There are many burning topics in the field of STEM education, but in my opinion, none of them is as controversial and unpredictably interesting as AI and its role in student learning. This is especially relevant to STEM education. Of course, we (educators) have been using AI for years even if few of us realized that we did. For example, the beginnings of AI could be seen in STEM homework systems, such as WebAssign or Mastering Physics, when the students were asked to solve problems online and the system attempted to scaffold them. Then Khan Academy and other more advanced STEM learning systems started considering AI’s applications for their platforms. However, what we often neglect to think about is how AI will inevitably change how our students learn outside of the classroom and how we engage them during our lessons. This is what I keep thinking about while teaching future STEM teachers, doing my research or preparing for my classes with K-12 students or university faculty from around the world.

I would like to outline here three examples, where I think AI will change how we teach STEM. These examples focus on helping STEM teachers reach outside of their comfort zone and expand their knowledge into other STEM fields.

1. Identifying and connecting BIG IDEAS in STEM: While many of us teach STEM, most of us are mathematics, physics, biology, chemistry, computer science, or geology teachers, as opposed to STEM teachers. As a result, while we might have strong ideas in one area, we might not feel as comfortable across other STEM fields. As a result, teaching BIG IDEAS across STEM might be very challenging (this is what BC Curriculum prescribes us to do). I think this is very AI might help educators. For example, I can ask ChatGPT to help me identify big discoveries in life sciences in 19th century:

My prompt: Big discoveries in life sciences in 19th century

ChatGPT

The 19th century was a pivotal era for discoveries in the life sciences, marked by significant advancements and groundbreaking work that laid the foundation for modern biology and medicine. Some of the most notable discoveries and developments include:

1. Cell Theory (1839): Matthias Schleiden and Theodor Schwann proposed the cell theory, stating that all living organisms are composed of cells and that the cell is the basic unit of life. This theory revolutionized biological sciences and is a fundamental concept in biology.
2. Theory of Evolution by Natural Selection (1859): Charles Darwin and Alfred Russel Wallace independently formulated the theory of evolution by natural selection. Darwin’s publication of “On the Origin of Species” in 1859 provided a unifying theory of life sciences, explaining how diverse species evolved over time from common ancestors.
3. Discovery of Microorganisms (19th Century): Throughout the 19th century, advances in microscopy led to the discovery and study of microorganisms. Scientists like Louis Pasteur and Robert Koch made significant contributions, including the development of germ theory which demonstrated that many diseases are caused by microorganisms.
4. Mendelian Genetics (1865): Gregor Mendel, through his work on pea plants, discovered the fundamental laws of inheritance, laying the groundwork for the field of genetics. His findings on dominant and recessive traits were initially overlooked but later recognized as the basis of genetic inheritance.
5. Antisepsis in Surgery (1867): Joseph Lister pioneered the use of carbolic acid as an antiseptic, drastically reducing surgical infections and making surgery safer for patients. This practice was one of the early steps towards modern antiseptic and aseptic techniques in medicine.
6. Vaccination Advances (1796-19th Century): Although Edward Jenner’s development of the smallpox vaccine occurred at the end of the 18th century (1796), the 19th century saw significant advancements and wider adoption of vaccination as a method to prevent infectious diseases.
7. X-rays (1895): Wilhelm Conrad Röntgen discovered X-rays, a form of electromagnetic radiation, which had a profound impact on medical diagnosis and treatment, particularly in imaging techniques.
8. Discovery of Hormones (Late 19th Century): The concept and discovery of hormones began in the late 19th century, laying the foundation for the field of endocrinology. The isolation of secretin in 1902 by William Bayliss and Ernest Starling marked a significant milestone, but the groundwork was laid in the 19th century.

These discoveries, among others, fundamentally changed our understanding of life, health, and disease, and set the stage for further advancements in the 20th century and beyond.

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As a STEM teacher, I can build on this starting point and use ChatGPT as a collaborator in my lesson planning when my knowledge of the subject might be limited. Of course, I cannot trust ChatGPT and I have to check what it gives me, but this is a great starting point. I can also ask ChatGPT directly about the connections of different STEM fields. This is my second example.

