AI Accelerates Coding Skills for Scientist, Enhancing Research Efficiency

Hannah Hackney, a PhD student at McGill University in Montreal, Canada, specializes in studying the optical and electronic properties of organic molecules through chemical synthesis. Her recent project required her to delve deeper into coding to manage and process large volumes of data, a skill she felt underprepared for despite some casual experimenting with coding in her free time using free apps, YouTube videos, and books. Hackney's colleague recommended she try using artificial intelligence (AI) tools like ChatGPT and Claude for coding assistance. Initially skeptical, Hackney was surprised by how effectively these large language models (LLMs) could accelerate her learning process. While she had previously found AI-generated suggestions for reaction mechanisms and initial drafts of documents to be either obvious or inaccurate, the LLMs proved to be invaluable for coding. Hackney's success with AI in coding can be attributed to the "zone of proximal development," a concept from educational psychology introduced by Lev Vygotsky in the early twentieth century. This theory suggests that learning is most effective when new information builds upon what the learner already knows, guided by a more knowledgeable source. In her case, Hackney's foundational knowledge of coding—gained from introductory courses—allowed the AI to provide meaningful and practical guidance, filling in gaps and accelerating her understanding. The conversational nature of LLMs significantly enhances the learning experience. Unlike traditional educational resources, where finding specific answers or waiting for forum responses can be time-consuming, LLMs offer immediate, tailored feedback. This rapid question-and-answer interaction helps learners enter a flow state, maintaining motivation and a sense of control. Hackney could easily adjust the complexity of explanations and delve into higher-level coding skills, such as structure, design, and debugging, which are typically overlooked in beginner tutorials. Moreover, AI tools provide "scaffolding"—a supportive framework that helps learners build their skills progressively. Hackney found that these tools could generate functional code for basic tasks, allowing her to focus on developing critical thinking and problem-solving abilities. These higher-order skills are essential for effective coding and are often already cultivated in scientists and researchers through their rigorous training in logical and algorithmic thinking, organization, attention to detail, and failure anticipation. In summary, Hannah Hackney's experience highlights the potential of AI in democratizing access to specialized education. By leveraging the strengths of LLMs, particularly their ability to provide immediate, personalized feedback, learners can rapidly acquire new skills even if they fall outside their primary expertise. For hackney, AI tools became a powerful ally in her journey to becoming a proficient coder, enhancing her ability to tackle complex data processing tasks in her research. Industry insiders and experts have noted the growing role of AI in education, describing it as a valuable supplement that can bridge the gap between theoretical knowledge and practical application. Companies like Anthropic, creators of Claude, and OpenAI, developers of ChatGPT, are dedicated to advancing LLMs to make them more versatile and user-friendly, further supporting the trend of AI-assisted learning. These advancements not only benefit students and researchers like Hackney but also hold promise for a wider range of professionals looking to upskill in areas such as programming, data analysis, and beyond.