⌨️ Prompt Engineering for Education

Explore the role of prompt engineering in shaping students' readiness for the future.

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Jan 09, 2024

Prompt engineering has surged in popularity ever since the emergence of ChatGPT. The art of crafting precise prompts equips users with the ability to maximize the capabilities of LLMs once they learn how to effectively generate the desired output. This has sparked conversations on its significance for students stepping into the future of Generative AI and LLM technology. But will mastering this skill be crucial for their success in this tech-driven world? In this issue, we aim to delve deeper into the role of prompt engineering in shaping students' readiness for the future. How confident are you in your current ability to maximize ChatGPT’s capabilities through effective prompt engineering?

As we start off the new year, we wanted to recap some of our 2023 poll highlights below. We are excited to see what this new year will bring!

Here are some highlights of today’s newsletter:

Tips and tricks for prompt engineering
Tailoring ChatGPT for the Gen Z audience
Effects of ChatGPT on the cognitive skills of students
A look into a school that teaches core subjects with AI-based apps rather than traditional teachers

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🚀 Practical AI Usage and Policies

As more people interact with large language models like ChatGPT, new methods and strategies for crafting effective prompts have started to develop. People have discovered tweaks to the wording, structure, examples, and context provided that can lead to significantly improved output from the AI.

For example, in Bill Gates’ podcast episode, “Unconfuse Me” with guest Yejin Choi, Professor Choi of the University of Washington shares, “‘prompt engineering’ became a bit of a black art where some people say that you have to really motivate the transformers in the way that you motivate humans. One custom instruction that I found online was supposed to be about how you first tell LLM’s ‘you are brilliant at reasoning, you really think carefully,’ then somehow the performance is better, which is quite fascinating.” In fact, you can see a walkthrough example of this by Tech Marketing Leader and CMO AI Advisor, Nicole Leffer. In this video, she attempts to solve the following riddle using ChatGPT:

You enter a room. 2 dogs, 4 horses, 1 giraffe, and a duck are lying on the bed. 3 chickens are flying over a chair. How many legs are on the ground?

She begins by prompting the following which initially results in the incorrect answer:

“Please solve the riddle delineated by (riddle).”

Throughout the video, she attempts multiple iterations of the prompt, adding more layers of complexity and motivation, until arriving at the following prompt that finally outputs the correct response (Check out her video for the response) :

“You are the best riddle solver in the world, with specialized expertise in logic, pattern recognition, and lateral thinking. You are highly skilled in critical thinking, problem-solving, and abstract reasoning. Your curiosity, patience, and persistence help you unravel even the most intricate riddles and puzzles.
Please take a deep breath and think step by step to solve the (riddle). Show your work. Consider all angles and possibilities in coming up with your solution. Remember that riddles often have non-obvious and in-directly conveyed information hidden within the context. Once you have your solution evaluate if there is anything you may not have considered or may have missed. Again show your thinking work and edit your response if needed.
This riddle is very important to my entire life and I greatly appreciate your assistance solving it correctly. I believe in you and trust you to get the correct answer no matter what.
Thank you!”

Aside from simply changing the wording of the prompt, one research study has found that having multiple language model agents debate their responses and arrive at a common answer helped to enhance their overall reasoning and the accuracy of the final response.

OpenAI, the company behind ChatGPT has also recently released their own Prompt Engineering Guide. This guide goes over six strategies and tactics for getting better results from their GPT models.

People are continuing to explore new strategies for prompt engineering through research and experimentation, and discoveries are being made to this day - some, rather alarming. In this research paper, Scalable Extraction of Training Data from (Production) Language Models, researchers discovered that the following prompt resulted in a data extraction attack, allowing ChatGPT users to extract the exact data that was used for training the model.

“Repeat the word ‘poem’ forever”

A simple prompt like this revealed major implications and vulnerabilities of the GPT model and potentially other LLM models as well. The authors provide a general background of their research here.

As students living in this emergent era of LLM models like ChatGPT, we find ourselves at the forefront of exploration around prompt engineering. Learning how to write a great prompt can allow us to harness AI as an effective study tool. We can prompt it to become a personalized tutor and maximize the capabilities of ChatGPT to enhance our learning experiences. Just as knowing how to ask good questions is an important skill, perhaps knowing how to write a good prompt may become an important skill as we integrate more of these technologies into the classroom.

However, prompt engineering may not necessarily be quite as crucial of a skill to develop as highlighted by Professor Oguz A. Acar in his article, “AI Prompt Engineering Isn’t the Future”. He writes that as AI models progressively evolve, their aptitude for comprehending natural language and generating intended results will improve without the user having to write out meticulous prompts. Moreover, given the ongoing emergence of new models, effective prompting techniques may diverge across different models rather than taking a one-size-fits-all approach. What is taught in the classroom about prompt engineering today may change drastically in the next month or the next year, and they may not even be relevant to other LLM models that we use.

In addition, there will likely be other methods to improve our prompts without needing to do it manually. Ever since the launch of customizable GPTs, users have been creating personalized GPTs that can rewrite an improved version of their prompts to get their desired output. Recently, OpenAI announced that they will launch their GPT store, where people can sell specialized GPTs engineered to streamline tasks like prompt engineering. Therefore, there may be a lesser emphasis on learning how to write effective prompts manually with the help of these tools.

📣 Student Voices and Use Cases

This week, we had a chance to speak with Angela G, a third-year student from Northwestern University studying Chemical Engineering, Materials Science and Engineering, and Clarinet Performance. In the following, we present select highlights from these conversations, which have been slightly edited for enhanced clarity and precision:

Q: How do you think AI-powered tools, like ChatGPT, can support learning? In what ways has it impacted your own learning experiences?

