Despite of (or in fact, because of) all its constraints and weirdness, AI is perfect for idea generation. You often need to have a lot of ideas to have good ideas. Not everyone is good at generating lots of ideas, but AI is very good at volume. Will all these ideas be good or even sane? Of course not. But they can spark further thinking on your part.
It is now trivial to generate a video with a completely AI generated character (you can use the images generated using the techniques in the guide), reading a completely AI-written script, talking in an AI-made voice, animated by AI. I created this video of a cyborg giving a TED Talk in under two minutes. The easiest way is to use D-iD, but a lot of competition is entering the space, and things are changing by the minute.
Both ChatGPT and GPT-3.5 are very good at writing code. There is evidence that using AI assistance in coding may cut programming time in half. You also might be able to use it even if you don’t code yourself. For example, you can create javascript art projects just by asking, as this thread explains. But the process is not 100% error-free for non-programmers.
To accomplish the same goals, non-programmers may not be aware that there has been a revolution in workable low-code or no-code application builders (many of which have nothing to do with AI) that use platforms like Bubble or Shopify to help you create fairly sophisticated apps without the need to program directly. Early evidence shows that companies built with these approaches get similar kinds of outcomes as other startups, but they do it faster and raise less cash (and get lower valuations). Many of these systems include good tutorials, such as Bubble’s Academy. However, these platforms typically charge for services.
Summarize texts. I have pasted in numerous complex academic articles and asked it to summarize the results, and it does a good job! (though remember the size limits). Even better, you can then interrogate the material by asking follow-up questions: what is the evidence for that approach? What do the authors conclude? And so on…
Help with concepts. You can ask the AI to explain concepts. Because we know the AI could be hallucinating, you would be wise to (carefully!) double-check its results against another source. This both helps you learn and confirms the AI output looks good. Once you have a sense that it is right, ask it to explain it in different ways “Like I am 10” or “in a script from The Office” or “in the context of a medical examination.” Again, this is a start for your learning journey, since it will often get subtleties wrong.
Some things to worry about: If you don’t check for hallucinations, it is possible that you could be taught something inaccurate. Use the AI as a jumping-off point for your own research, not as the final authority on anything. Also, it isn’t actively connected to the internet, so it has no up-to-date information.
There are many ethical concerns you need to be aware of. AI can be used to infringe on copyright, or to cheat, or to steal the work of others, or to manipulate. And how a particular AI model is built and who benefits from its use are often complex issues, and not particularly clear at this stage. Ultimately, you are responsible for using these tools in an ethical manner. Be transparent about how you use AI, and take responsibility for the output you create.
Amazingly helpful Ethan. How much did you use ChatGPT in writing it? That aside, I really like the vibe of embracing AI, rather than running from it. This has the feel of the heady days back in the 1990s when people were waking up to the web. But I think you rightly have included some warnings, e.g. hallucinating AI. Keep writing, I'm really enjoying it.
Artificial intelligence technology has become increasingly sophisticated and readily available. We believe that educators can contribute to how this important technology is understood and used. We invite you to engage thoughtfully and attentively with this teaching guide as a way to learn about and positively influence the dialogue around artificial intelligence in education.
We offer this guide to all instructors and teaching teams approaching the topic of generative AI tools in education, whether for the first time or as part of your ongoing engagement with the topic, in response to practical concerns that we heard from instructors like yourself. You don't need to be an expert or have prior experience with generative AI to use this resource, though you should have some understanding of or experience with teaching and learning in higher education contexts. We intend this guide to apply to any disciplinary area or teaching modality and to help you structure the work of integrating AI tools into your teaching practice.
We cannot comprehensively address the complex topic of artificial intelligence in any short guide. Many campus service providers, such as University Information Technology (UIT), Stanford Accelerator for Learning, and the Institute for Human-Centered Artificial Intelligence (HAI), have developed excellent resources that offer insight into AI in terms of technical aspects, innovative new tools, societal impacts, AI research, and so on. We have chosen to focus on the practical and pedagogical aspects of AI tools in the classroom. We will focus on generative AI chatbots in particular, but you may find the content here can also apply to other generative AI tools, such as image, media, or code generators.
Each page of this guide contains one instructional module including content, practice tasks, and assessment activities. We suggest that you complete the activities and suggested readings in each section as a self-directed online lesson. We designed each module as a discrete and complete lesson that you can finish in a relatively short amount of time. You can work through the modules in any order. We encourage you to engage fully with each module, completing the recommended activities to reinforce your learning.
Stanford's Center for Teaching and Learning has also developed do-it-yourself workshop kits inspired by these modules. The kits expand upon the topics and strategies covered in these modules. Each workshop kit typically contains a resource list, sample agenda, promotional materials, slide presentation, facilitator's notes, key strategies, and an evaluation tool to assess learning and gather feedback.
