Biochem Round 2

Teaching biochemistry for the second time this past semester was not as time-consuming as the first time two years ago. I spent 3-5 hours per week on class prep and updating the materials which was three times less than my first run. This was significantly more manageable given I had two larger sections of G-Chem 1 using a new textbook. For Biochem, I did not make large-scale changes to the course. The topical flow was similar and I mainly updated the slides and study guides. I made some changes to the in-class computational activities, exclusively using the Molstar/PDB viewer (and skipping Pymol). I added a protein-folding-prediction exercise given the ubiquity of AlphaFold-like tools. It also has a nice wow-factor!

 

This second pass, I was able to clear up some errors I made and confusion on my part about some of the more complex enzyme regulation involving kinases, in particular FBPases. I streamlined the enzyme kinetics so it would be less heavy math-wise, and I think I did a better job with carbohydrate nomenclature without getting stuck in the weeds. Those are the positives. The negatives are that I likely went faster and had a little more information on my slides when I should likely have done the opposite. I also went into more chemical detail because a third of my class were chemistry or biochemistry majors; in contrast I had less than fifteen percent of them the first time I taught it.

 

My class was fifty percent larger this time around, simply because there were more students enrolling in the course as numbers have rebounded post-pandemic. This probably made the largest difference because it means I help each student less individually. This was certainly true during in-class activities where the students work in pairs or small groups and I circulate. The majority of students never came to office hours, which didn’t help matters. My end-of-semester grade distribution was much wider and included some D’s and more C’s, and there was a surprising amount of nonsense answers on exams. That being said, many of the students still did well and two-thirds were in the A and B range, unlike the first time around when ninety percent earned A’s and B’s. It was an unusually small class and I was likely paying lots of attention to the students and their learning. By spending less time on the metacognitive aspects of my own teaching and focusing much of my time on G-Chem, I think I did a poorer job overall.

 

The end-of-semester course evaluations were not surprising. On the Likert scale questions, my ratings went down – as expected for a larger class with more students not doing so well in the course. There were the usual comments about the speed at which we went through the material and the density of the material. A couple of students thought the twice-a-week format (with two longer rather than three shorter classes per week) was exhausting, and I see their point even with my three-minute break mid-class. Students found the study guides the most helpful; again not surprising. The chemistry and biochemistry majors liked my chemical emphasis and details. The non-majors did not like it. One made comparisons to the other sections which had “more MCAT applications” and another felt that while the other sections “skimmed through a lot of topics”, our class “felt like we learned the whole damn book”.

 

I don’t know when I will get to teach the class again. Recent staffing changes in my department might preclude my teaching it again anytime soon. If there is a next time, I would consider not using a standard textbook now that I am more comfortable with the material. One problem with following the textbook somewhat closely (which is a reasonable thing to do when you’re teaching something for the first time or two) is that you can get lost in the details and forget the big picture. A couple of students commented that this is how they felt about my class. I think instead of opening with review of G-Chem concepts and launching into amino acids and proteins, maybe I can start with some big picture metabolism (not the weeds) before getting into the building block molecules. There’s a logic to biochemistry and I’d like the students to see this. I thought I was trying to emphasize this, but many students found these details bewildering possibly because I had not spent enough time on the big picture or I was too abstract.

 

My self-rating for Round 2 is that I was overall mediocre; I’m not sure I did a better job teaching the second time around even though I was clearly more comfortable with the material. Perhaps that was the problem; I let the curse of knowledge slip in, and spending less time on thinking about the class showed.

Student A.I. use: Fall 2025

The last two times I taught G-Chem 1, I briefly told students how a generative A.I. such as ChatGPT can be useful and what some of its limitations are. Last semester (Fall 2025), I made no mention of A.I. use in any of my classes until the last week of the term. I surveyed the students asking if they used A.I. in my class, how so, how often, and if they found it helpful. The questions were open-ended and students could answer (or decline to do so) in any way they wish. I prefaced by saying that I had no problem with A.I. use, and that their responses would help me provide guidance to my future classes. I taught two sections of G-Chem 1 and one section of Biochem. My musings on the results are mainly focused on G-Chem because of the larger class sizes.

