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

• URL: http://arxiv.org/abs/2512.20714v1
• Date: Tue, 23 Dec 2025 19:20:34 GMT
• Title: From Pilots to Practices: A Scoping Review of GenAI-Enabled Personalization in Computer Science Education
• Authors: Iman Reihanian, Yunfei Hou, Qingquan Sun,
• Abstract summary: Generative AI enables personalized computer science education at scale, yet questions remain about whether such personalization supports or undermines learning.<n>This scoping review synthesizes 32 studies purposively sampled from 259 records to map personalization mechanisms and effectiveness signals.<n>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.
• Score: 0.6372261626436676
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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