The variety of help that large language models (LLMs) provide has made them popular among students across fields. Computing education has been particularly affected, as LLMs can handle coding tasks effectively and provide feedback. This has raised hopes for supporting students, while creating concerns about learning and academic integrity. Researchers have responded by developing tools that leverage LLMs' potential while mitigating risks. Despite growing empirical studies on LLM-driven tools, there is no comprehensive study examining these tools in computing education, critical for understanding the future of learning systems. We review LLM-driven, student-facing tools in undergraduate computing education, examining their pedagogical approaches, technical design, evaluation approaches, and educational impacts. We also discuss how these tools impact the broader educational system, including institutional and pedagogical structures. Following PRISMA guidelines, we systematically searched three major libraries, conducted rigorous screening, and analyzed 52 papers. Our findings reveal that prompt engineering and multi-stage pipelines are the dominant technical approaches, with guardrails and prompt chaining also widely adopted. Pedagogically, most systems provide scaffolding, problem-based learning, and direct instruction. For evaluation, researchers commonly relied on student surveys and interaction logs, with limited assessment of long-term learning outcomes. Evidence shows generally positive impacts on student performance, efficiency, and engagement, although significant challenges remain in delivering adequate scaffolding, preventing over-reliance, and supporting the development of meta-cognitive and problem-solving skills. Future work should focus on longitudinal outcomes, systematic evaluation of learning effectiveness, and the integration of established pedagogical frameworks. This review provides researchers, developers, and educators with a roadmap for designing and studying the next generation of AI-enhanced learning tools.
Tabarsi et al. (Tue,) studied this question.