Exploring the Design Space of LLM-Based Programming Support in CS Education: A Scoping Review through the Lens of Assistance Governance

2026-07-23

AI Summary

Current LLM-assisted programming education systems lack a unified conceptualization of their assistance boundaries, implementation approaches, and control mechanisms, hindering education-oriented comparative analysis. Addressing this gap, this study conducts a scoping review and qualitative content analysis of 90 relevant systems, applying thematic coding to develop an innovative three-dimensional governance framework—PEA (Policy, Enforcement, Authority)—alongside a corresponding governance codebook. The framework reveals pervasive issues in existing systems, notably excessive centralization of authority and insufficient configurability. This work provides the first systematic mapping of governance design patterns in LLM-based programming support tools, establishing a theoretical foundation and a structured design vocabulary for developing next-generation educational tools that are goal-aligned, configurable, and accountable.

Abstract

As large language models (LLMs) become integrated into programming education, learner-facing systems increasingly differ in how that assistance is bounded, enacted, and controlled. These governance decisions are often described implicitly, making it difficult to compare systems in educationally meaningful ways. To address this gap, we conduct a scoping review and qualitative synthesis of 90 peer-reviewed LLM-based programming support systems in CS education. We analyze assistance governance through three dimensions, which we refer to collectively as PEA: Policy, capturing what forms of help are allowed or restricted; Enforcement, capturing how those boundaries are operationalized through interaction and system behavior; and Authority, capturing who can configure, adapt, or override them during use. Our findings show that systems often share similar pedagogical goals, but implement those goals through varied enforcement mechanisms. At the same time, authority remains highly centralized in system logic, with fewer systems giving learners or instructors runtime control. This work contributes PEA as a three-dimensional analytic lens, a governance codebook empirically refined within these dimensions, and a map of underexplored configurations in the current design space of LLM-based programming support. By making these explicit and comparable, PEA offers a vocabulary for analyzing existing systems and designing future tools that are pedagogically bounded, configurable, and accountable.

Problem

• LLM-based programming support
• assistance governance
• CS education
• design space
• pedagogical boundaries

Innovation

• Assistance Governance
• PEA Framework
• LLM-based Programming Support
• Scoping Review
• Configurable Educational Tools