2. Lesson planning with ChatGPT – Identifying connections across STEM fields:

My prompt: Examples of Connections between mathematics biology and physics

ChatGPT

The connections between mathematics, biology, and physics are profound and varied, reflecting the interdisciplinary nature of modern science. Here are some notable examples illustrating these connections:

1. Population Dynamics and Ecology (Mathematics and Biology): Mathematical models, especially differential equations, are extensively used to understand population dynamics in ecology. The Lotka-Volterra equations, for example, model the interactions between predator and prey populations, helping ecologists understand complex biological interactions.
2. Biophysics and Molecular Dynamics (Physics and Biology): Biophysics applies the principles of physics to study biological systems. This includes understanding the physical properties of molecules like DNA and proteins, and how physical forces impact biological processes at the molecular level.
3. Statistical Mechanics and Thermodynamics (Physics and Biology): Statistical mechanics and thermodynamics are crucial in understanding biological processes such as protein folding, enzyme activity, and cellular metabolism. These physics concepts help explain how biological systems maintain order and function under various conditions.
4. Bioinformatics and Computational Biology (Mathematics and Biology): The analysis of biological data often requires sophisticated mathematical algorithms. Bioinformatics, for instance, involves the use of mathematics and computer science to analyze and interpret biological data, like genetic sequences.
5. Neuroscience and Neural Networks (Mathematics, Biology, and Physics): The study of the brain and nervous system is inherently interdisciplinary. Mathematical models and physical principles are used to understand neural network behavior, synaptic transmission, and brain function.
6. Epidemiology and Disease Modeling (Mathematics and Biology): Mathematical modeling is essential in epidemiology for predicting the spread of diseases, understanding the dynamics of epidemics, and informing public health strategies.
7. Quantum Biology (Physics and Biology): An emerging field, quantum biology investigates the possible role of quantum phenomena (like quantum coherence and tunneling) in biological processes, such as photosynthesis, enzyme action, and even animal navigation.
8. Mathematical Physiology (Mathematics and Biology): This field applies mathematical techniques to understand physiological processes. For example, the Hodgkin-Huxley model uses differential equations to describe how action potentials in neurons are initiated and propagated.
9. Fluid Dynamics in Biology (Physics and Biology): The principles of fluid dynamics are applied in understanding blood flow in the circulatory system, air flow in respiratory systems, and even in the movement of microorganisms.

These examples illustrate how mathematics and physics not only provide tools and frameworks for understanding biological phenomena but also deepen our fundamental understanding of life at various scales, from molecular to ecological.

3. AI use in Project-Based Instruction

As a teacher, I can consider which one of these examples might be of interest to my students and which one I might need to expand on. Finally, Chat GPT might help my students and myself when working on long-term projects.

Challenge of AI: Can we trust it and should we? – Beyond cheating.

While many educators focus on student cheating with AI, I am more concerned with students trusting it, without being able to critically evaluate the trustworthiness of AI’s answers. At the same time, AI can also help develop critical thinking skills if students are asked to evaluate ChatGPT’s response critically and identify when it is hallucinating. This is especially important today when  many students fail to realize that one cannot trust AI, but has to be vigilant when relying on the information it supplies. There are many interesting articles on the topic: https://zapier.com/blog/ai-hallucinations/ .

In summary, I think AI has a lot of potential, it can open many opportunities for creative STEM educators and creative students. However, they are also possibilities that if STEM educators create assignments that can be done with ChatGPT alone, without any higher order thinking requirements, then cheating will be inevitable. For example, finding information about people or writing a “standard essay” about basic concepts can be done with ChatGPT alone. This makes teaching more challenging, as we have to think what are the skills that we want the students to acquire that will be valuable for the, in the future? So while ChatGPT in particular and AI in general made our lives more interesting, it also added a lot of challenges to educators that we all have to think how to address.

For more information:

Emsley, R. (2023). ChatGPT: these are not hallucinations – they’re fabrications and falsifications. Schizophrenia, 9(1), 52. https://doi.org/10.1038/s41537-023-00379-4 

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 1-12. https://doi.org/10.1080/14703297.2023.2190148

Cardona, M. A., Rodríguez, R. J., & Ishmael, K. (2023). Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations. W. US Department of Education, DC.

Barnett, T. R. (2023). The Official ChatGPT Guide for Teachers: A Must-Read Guide to Enhancing Classroom Learning and Addressing Ethical Concerns.

Posted: 01|20|24 at 6:29 pm. Filed under: General, General teaching, Interactivity in the classroom.
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