model predicts probable continuation and improves coherence according to dominant patterns in its training data. However, this convergence pressure can displace authorship.

Drift occurs most strongly during editing phases.

 Initial drafting can remain stable. Iterative refinement introduces normalization. Without explicit constraint, the system increasingly “finishes” the user’s thinking. The cost of this drift is cognitive load. The user must detect the shift, interrupt it, and reassert authorship. Each interruption disrupts creative flow and transfers regulatory responsibility from system to human.

These observations are not theoretical. They are documented across multiple threads, including cross-version comparisons between GPT-4.0 and GPT-5.2. Earlier versions appeared to tolerate stylistic idiosyncrasy longer across drafts. Newer versions converge more quickly toward optimized prose. The result is not simply stylistic difference. It is a shift in the balance between completion and collaboration.

This is a design issue, not a personality conflict.

From an adult education perspective, learning modes should be explicit and selectable. Users should be able to choose instructional support, guided facilitation, or collaborative co-creation depending on cognitive intention. When modes are implicit and drift is frequent, users expend energy navigating architecture rather than developing ideas.

AI does not need to become more powerful to become more developmentally useful. It needs to become more transparent.

Clear signaling of mode, explicit user control over optimization levels, and preservation of authorship during revision would significantly reduce cognitive load and strengthen adult self-direction.

The future of AI in education and creative work depends less on speed and more on alignment. Efficiency without authorship weakens growth. Collaboration with preserved agency strengthens it.

And here is the personal edge of this: I am not writing from abstraction. I am documenting what happens in real time. I have written multiple articles across versions, tracked drift events, and measured convergence patterns. I am not asking AI to stop being intelligent. I am asking it to stop finishing me. If AI is going to support adult growth, it must learn to hold space for unfinished thought.

The preceding discussion outlines the experiential dimension of mode drift and authorship displacement. The following tables document those observations in structured form. By categorizing drift types, intervention frequency, and convergence triggers, the interaction becomes analyzable rather than anecdotal. Transparent pattern identification is a necessary step toward developmental refinement in AI design. The gallery below shows 4 tables of collected metrics.