 

In G-Chem, 12% of students said they did not use any A.I., while 88% did so. ChatGPT was by far the main source, with Gemini a distant second. (Other apps got only one or two mentions.) Only a small proportion of students said they used it a lot. Most used it sparingly or occasionally. A.I. was most often used shortly before exams (in conjunction with getting answers to my study guides) and on the stoichiometry unit where students wanted help on step-by-step calculations. From my limited tests, GPT-4o does noticeably better on stoichiometry than GPT-3 (which wasn’t very good) in providing a correct solution, although typically a verbose one.

 

Interestingly, a few students used the chatbot to recommend youtube videos to help them understand a topic. (Many students just use Google or go straight to youtube to look for such videos.) Most students said they found it helpful in “explaining” concepts or how to solve problems. Several students specifically said they used it to generate practice problems or to quiz themselves. One student said it helped them “decode” their notes and explain it in a simple way. Students said it was particularly helpful when they missed class, one even saying “I didn’t need to go to office hours… it gave me the answer from anywhere I liked.” While the majority of students said they found ChatGPT useful, a handful did not.

 

A number of students provided specific caveats in their usage. A student writes: “I would strongly recommend not heavily depending on it for homework, as it ends up being more harmful than beneficial. You must know how you obtained your answer, not just copy and paste.” Another student: “These models are constructive for learning as long as you use them productively and have them guide you instead of answering for you.” A student notes: “It was helpful, but some ideas it presented contradicted my notes, so I am not sure how accurate it is.” Another student: “While not always correct, I felt that it would usually get me started in the right direction to finish understanding the topic or solving the question on my own.” Interestingly, the students who made these types of comments were almost all students who earned A’s or B’s as their final grade. Also noteworthy, the 12% of students who did not use A.I. also earned A’s or B’s. (The average grade was in the C+ range so slightly less than 50% of the students earn A’s or B’s.) Of the students who used it sparingly or rarely, again these were the A or B students. This is perhaps not surprising. The students who knew the material felt less of a need to use A.I.

 

Since the best use of a generative A.I. is to generate test questions and study guides, I’m glad to see many students mention it in this way. Even more use it for explanations or answers which is more hit-or-miss, but I’m glad that students noticed this. Here’s one thoughtful student comment: “When it comes to studying equations, ChatGPT was very helpful because it showed me step-by-step how to solve it. I also used this model to create practice problems for me. In terms of elaborating the material from class, it was moderately helpful. It mostly gave me vague explanations.” This student also thought it was a limitation of the free version and mused that if they had used a paid version they may have had better results. One student would load the study guide in and then ask ChatGPT to provide timed quiz questions so that the student would feel like they were in an exam.

 

In Biochem, I saw similar trends: 15% of the students did not use A.I. (All three earned A’s and were among the top five.) There aren’t many math-related or calculation questions in Biochem so most of the students used it to clear up things they weren’t sure about, again usually pertaining to the study guides or my lecture slides (which I provide to the students). Since this is a smaller class, I’m not sure if any trends are significant.

 

My takeaways: Students are going to use A.I. in a chemistry class regardless of whether you have a policy or not. The majority of them already do so and feel that it is helpful, so they will keep doing so. The academically stronger students use it less, but likely because they feel they understand the material in class and are able to solve problems without outside help most of the time. Many students leverage the generative capabilities of a Large-Language-Model A.I. to generate test questions although whether they are generating sufficiently complex questions is less clear. Some students notice the weaknesses of A.I. answers yet still find it helpful as a guide. Students think A.I. helps to “simplify” some concept they are struggling with. Whether or not it is over-simplified is less clear. Students still gravitate to video explanations to supplement the text explanations of A.I., and youtube remains a key source for students.

Out of Book

In The Most Human Human, Brian Christian muses about his experience playing a “confederate” during the 2009 Loebner prize. It’s an annual Turing Test competition where entrants have designed artificial intelligence computer chatbots to mimic a human. A judge has a five-minute conversation over a computer terminal with a chatbot or an actual human being, and then has to decide which is which. As a “confederate” Brian is one of the humans. The chatbot that best fools the judges into thinking it’s a human is dubbed “The Most Human Computer”. The human that best convinces the judges of his or her humanity is “The most human human”.

