# From Drift to Direction: The Architecture Behind Digression Control (Role Play)

DevFeed: [From Drift to Direction: The Architecture Behind Digression Control (Role Play)](<https://devfeed.tech/articles/from-drift-to-direction-the-architecture-behind-digression-control-role-play-26353.md>)

Original publisher: [Read original article](<https://medium.com/udemy-engineering/from-drift-to-direction-the-architecture-behind-digression-control-role-play-1720d9a3a6a0?source=rss----19c6d3367ed4---4>)

Author: Raka Dalal

Published: 2026-04-09T12:25:37Z

Content type: article

Language: en

Sources: [Udemy Engineering](<https://devfeed.tech/sources/udemy-engineering.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [systems](<https://devfeed.tech/topics/systems.md>), [Learning](<https://devfeed.tech/topics/learning.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [blocking](<https://devfeed.tech/tags/blocking.md>), [conversational-ai](<https://devfeed.tech/tags/conversational-ai.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [learning](<https://devfeed.tech/tags/learning.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-evaluation](<https://devfeed.tech/tags/llm-evaluation.md>), [prompt-engineering](<https://devfeed.tech/tags/prompt-engineering.md>), [systems](<https://devfeed.tech/tags/systems.md>)

## AI overview

Udemy's Role Play experience uses open-ended LLM-mediated conversations for practice with defined learning goals. The article examines digression as a systems challenge and introduces a Response Steering Layer intended to preserve conversational naturalness while improving goal adherence.

## Source excerpt

Role Play @UdemyIntroduction AI-powered role play systems aim to recreate one of the most effective learning modalities: practicing real-world conversations in a safe, repeatable environment. In Udemy's Role Play experience, learners engage in open-ended, free-text dialogues with an AI character that embodies a specific persona -- such as an interviewer, a stakeholder, or a customer -- while working toward clearly defined learning goals. This contrasts with many traditional digital learning tools, which rely on static content, multiple-choice interactions, or scripted simulations that limit conversational variability. This design prioritizes realism and skill transfer, allowing learners to respond naturally rather than selecting from predefined options. Open-ended conversations mediated by LLM-based systems, however, introduce a core systems challenge: digression. In real conversations, people may drift off-topic, but human tutors actively manage and redirect such drift. In a learning-oriented role play mediated by an AI system, persistent digressions undermine the experience in more subtle but consequential ways. Learners may shift into meta-conversations, attempt to override the role being played, or explore tangents unrelated to the scenario's objectives. When this happens, learning goals become harder to assess, feedback loses grounding in the conversation, and the interaction no longer reflects the real-world situation the learner is meant to practice. A naive solution is to tightly constrain the conversation -- blocking inputs, rejecting turns, or forcing the dialogue back onto a scripted path. While this approach improves goal adherence, it comes at the cost of immersion and learner agency. Overly rigid controls make the AI feel less like a conversational partner and more like an instructional interface. On the other hand, allowing unrestricted conversational freedom preserves naturalness but leads to goal drift, inconsistent learning signals, and reduced reliab