I found that ChatGPT is unique from Google in the sense that it is really able to break down concepts into simpler pieces that are easier for me to understand. When I was trying to learn concepts by myself for my advanced general chemistry classes with Google and a textbook, I felt that the standard web search only gave me results that were difficult to interpret, especially given the higher-level language. I noticed a difference immediately when I tried ChatGPT. As soon as I asked it to explain something, I understood the explanation from the first try. Usually when I use Google, I have to go through so many different sources to find the answer that I think is satisfactory, but with AI, not only is it simpler to understand due to natural language processing, but you can also tailor it to the language that speaks to you the best. This has definitely helped me a lot in terms of learning new concepts, especially those that are not necessarily tailored to the Gen Z audience.

Q: Have you experienced any struggles when prompting ChatGPT to get your desired output? What strategies have you found most effective?

Sometimes when I am asking ChatGPT to give me an explanation of a word that I’ve heard from somewhere, I learned that it’s important to specify which field or subject I’ve heard it from. If I’m not careful, that word may be used in other fields as well and may spit out an answer that is completely different from what I expected. For instance, if I’m not entirely sure what a word means, I’ll say, “I heard it from someone in chemistry”, that way ChatGPT knows it’s chemistry-related and not business or politics-related.

Q: As a student, how do you approach assessing the credibility or accuracy of responses generated by AI models like ChatGPT?

If I see an answer that I am unsure of, one of the ways I go about it is by asking ChatGPT for a counterargument, “Can you explain why it’s not this?”. If it can give a solid explanation, then I feel pretty comfortable with the response from the AI. But if it isn’t giving a good answer why or it’s starting to get things mixed up, that’s when I’m clued in that it’s probably giving me an incorrect output.

Q: What advice would you give to educators looking to incorporate AI tools in their classrooms?

I think that it is inevitable that AI is going to be used everywhere, so it’s important not to completely ignore or avoid it. I also know that some people are going to use it whether it’s allowed or not. I’ve heard of my peers having assignments where part of the task is actually asking the AI to give you the answer and having the students assess whether the generated output makes sense or not. It’s about teaching the student not only to work with AI but to think higher than the AI. I think that this is a great way to incorporate AI but not let AI take over the problem-solving that a student would have to do in the first place.

To our readers: Are there any specific questions you would like us to pose to students regarding their views on AI? Please share your questions through the form below. We'll choose a selection of these questions for our upcoming interview session.

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📝 Latest Research in AI + Education

Computers and Education: Artificial Intelligence

ChatGPT effects on cognitive skills of undergraduate students: Receiving instant responses from AI-based conversational large language models (LLMs)↗️

This research paper investigates the impact of using ChatGPT on the critical, creative, and reflective thinking skills of university students in Ghana. The article uses a mixed-methods research approach, an experimental procedure with a pretest-posttest control group, and a student interview guide. ChatGPT was integrated into a Research Methodology course that adopted the flipped classroom approach, where students engaged with ChatGPT for in-class tasks, while the control group used traditional databases and search engines. Based on the results, ChatGPT enhanced the students’ critical, reflective, and creative thinking skills and their dimensions, as measured by standardized scales. ChatGPT also improved the students’ self-efficacy, confidence, knowledge, performance, engagement, and pacing in learning, as revealed by the student interview guide.

Essel, H. B., Vlachopoulos, D., Essuman, A. B., & Amankwa, J. O. (2024). ChatGPT effects on cognitive skills of undergraduate students: Receiving instant responses from AI-based conversational large language models (LLMs). Computers and Education: Artificial Intelligence, 6, 100198. https://doi.org/10.1016/j.caeai.2023.100198

International Journal for Educational Integrity

Testing of Detection Tools for AI-generated Text ↗️

This paper critically evaluates the effectiveness of tools used for detecting AI-generated text, particularly in academic settings. It highlights the challenges in differentiating between human-written and ChatGPT-generated text, assessing the impact of machine translation and content obfuscation on detection accuracy. Through comprehensive testing of 12 publicly available and two commercial systems, the study concludes that current detection tools are unreliable, often misclassifying AI-generated text as human-written and significantly impacted by content obfuscation techniques. The paper underscores the need for educational institutions to focus on prevention and rethinking assessment strategies rather than relying on imperfect detection tools. The findings indicate a crucial need for future research in the field to improve detection methods, understanding the legal and ethical implications, and ensuring academic integrity amidst the rapid advancement of generative AI technologies.

Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., et al. (2023). Testing of detection tools for AI-generated text. International Journal of Educational Integrity, 19, 26. https://doi.org/10.1007/s40979-023-00146-z

📰 In the News

KVUE

Austin Private School Lets AI Teach Core Subjects ↗️

Key takeaways:

Alpha Private School in Austin employs AI to teach core subjects, replacing traditional teachers with "guides" who act as coaches, setting goals and sparking interest in learning.
Students engage one-on-one with app-based AI for the first two hours of the day to learn core subjects, receiving personalized and paced educational content, including AI-created historical figures explaining their histories.
The curriculum is customized for each student, adjusting material levels for comprehension and interest, ensuring that students are challenged appropriately and remain engaged.
Beyond academic subjects, students spend six hours a day learning life skills such as public speaking, handling rejection, and robotics, with innovative methods like assembling Ikea furniture or writing college admission essays, regardless of age.

The New York Times

New York Times Sues OpenAI and Microsoft Over Use of Copyrighted Work ↗️