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‘Everyone now uses AI’: chemistry students share how and why they’re using AI tools

Gary Lloyd | Nov 10,26 | 01:33 EST

Technology

From summarising concepts to writing lab reports, AI use is ‘rife throughout undergraduate all the way up to PhD’

What is AI? This section defines various terms within artificial intelligence. AI refers to machines performing human-like tasks like reasoning and decision-making. Generative AI analyzes data patterns to create original content (text, images, videos), with examples like ChatGPT and Copilot. Large Language Models (LLMs) are deep learning programs trained on vast amounts of data to understand and generate language. Neural networks are interconnected artificial neurons that learn from statistical patterns. Machine learning is a subset of AI enabling machines to learn from datasets and make predictions without explicit programming, improving with more data. Deep learning is an advanced form of machine learning using multi-layered neural networks to analyze complex data for applications like speech recognition and image generation. Putting it to the students Dion Josh, an undergraduate at the University of Oxford, primarily uses AI to simplify complex chemistry concepts, especially for inorganic chemistry. He finds AI makes tutorials more productive by handling basic questions, allowing professors to address 'really intense questions.' Ellie Yates, a PhD student at the University of Sheffield, cautions against using AI for summarising research due to 'hallucinated' answers that can mislead research. She stresses the importance of AI literacy to understand its capabilities and limitations, and worries about students losing critical writing skills by over-relying on AI for lab reports. Conversely, Jasper Butcher, a protein design researcher, uses AI tools 'for everything,' including generating code and running experiments, which he believes accelerates scientific work. He also uses AI to create more creative problem sheets for students. However, Butcher expresses concern that AI might lead to students 'one-shotting' their degrees in isolation, reducing collaborative learning experiences. Teaching in the time of AI Fraser Scott, a chemistry education researcher at the University of Strathclyde, observes that AI use is widespread among undergraduate and PhD students, evolving too quickly for educators to fully adapt. He notes that the current response often focuses on safeguarding assessments rather than integrating AI as an educational tool. Scott argues that educators have a responsibility to teach students how to use generative AI appropriately, much like textbooks or calculators. He also highlights a 'dichotomy of use,' where educators themselves frequently use AI while often holding strong opinions against student usage. Students, like Dion Josh, feel AI is indispensable for university work, indicating a shift in learning paradigms.

What is AI?

This section defines various terms within artificial intelligence. AI refers to machines performing human-like tasks like reasoning and decision-making. Generative AI analyzes data patterns to create original content (text, images, videos), with examples like ChatGPT and Copilot. Large Language Models (LLMs) are deep learning programs trained on vast amounts of data to understand and generate language. Neural networks are interconnected artificial neurons that learn from statistical patterns. Machine learning is a subset of AI enabling machines to learn from datasets and make predictions without explicit programming, improving with more data. Deep learning is an advanced form of machine learning using multi-layered neural networks to analyze complex data for applications like speech recognition and image generation.

Putting it to the students

Dion Josh, an undergraduate at the University of Oxford, primarily uses AI to simplify complex chemistry concepts, especially for inorganic chemistry. He finds AI makes tutorials more productive by handling basic questions, allowing professors to address 'really intense questions.' Ellie Yates, a PhD student at the University of Sheffield, cautions against using AI for summarising research due to 'hallucinated' answers that can mislead research. She stresses the importance of AI literacy to understand its capabilities and limitations, and worries about students losing critical writing skills by over-relying on AI for lab reports. Conversely, Jasper Butcher, a protein design researcher, uses AI tools 'for everything,' including generating code and running experiments, which he believes accelerates scientific work. He also uses AI to create more creative problem sheets for students. However, Butcher expresses concern that AI might lead to students 'one-shotting' their degrees in isolation, reducing collaborative learning experiences.

Teaching in the time of AI

Fraser Scott, a chemistry education researcher at the University of Strathclyde, observes that AI use is widespread among undergraduate and PhD students, evolving too quickly for educators to fully adapt. He notes that the current response often focuses on safeguarding assessments rather than integrating AI as an educational tool. Scott argues that educators have a responsibility to teach students how to use generative AI appropriately, much like textbooks or calculators. He also highlights a 'dichotomy of use,' where educators themselves frequently use AI while often holding strong opinions against student usage. Students, like Dion Josh, feel AI is indispensable for university work, indicating a shift in learning paradigms.