 

Chapter 5 of Brian’s book, titled “Out of Book”, refers to where chess games at interesting. Chess openings and endings are often scripted. Over time a database builds up on effective opening moves and their variants. The same is true at the endgame where few pieces remain on the board and a brute-force analysis can determine who the winner will be. The Book refers to this wealth of knowledge in chess openings and endgames. It’s the midgame where things gets interesting, when players are forced into out-of-Book situations. Computer chess programs are loaded with The Book. All decent programs have them. What might make a program superior to others is how it handles the out-of-Book midgame. What makes a chess player a grandmaster is being able to successfully navigate the midgame.

 

At the novice level, memorizing more openings and endgames and practicing them a lot, often leads to victory over someone with a less prodigious memory. You get into a superior position by standing on the shoulders of the grandmaster giants who have gone before, and the game is about who blunders first by playing a known inferior move from the database. You don’t really need to understand the why behind the moves; you just need to execute them in the correct sequence. At the mastery level for humans, understanding the why is crucial to know when to effectively get out-of-Book and try to outplay your opponent with ingenuity. The challenge in playing against today’s computers is that they can store a huge Book and shrink the space between opening and ending. You might lose to a computer program before even getting out-of-Book.

 

All this makes me thinks of A.I. use in education. The majority of my students use chatbots to help them complete homework assignments and study for exams. They think it helps their learning, and that may be true some of the time, but I suspect it also leads to a dose of self-deception. They think they know something but when exam time comes (with no book or chatbot to consult), they don’t perform well. My exam questions should be no surprise based on what we cover in class and the study guides I provide, and the academically strong students have no problem doing well.

 

In contrast, I see a mishmash of nonsense written by the students who have no clue what’s going on.  They don’t know what they don’t know. If they made the effort to memorize the worked examples and explanations in class, they might do a tad better on the exams, but that’s not what’s happening. Instead, they cut-and-paste a question to pose to a chatbot, and read the answer thinking they understand it. Compounding the issue is that if you have little understanding of the subject matter, you are unable to tell if the chatbot answer is correct, wrong, or not-even-wrong. Large Language Model chatbots are designed to sound plausible. Their prodigious memory means they are likely to string together words that sound like the right answer. Maybe it is, maybe it isn’t, maybe it’s simply misleading and neither here-nor-there.

 

A.I. is hailed as a potential disruptor and savior of education. Its champions are the tech companies trotting out deals to hook the young early in the hopes that they will shell out money for premium access later. Schools and universities are stirred into a frenzy by FOMO vibes and guilt-tripped about not preparing their students for the upcoming A.I. revolution. You’ve gotta be able to access The Book. Everyone’s doing it! But like the novice chess players who consult a book to advance their moves without understanding the deeper reasons, novice chemistry students consult an A.I. chatbot and now think they have advanced their understanding of the subject matter.

 

I think that A.I. coupled with knowledge-expertise can be an excellent tool for discovery and pushing the frontiers of knowledge. There lies the out-of-Book realm, ripe for discovery. For novices, though, chatbot education only provides a shallow and self-deceptive “learning”. If the world is headed towards an idiocracy, this might not matter. What pains me is that the bifurcation between the haves and have-nots will continue to expand, and it may devolve into total anarchy or a totalitarian state of affairs.

 

Interspersed with the stories of computer chess, Brian concludes his chapter by thinking about the conversations between humans. There’s an opening and closing, mostly scripted, and a potential interesting middle where two people might learn something new about each other and themselves. In a brief chat with a stranger or an acquaintance, one might never stray out-of-Book. Many top chatbots in the Loebner prize were able to steer the conversation to stay in The Book, banal yet effective. It encouraged me to think about getting out-of-Book and become a better conversationalist. Maybe I can try out some new surprising out-of-Book lines with my students and make a better connection this new year!

