Date
Publisher
arXiv
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.
Study at a glance
- Question: How is generative AI used to personalize higher education computer science, and what design choices support learning?
- Setting and data: Higher education computer science courses; 32 empirical studies published between 2023 and 2025 purposively sampled from 259 records.
- Method: Scoping review (following Arksey-O'Malley and JBI guidance).
Why it matters
Delivering personalized support to novice computer science students at scale remains a challenge. While generative AI enables individualized assistance, questions persist about whether it genuinely supports or undermines learning, risks student over-reliance, and compromises integrity.
Key findings
- Explanation-first guidance, graduated hint ladders, and solution withholding were consistently associated with more positive learning processes than unconstrained chat interfaces that routinely provided full answers.
- Across 32 synthesized studies, generative AI personalization grouped into five domains: assessment (25%), tutoring assistants (22%), personalized materials (19%), formative feedback (19%), and code review (16%).
- Positive learning outcomes commonly depended on grounding AI assistance in student artifacts, incorporating human review loops, providing multi-level scaffolding, and integrating AI literacy instruction.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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