Literature Analysis

Introduction

As generative AI tools like Cursor AI become increasingly embedded in software development, they raise critical questions about productivity, ethics, and the evolving role of human coders. This literature review explores the central question of how do generative AI tools like Cursor AI enhance software development while introducing new ethical and professional challenges? In alignment with our Instruction Set, this analysis considers both the benefits and drawbacks of AI-assisted coding, drawing on academic and industry sources to evaluate how these tools shape the present and future of programming.

How AI like Cursor can make Life Easier

Cursor AI shows promise for the use of generative AI to help streamline development. According to Andrade-Hidalgo et al., “AI tools can reduce human error by identifying bugs and suggesting corrections,” which enhances code reliability and efficiency. They also note that “AI-generated suggestions often reflect best practices,” promoting knowledge sharing and consistency across teams (Andrade-Hidalgo et al. 7). These features reduce onboarding time for new developers and foster cleaner, more maintainable codebases. Additionally, Cursor AI can bridge knowledge gaps within teams of varying experience levels, creating a more inclusive and collaborative development environment. By offloading repetitive or syntactic tasks, AI tools also reduce mental fatigue, potentially improving developer well-being and job satisfaction. This not only supports more sustainable coding practices but also allows teams to focus on innovation rather than routine troubleshooting. As AI becomes more integrated, tools like Cursor may play a key role in shaping efficient and balanced software development workflows.

The Ethical Dilemma of Generative AI

Wynants et al. emphasize the educational potential of AI, stating that “AI can scaffold learning by offering real-time feedback,” which helps novices grasp complex concepts more intuitively. They caution, however, that “blind reliance on AI-generated content can erode critical thinking skills” (Wynants et al. 3). This highlights a big dilemma, AI can serve as a supportive tutor, yet risks becoming a crutch. The balance between guidance and independence becomes a crucial consideration in both academic and workplace training. Educators and mentors must encourage self aware and ethical reflection, ensuring learners understand not just how code works, but why it works. Without this emphasis, students may gain speed but sacrifice a deeper, conceptual understanding of fundamental programming logic.

Lowe explores the ethical gray zones of generative AI, particularly around authorship, responsibility, and bias. She writes, “When AI generates code, the question of who owns or is responsible for it becomes murky,” raising legal and professional concerns about liability and intellectual property (Lowe 22). She also warns that “AI systems may replicate biased or insecure patterns from their training data,” which could embed systemic flaws into critical applications (Lowe 24). These concerns are particularly urgent in high stakes environments like healthcare, law enforcement, or finance, where software reliability and ethical standards must be upheld. Developers cannot afford to assume AI’s outputs are neutral. They must audit, interpret, and when necessary reject the suggestions.

The Future of Generative Coding AI

Looking ahead, generative AI is poised to reshape the programming profession. A Forbes article notes, “AI-assisted programming roles, using tools like GitHub Copilot and Cursor that not only make coding more efficient but also save time for developers to focus on core business logic and problem-solving rather than handling repetitive tasks.,” while also acknowledging concerns that such tools may “take over the jobs of low-skilled coders” (Dabbirl). Similarly, Brainhub reports that “Programming trends suggest that software development will undergo a radical change in the future: the combination of machine learning, artificial intelligence, natural language processing, and code generation technologies will improve… and bring about several notable changes and advancements” (Brainhub). This transition suggests that programming is evolving from rule based task execution to a field defined by creativity, ethics, and strategic planning. Rather than replacing developers, AI may elevate the profession by demanding higher order thinking and interdisciplinary skills. Preparing for this transformation requires AI literacy, ethical reasoning, and critical thinking to be integrated into technical education and professional development.

Conclusion

Issues around authorship, intellectual property, and skill degradation are not minor byproducts but core concerns that must be actively addressed. To navigate these challenges, developers and educators need to cultivate a culture of ethical literacy alongside technical proficiency. This includes building critical thinking into AI tool usage, encouraging transparency in how AI decisions are made, and integrating discussions of fairness, accountability, and bias into coding education. Moving forward, the future of coding will depend less on how quickly we can generate code and more on how thoughtfully we can design, evaluate, and apply it. As generative AI continues to evolve, human oversight becomes not just important but indispensable. Developers who thrive in this future will be those who pair their technical skills with ethical reasoning and a nuanced understanding of their tools’ capabilities and limits. This will help guide users of AI like cursor how to correctly understand and use AI as a tool rather than a shortcut to skip the inner workings of how code works.