PJ Adventures

This winter break, I read the first five books of Percy Jackson & the Olympians penned by Rick Riordan. It was one of my thoughtful students who recommended I read them; he thought I would enjoy the creativity of infusing Greek myth and magic into modern day life. PJ&O is suitably found in the Young Adult section of my local library. Each book is a romp; the action moves quickly and punctuated with plenty of humor.

 

I’d read a fair bit of Greek mythology and was familiar with the Labours of Heracles and The Odyssey, two key sources of the myriad characters that show up in PJ&O. But they show up in surprising circumstances and often in disguise. I also see many similarities to the Harry Potter series with a young protagonist, discovering his true identity and abilities, being thrown into adventures alongside his close friends. In each book, the kid grows a year older, as a prophecy looms towards its fulfilment. There’s an antagonist who is slowly gaining power and trying to resurrect himself.

 

There are several types of beings in PJ&O. Ordinary mortals having ordinary physical characteristics normally do not perceive magic because of the Mist, more about that later. Gods can change form and present themselves in different ways; they seem to have some physicality and can be hurt by magical weapons. Demi-gods are the offspring of gods and mortals and seem to have the physicality of mortals but special characteristics depending on who their god-parent is. Percy, the son of Poseidon, can manipulate water. Monsters cannot be perceived by most ordinary mortals and their true form is hidden by the Mist. They turn into dust when slain with magical weapons and can be re-formed at a later time.

 

Are the gods and monsters made of different physical elements? It’s unclear. They certainly interact with earth and water. But at least some of them can change shape and form, so maybe it’s a different underlying physicality. The books hint that belief in the gods seems to sustain their existence. There are ordinary mortals who can perceive the world of myth and see through the Mist. How and why is also unclear. Is there a color of magic that can only be seen by some? Few are born with the ability and it’s unclear how such a trait arises and whether it can be passed down genetically. The scientist in me is curious about such details, but the stories skip right to the action. You suspend belief and go with the flow. At least that’s what Percy does as he blunders through one adventure after another.

 

The Mist is interesting. Is it a matter of physical perception or is it that mortal brains can only interpret what is physically familiar and therefore cannot perceive the magic. If you saw something so stupendous it defied belief, your mind might rationalize it into something that fits into your world view. The alternative is to go mad, a not-unreasonable possibility. There is no Magical Squad that goes around obliviating the memories of otherworldly sights in PJ&O. Ultimately, like any good fictional story successfully immerses you into its supernatural world so that the physical oddities aren’t as important. The themes of friendship, loyalty, love, truth, anchor the story and that seems to be what most of us mortals care about. The abstract takes precedence over the physical surface, and those of us constantly sweating the physical details are the odd ones out among mere mortals.

Concreteness

As a theorist, I’m very comfortable thinking abstractly. I believe this has helped me gain expertise in my field, evidenced by being able to “see” the deep features when problem-solving, and not be distracted by surface-level features. I also believe that one of the biggest challenges of being a teacher in my area of expertise is the curse of knowledge. I cannot “unsee” the deep features of chemistry, but neither can I bestow my mind’s-eye-sight to the novice. It’s not a concrete gift I can give.

 

My job is to help move students along the path from novice to expert. I’m trying to help students see chemistry the way I do. I have a limited amount of time to carry out this task. Similarly, my students have a limited amount of time to learn a body of material before they take the final exam that assesses their knowledge. My strategy is to make sure students know definitions and problem-solving protocols. Then we go through several different examples where I try repeatedly to point out the common deep structure of the problem even though the surface features are diverse. One challenge is baseline knowledge. The expert has tons of it; the student has little. When you don’t know much, you can only grasp in vain at the surface features that seem more concrete, even though they are less important for solving a problem.

 

I’ve been wondering if, over the years, my theoretical bent has quietly asserted itself more and more in my pedagogy, favoring the abstract over the concrete. Yes, I do want students to be able to think abstractly. This is particularly important in chemistry where understanding what is happening in the tiny nanoscale regime requires abstract imagination. We can’t see atoms or wavefunctions or chemical bonds or dipole attractions. Chemists are always imagining what is invisible to the naked eye, because the heart of chemistry comes from making and breaking chemical bonds. I talk about balls and sticks and springs and waves. I ask students to imagine such entities. I draw graphs. I write equations. I try to include real-world tidbits of chemistry in the mundane that’s all around us. I find myself excited just thinking about such things. But they are all still in my mind’s eye.

