From Pilots to Practices: A Scoping Review of GenAI-Enabled Personalization in Computer Science Education

TL;DR

This paper reviews recent studies on how generative AI is used to personalize computer science education, highlighting effective design patterns and proposing a framework for responsible adoption.

Contribution

It synthesizes 32 studies to identify effective personalization mechanisms and offers a framework for safe, scalable implementation of GenAI in higher education.

Findings

Designs with explanation-first guidance improve learning outcomes.

Context-aware tutoring and artifact grounding are effective.

Risks like bias and privacy require operational mitigation.

Abstract

Generative AI enables personalized computer science education at scale, yet questions remain about whether such personalization supports or undermines learning. This scoping review synthesizes 32 studies (2023-2025) purposively sampled from 259 records to map personalization mechanisms and effectiveness signals in higher-education computer science contexts. We identify five application domains: intelligent tutoring, personalized materials, formative feedback, AI-augmented assessment, and code review, and analyze how design choices shape learning outcomes. Designs incorporating explanation-first guidance, solution withholding, graduated hint ladders, and artifact grounding (student code, tests, and rubrics) consistently show more positive learning processes than unconstrained chat interfaces. Successful implementations share four patterns: context-aware tutoring anchored in student artifacts, multi-level hint structures requiring reflection, composition with traditional CS infrastructure (autograders and rubrics), and human-in-the-loop quality assurance. We propose an exploration-first adoption framework emphasizing piloting, instrumentation, learning-preserving defaults, and evidence-based scaling. Recurrent risks include academic integrity, privacy, bias and equity, and over-reliance, and we pair these with operational mitigation. The evidence supports generative AI as a mechanism for precision scaffolding when embedded in audit-ready workflows that preserve productive struggle while scaling personalized support.