We did not copy and paste any language generated by AI chatbots into this guide. We used AI chatbots, primarily ChatGPT, to generate feedback on the clarity and structure of some of the writing and to clean up some text formatting. We used ChatGPT and other chatbots more extensively in the development of the module "Exploring the pedagogical uses of AI chatbots" to mimic how we thought instructors and students might use them in a course and to better understand the pedagogical potential and challenges of such tools.
We are a team of support staff from different parts of the Office of the Vice Provost for Undergraduate Education (VPUE). Our team created the guide in the summer of 2023 through the collective effort of dedicated colleagues from across the university. We want to thank the following people who contributed to this resource.
You may adapt, remix, or enhance these modules for your own needs. This guide is licensed under Creative Commons BY-NC-SA 4.0 (attribution, non-commercial, share-alike) and should be attributed to Stanford Teaching Commons. If you have any questions, contact us at TeachingCommons@stanford.edu.
Generative AI tools and technologies, such as ChatGPT, may not be listed as authors of an ACM published Work. The use of generative AI tools and technologies to create content is permitted but must be fully disclosed in the Work.
“Authors are allowed to use generative AI and AI-assisted technologies in the writing process before submission, but only to improve the language and readability of their paper and with the appropriate disclosure, as per our instructions in Elsevier’s Guide for Authors(opens in new tab/window).”
Research EngineFirstly, because these tools cannot take accountability for such work, AI tools/large language models cannot be credited with authorship of any Emerald publication. Secondly, any use of AI tools within the development of an Emerald publication must be flagged by the author(s) within the paper, chapter or case study.
“Large Language Models (LLMs), such as ChatGPT, do not currently satisfy our authorship criteria. Notably an attribution of authorship carries with it accountability for the work, which cannot be effectively applied to LLMs. Use of an LLM should be properly documented in the Methods section (and if a Methods section is not available, in a suitable alternative part) of the manuscript.”
Natural language processing tools driven by artificial intelligence (AI) do not qualify as authors, and the Journal will screen for them in author lists. The use of AI (for example, to help generate content, write code, or process data) should be disclosed both in cover letters to editors and in the Methods or Acknowledgements section of manuscripts.
Contributions by artificial intelligence (AI) tools and technologies to a study or to an article’s contents must be clearly reported in a dedicated section of the Methods, or in the Acknowledgements section for article types lacking a Methods section.
The currently available language models are not fully objective or factual. Authors using generative AI to write their research must make every effort to ensure that the output is factually correct, and the references provided reflect the claims made.
Authors must be aware that using AI-based tools and technologies for article content generation, e.g. large language models (LLMs), generative AI, and chatbots (e.g. ChatGPT), is not in line with our authorship criteria. Where AI tools are used in content generation, they must be acknowledged and documented appropriately in the authored work.
The final decision about whether use of an AIGC tool is appropriate or permissible in the circumstances of a submitted manuscript or a published article lies with the journal’s editor or other party responsible for the publication’s editorial policy.
In accordance with Purdue policies, all persons have equal access to Purdue University’s educational programs, services and activities, without regard to race, religion, color, sex, age, national origin or ancestry, genetic information, marital status, parental status, sexual orientation, gender identity and expression, disability or status as a veteran. See Purdue’s Nondiscrimination Policy Statement. If you have any questions or concerns regarding these policies, please contact the Office of the Vice President for Ethics and Compliance at vpec@purdue.edu or 765-494-5830.
Using AI for research in current times, where the research landscape is evolving at an unprecedented rate, can be a game changer. Artificial intelligence can assist, augment, and even revolutionize the way we discover, conduct, and write scientific research.
From the invention of the first wheel for moving around faster to Galileo observing the cosmos using a telescope, there has been no shortage of instances where scientists have used technology to do their work more efficiently. And isn’t that the whole point? Since human faculties can be limiting.
Using AI for research is no different, particularly in current times where the research landscape is evolving at an unprecedented rate. New scientific domains are sprouting frequently, millions of papers are being published every year, and there are vast amounts of data needing to be synthesized.
This is where artificial intelligence can assist, augment, and even revolutionize the way we discover, conduct, and write scientific research. Generative AI has proven itself to be more than a simple buzzword to the point where it can provide real useful value to a researcher at any level.
If you go about conducting a manual literature review, you’re talking about countless days of dedicated effort in search and reading. On the other hand, AI can significantly reduce the time and effort it takes to conduct a literature review.
There are AI search engines in plenty that comb through vast databases of research papers, identify relevant papers, and even summarize key findings. This can help you speed up paper analysis, find trends or gaps in the literature, and discover a research question faster.
Most of these AI research assistants and ChatPDF tools help you discover new research articles based on a more accurate semantic search. Even if you don’t have the right keyword, you will still be able to find the correct papers.
AI can make academic papers easier to read and understand by simplifying jargon and complex topics in research papers. They can also summarise long papers into shorter reads so that you save quite some time while going through heaps of scientific articles.