 

Do the students see things the way I do? They can tell that I’m knowledgeable and enthusiastic about what I’m teaching (evidenced by comments in student evaluations). But this doesn’t mean they are learning how to think chemically. Humans haven’t been around for long, and for most of human evolution, learning has been visceral. Concrete. Physical. Not so much in the abstract realm. I think I need to bring more concreteness to the teaching and learning of chemistry, and counter my strong bent towards the abstract. Not that the abstract is unimportant – it’s still vital! – but to do better in helping novice students learn chemistry. The best science writers who are able to convey complex ideas to their novice readers employ the visceral in their language. Blood and guts and more. Your mind’s eye is not the only think activated. You can almost feel, smell, taste even though you’re just scanning words on a page. There’s a concreteness to it.

 

How will I remind myself to keep working on this? Over time I am likely to revert back to emphasizing the theoretical and abstract. Maybe I need a (small) concrete block. A physical brick might be sufficient. I could muse more about this topic, but instead maybe I should take the good first step of getting up off my chair and away from my computer and locate said concrete brick.

Thermodynamic Warfare

Sometimes you just need an equation. Even if you’re writing a “popular” book where equations are discouraged. No, I’m not talking about E = mc² that shows up just to be associated with someone famous.

 

Karen Lloyd, the author of Intraterrestials is a superbly engaging writer. Her book is littered with well-chosen metaphors and analogies to explain how scientists study organisms hiding away deep in the subsurface of our planet. But I appreciate that the professor in her wants to teach her readers something useful and profound. She chose the Gibbs Free Energy equation:

 

I explain this equation every year to my G-Chem 2 students when we discuss thermodynamics. Lloyd does so with much more flair. In chapter 6 (“Breathing Rocks”) she opens with her life-harrowing yet exhilarating experience of sampling for microbes at a volcano caldera in Chile. After the scenario of physical heat and motion (get your samples quick so you don’t die!), she launches into the heat and motion associated with thermodynamics. She explains the Gibbs equation with colorful examples such as roller coasters and hand warmers. I could quote her for several paragraphs, but instead, I recommend you read her book in full. It’s a page-turner!

 

The crux is that subsurface organisms, unable to get their energy from the sun (like photosynthetic organisms) or eat food they can metabolize with oxygen (like most of us do) respire by breathing rocks. They eke out a low-energy lifestyle turning carbon dioxide (from carbonate rocks) into biomass with the help of nitrogen and sulfur compounds, also found in minerals. Such chemical reactions typically have a small negative delta-G, so you can’t get much energy from them, but they are still energetically “downhill” and thus favorable.

 

But things get even weirder when there’s competition for resources. In chapter 7 (“Life on the Edge”), Lloyd sets up the discussion with another vignette in the cold of Svalbard, Norway, where she is cutting sediment cores dug up for her research. While doing so, she ponders life in the cold Arctic with tremendously varying sunlight. And now I have to quote her: “But intraterrestials don’t care about sunlight or cold. They care about delta-G.” And unlike our familiar surface microbes that “secrete deadly antibotics, hoard nutrients, and grow ultrafast to get ahead” and beat out the competition, subsurface microbes have an additional weapon: “If one microbe’s delta-G is better than another one’s then the first microbe can asphyxiate the second.”

 

I was delighted that Lloyd chose sulfate-breathing microbes to illustrate her point since I’m studying the role of sulfur at the origin of life in competing autocatalytic cycles. She delves into the equation, now focusing on how delta-G can be modulated by Q, the reaction quotient. It’s counterproductive for them to grow big fast because that decreases sulfate diffusion in their cellular bodies. Releasing antibotics is also bad because it would kill symbiotic species in addition to its direct competitors. Molecular hydrogen is a required “food”; you can’t stop your competitors from getting it, but you can hoard enough so that you still have a borderline negative delta-G, while forcing the delta-G of your competitors to turn positive (“uphill”, energetically unfavorable) so their metabolism no longer yields energy and they die.

