Tiffany Barnes

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, raising hopes for supporting students while creating concerns about learning and academic integrity. Researchers have responded by developing customized tools that leverage LLMs' potential while mitigating these risks. Despite a rapidly growing body of empirical studies, no prior review has focused specifically on these purpose-built tools, where educators' pedagogical and technical design decisions are explicitly represented. We address this gap by reviewing LLM-driven, student-facing tools in undergraduate computing education, examining their pedagogical roles, technical design, evaluation methods, and impacts on learners, and discussing what they imply for the broader educational system. Drawing on PRISMA guidelines, we searched three major databases (ACM, IEEE, and ScienceDirect), conducted rigorous screening, and analyzed 52 papers. Technically, prompt engineering and multi-stage pipelines are the dominant approaches, with guardrails and prompt chaining also widely adopted, while more demanding methods such as fine-tuning remain rare. Pedagogically, most tools position the LLM as a Mentor who gives feedback on student work or a Tutor who delivers instruction, whereas roles that support metacognition, learning-by-teaching, and especially teamwork are very rare. Evaluation relies heavily on surveys and interaction logs, capturing student perceptions and usage far more often than learning outcomes, with little attention to long-term effects. Across studies, impacts on engagement and student perception are generally positive, whereas evidence for gains in learning and performance is more mixed. We argue that future work should prioritize longitudinal and learning-focused evaluation, personalization for different learners, and collaborative sensemaking and teamwork. This review offers researchers, developers, and educators a roadmap for designing and studying the next generation of AI-enhanced learning tools.

Large language models (LLMs) are rapidly reshaping software development, but their impact across the full software development lifecycle is underexplored. Existing work tends to focus on isolated activities such as code generation or testing, leaving open questions about how LLMs affect developers, processes, products, and the broader software ecosystem. We address this gap through semi-structured interviews with sixteen early-adopter software professionals who integrated LLM-based tools into their day-to-day work in early to mid-2023. We treat these interviews as early empirical evidence and compare participants’ accounts with recent work on LLMs in software engineering, noting which early patterns persist or shift. Using thematic analysis, we organize our findings around four dimensions: people, process, product, and society. Developers reported substantial productivity gains from reducing mundane tasks, streamlining search, and accelerating debugging, but also described a productivity-quality paradox: they frequently discarded generated code and shifted effort from writing code to critically evaluating and integrating it. LLM use was highly phase-dependent, with strong uptake in implementation and debugging but limited influence on requirements gathering and collaborative work. Participants developed new competencies to use LLMs effectively, including prompt engineering strategies, multi-layered verification, and security-conscious integration to protect proprietary data. They also anticipated changes in hiring expectations, team practices, and computing education, while emphasizing that human judgment and foundational software engineering skills remain essential. Our findings, consistent with evidence from large-scale studies, offer actionable implications for developers, organizations, educators, and tool designers seeking to integrate LLMs responsibly into professional software practice.