My current stance when teaching second-year organic chemistry laboratory is that students are discouraged to use generative artificial intelligence (such as ChatGPT).
The syllabus “add-on” I would be recommended to use is as follows (in italics), and I will add commentary that relates specifically to the course.
This course includes the independent development and practice of specific skills, such as:
- Apply the theory from CHEM 266 to the reactions executed in CHEM 266L.
- Complete laboratory reports ethically by using data generated by you or your lab partner, crediting all sources of information.
- Demonstrate independent thinking by answering questions in your own words, showing critical reflection beyond the initial description of observations and facts.
- Gain experience in drawing accurate skeletal formulas (line-bond diagrams) of molecules.
- Answer conceptual questions by connecting ideas from the lecture to observations and principles in the laboratory.
- Demonstrate ability to draw reaction mechanisms.
Therefore, the use of Generative artificial intelligence (GenAI) trained using large language models (LLM) or other methods to produce text, images, music, or code, like Chat GPT, DALL-E, or GitHub CoPilot, is not permitted in this class. Unauthorized use in this course, such as running course materials through GenAI or using GenAI to complete a course assessment is considered a violation of Policy 71 (plagiarism or unauthorized aids or assistance). Work produced with the assistance of AI tools does not represent the author’s original work and is therefore in violation of the fundamental values of academic integrity including honesty, trust, respect, fairness, responsibility and courage (ICAI, n.d.).
I consider second-year courses to still be teaching students fundamentals in learning such as:
- Efficient use of provided course materials: I curate all the laboratory content myself and know the lecture notes! I even have experience teaching both semesters of first year and can guide you to find information. When I write lab questions, I often go back into the lab manual and course notes to see whether you have the tools to answer each question.
- Ability to connect concepts across modules within the course and with pre-requisite knowledge: This is part of metacognition, part of learning is not “discarding” knowledge acquired in a course, module or experiment. It is often all connected! Without integrating prior experiences you had as a learner, it is going to be much more difficult to progress into your profession of choice.
- Become a self-regulated and self-directed learner: As you progress into your career, it will be a lot less about grades and external feedback and more about your own internal critique of your work. Often in workplaces your training will be self-directed as opposed to having a direct supervisor who will walk you through new processes step by step. Knowing when your output is perhaps not ideal and reaching for help after showing the steps you took to resolve on your own will give a great impression on people you work with.
We now have information available at our fingertips all the time. I do really appreciate being able to quickly google information, but sometimes with specific topics knowing where to look avoid time wasted. I often hear from students “I spent hours researching this question online.” While doing some research is great, at this level I would prefer students to re-read the background of the experiment and ask for support versus spending hours on research. First, consider your information literacy skills: Are you considering whether the resources you are reading are written by an expert? How proficient are you with organic chemistry to vet whether the statements are correct? This is not meant as a dig to students: there is a lot of misinformation on the internet. For example, several websites mention that organic solvents used in liquid-liquid extraction must be non-polar when this is absolutely untrue!
With generative AI, I worry about “hallucinations” from the LLM and students not being able to recognize that the output is wrong. Recently, the newly released Google AI recommended to add glue to pizza sauce if the cheese and toppings are not sticking well (https://www.forbes.com/sites/jackkelly/2024/05/31/google-ai-glue-to-pizza-viral-blunders/). Most people would have enough life experiences to know that is a poor recommendation! But what if instead an AI model said that relative solubility in the mobile phase and distinguishable retention factors (RF values) were the two most important properties to consider when choosing thin layer chromatography to identify molecules, would you think to ask more? What about limitations related to volatile compounds and the of visualization methods used in the lab (usually UV light)? Are other properties important to consider?
The less you know about a topic, the more challenging research can be and this is why it may be best to use provided resources or begin with them to avoid getting lost in incomplete or conflicting information.
You should be prepared to show your work. To demonstrate your learning, you should keep your rough notes, including research notes, brainstorming, and drafting notes. You may be asked to submit these notes along with earlier drafts of their work, either through saved drafts or saved versions of a document. If the use of GenAI is suspected where not permitted, you may be asked to meet with your instructor or TA to provide explanations to support the submitted material as being your original work. Through this process, if you have not sufficiently supported your work, academic misconduct allegations may be brought to the Associate Dean.
This part of the statement I care about less. In my courses, the assignments are smaller scale, so I do not expect students to need extensive draft versions or preliminary work. I also do not have time to go into a witch hunt about how student got to their submitted version of a report. But this highlights one of the issues in using generative AI early in your learning process: if you are using tools to write short paragraphs and direct your development of arguments and thesis statements, are you learning those core skills? Will you have the pre-requisite knowledge to tackle longer technical reports or research projects? Learning to write is challenging, and the only way to improve is to write often! I am particularly sensitive to ESL and first-generation learners who may not have many resources in their lives to help with those skills. Part of this blog is to encourage me to write more often about my thoughts on teaching. I encourage you to use the Writing Centre and the library (linked below), they offer many programs, some even on a drop-in basis and can support you.
In addition, you should be aware that the legal/copyright status of generative AI inputs and outputs is unclear. More information is available from the Copyright Advisory Committee: https://uwaterloo.ca/copyright-at-waterloo/teaching/generative-artificial-intelligence
The lack of transparency in LLM development is one of my biggest concerns. While we know the large companies working towards their refinement, there are many ethical issues that bring me pause:
- AI also is victim to the worst biases in society (sexism, racism and in particular colourism)
- Workers doing some of the “grunt work” such as data labeling are largely in lower paid positions, gig workers or contractors without much labor rights. They can also be exposed to highly traumatic content (images and description of violence for example) without support.
- Just as any source, LLM are not completely neutral, and it is harder to know whether there is a hidden agenda. This could end up creating situations like algorithms on video streaming platform showing content that helps radicalize users.
- Storing and using large amount of information is environmentally taxing. Server clusters use large amounts of power and water to operate. With the context of a worsening climate crisis, I worry about using AI when we do not have the resources to spare.
These ethical issues could seem disconnected from organic chemistry, but any tool we use should be vetted on whether we support their operation principles. I liken this to learning more about sustainability to improve my experiments from “classics in teaching basics of organic chemistry” to sustainable experiments preparing the work force of tomorrow.
Students are encouraged to reach out to campus supports if they need help with their coursework including:
- Student Success Office for help with skills like notetaking and time management
- Writing and Communication Centre for assignments with writing or presentations
- AccessAbility Services for documented accommodations
- Library for research-based assignments
I highly recommend the above resources! The University offers so many options to help you develop metacognitive skills and life-long learning skills that will carry you through your career. I have been learning/practicing organic chemistry now for half of my life (17 years) and teaching it in some form since 2016 and I still learn at work on a nearly daily basis!