 

Lloyd writes: “Like a shipwrecked sailor dying of dehydration while surrounded by water, these microbes expire with their food right in front of them. Sulfate reducers win because they take the whole system to the bitter edge of their own thermodynamic capabilities, which pushes everyone else off the cliff.” Ugh. That’s war. But then as the amount of sulfate reduces, the sulfate reducers now face extinction. As they die off, their competitors (often methanogens) can access more hydrogen once again and a revolution takes place.

 

Life gets weirder still. Some microbes (methanogens!) can reverse their food and waste as delta-G switches, so they can keep eking energy. Others ferment; in Lloyd’s words: “takes one slice out of the pie and puts the rest back into the fridge for others to eat later. It’s very polite. Because of this restrained eating, fermentation ends up being one of the lowest-energy processes known to support life.” The low-energy living of intraterrestials suggests that they might live a long, long time without reproducing. It’s immortality of a sort, though not the one we might desire.

 

Reading Lloyd’s book rejuvenated my excitement about my research projects. It also reminded me that I want to be a better teacher and communicator. While this was a library book, I will be purchasing my own copy because it deserves re-reading, and I still need to delve into the scientific papers listed in the references!

 

Optimizing Learning and Attention

“Learning is the slow, ponderous and beautiful Galapagos tortoise, and online content is the invasive predator which will inevitably drive it to extinction.”

 

This quote from Daisy Christodolou’s article (“Why education can never be fun”) really struck me. Addictive online games and videos, she argues, only need to optimize in one dimension (fun!) while apps which might actually increase learning need to optimize in two dimensions (fun and learning). Sometimes the two parameters oppose each other. For many learners, this will be the case when learning material involving math. Your most engaging learning app will never beat the app that only needs to optimize for holding one’s attention. She writes: “However much fun you make learning, someone else will use the same techniques minus the constraint of learning. You are in an arms race where you have one arm tied behind your back.”

 

The subject that I teach, chemistry, is hard. While there is some math involved in the introductory levels, what is more challenging is the abstraction of having to juggle three aspects simultaneously – known as Johnstone’s Triangle. Chemistry is abstract by nature. We’re trying to explain everything in terms of tiny things that we can’t see. Hence, we have to think about chemistry using models. None are complete-in-itself; expertise in chemistry involves fluidly moving amongst a panoply of such models. This is not easy for the novice learner.

 

As more tasks become facile with the aid of technology, we humans who outsource our thinking to such machines will become less adept at the basics. In many instances, that might be okay. I have no interest in going back to the stone age, and I’m glad I was not born a century ago. I like my technology-aided creature comforts. I even blog to offload some of my cognitive effort. But something is lost in the process. It’s okay if what I’m losing isn’t crucial, but if it’s something important such as basic numeracy, facility with language, concentration skills, or thinking deeply and actively, then this is a problem. It’s possible that humankind is heading towards a general idiocracy with a small number of elites controlling the levers. Or it could be worse – the oligarchs might be just richer and power-wielding members of the idiocracy.

 

Learning science and math is not easy, but I think it is important to understanding the world we live in. It will sometimes be a slog. No pain, no gain. As educators, we should try to make our subject matter interesting and relevant, but there is only so much you can do to gamify your course before running into the hard reality of actually learning difficult material. Passively consuming short-form videos from creators who are optimizing eyeballs may give you a false sense that you know something. But that knowledge might be superficial at best, or possible misleading, or simply wrong. And if you don’t have the basic knowledge, you won’t be able to tell when you’re consuming crap.

 

After thinking about this, I took a moment to think more carefully about some seemingly basic concepts in chemical bonding that are much more than meets the eye. I needed to remind myself of things that I had read a year or two or more, but have since forgotten because I haven’t practiced the effortful cognition needed to retain some of these ideas. But if I don’t keep making the hard effort, I will slowly but surely be joining the idiocracy and not even realize it.