I just returned from the largest-ever UPCEA Annual conference in Washington DC. More than 1,000 leaders in professional, continuing and online education gathered to learn, share and prepare for the future. I had the privilege to greet hundreds of those as they registered for the conference on Wednesday. Many of those attending were excited about the potential of new knowledge that will be provided by the emerging generative technologies. Yet, others were somewhat fearful of the direct impact of generative AI on current and future jobs for themselves and their colleagues. Everyone seemed interested in just how ChatGPT works and how it is likely to change over time. Let’s sort through the current issues and concerns, then suggest what we are most likely to see in the near future. 

It is certainly true that GPT-4 and other large language models bring new abilities and opportunities to higher education. To quote Udacity CEO and Co-founder, Stanford Professor Sebastian Thrun, generative AI is “The single biggest thing we are ever going to see in our lifetime…” The reach of this technology goes much further than education. Our society will soon become populated with personal assistants serving individual and group needs across our culture. They will become high-powered personal assistants, tracking our individual interests, preparing us for work daily, and supporting us in advancing our careers. In effect, each and every one of us will have a super-charged, highly-empowered “Siri” or “hey Google” that will be pro-active and perform much more than simple reminders, fact checks, and search engine lookups. The next generation of personal assistants will anticipate our knowledge needs and provide value adds in information discovered through deep searches every day. 

As GPT-4 was released, OpenAI also released a treasure trove of benchmarking and further testing of their most recent versions. Incredibly, the GPT-4 version stacks up very well on standardized tests against some of our top graduate assistants. Check out these scores: Uniform Bar Exam 90^th percentile; SAT evidence-based reading/writing 93^rd percentile; SAT Math 89^th percentile; GRE Verbal 99^th percentile with dozens more documented and presented. To me, these and the rest of human-AI benchmarking results that OpenAI reported are astounding. I encourage all readers to check out the report that can be found online.

As mentioned in “Online: Trending Now” earlier this month, Ed Felten (Princeton), Manav Raj (University of Pennsylvania) and Robert Seamans (New York University) have released a soon-to-be peer-reviewed paper — “How Will Language Modelers such as ChatGPT Affect Occupations and Industries?” — in which the case is made that generative AI has the potential to impact teaching in many fields. 

Importantly, we in higher education will determine if these tools will supplement or, rather, replace workers in our field. If we follow the early lead of businesses, it is likely that we will reduce our staffing while expanding the use of generative AI. The question has been posed in substantial early surveys. Resume-Builder reports:

In February, ResumeBuilder.com surveyed 1,000 U.S. business leaders to see how many companies currently use or plan to use ChatGPT…. 1 in 2 companies have replaced workers with ChatGPT since November. Forty-eight percent of companies have replaced workers with ChatGPT since it became available in November of last year. In the future, ChatGPT may lead to more layoffs according to business leaders. When asked if ChatGPT will lead to any workers being laid off by the end of 2023, 33% of business leaders say ‘definitely,’ while 26% say ‘probably.’ Within 5 years, 63% of business leaders say ChatGPT will ‘definitely’ (32%) or ‘probably’ (31%) lead to workers being laid off.

In addition, respondents in the Resume-Builder survey indicated that 9 in 10 business leaders say it’s beneficial for job applicants to have ChatGPT experience, and that it’s possible having ChatGPT experience could help workers keep their jobs. In assessing candidates to hire, 92% of business leaders say AI/chatbot experience is a plus. 

The advice seems clear. We all may be well-served by investing some time in understanding this technology and testing out ways in which we may be able to use it to advance our work. 

The question of just how this large language model works, is perhaps best described by ChatGPT itself. I asked it to describe the process. In a response to my question on March 17, 2023, ChatGPT wrote: “Chatbot GPT (ChatGPT) is a language model based on the GPT-4 architecture developed by OpenAI. It functions by utilizing deep learning techniques, specifically, a type of artificial neural network called a Transformer. The GPT in ChatGPT stands for “Generative Pre-trained Transformer.” The app went on to describe six key steps in a “high-level overview.” Those include Pre-training, Fine tuning, Tokenization, Context vector, Attention mechanism and Decoding. The complete reply can be found in my presentation notes as well as – if you prefer – a human-written summary from March 10, 2023, “How does ChatGPT work?” by David Gewirtz, ZDNet. 

Sharon Goldman writing in VentureBeat highlights the “hottest” new trend in generative AI. It is the rapidly-growing number of productivity apps.  We are likely to see this trend continue to play out through the rest of the year. These tools have the possibility of saving time while providing higher-quality results in many different aspects of your work life. Many are free or low-cost. 

Looking ahead to the not-too-distant future, we are likely to see more about biocomputers. Using brain and stem cells, researchers are hoping to vastly accelerate and expand performance. Research is already well underway at Johns Hopkins:

It’s called organoid intelligence, or OI, and it uses actual human brain cells to make computing “more brain-like.” OI revolves around using organoids, or clusters of living tissue grown from stem cells that behave similarly to organs, as biological hardware that powers algorithmic systems. The hope—over at Johns Hopkins, at least—is that it’ll facilitate more advanced learning than a conventional computer can, resulting in richer feedback and better decision-making than AI can provide…. Using human brain cells to power computers has obvious ethical implications, which the researchers openly acknowledge. 

Decades ago, a wave of AI replaced assembly-line blue-collar jobs. This version of AI is more likely to replace administrative white-collar jobs that previously required college, even graduate degree level knowledge and skills. However, just as was the case those decades ago, the overall number of jobs increased; that is likely to happen again with the advent of generative AI. The new jobs will likely require a thorough understanding of generative AI and the skills to apply this emerging technology to advance effectiveness and efficiency. 

This article was originally published in Inside Higher Ed’s Transforming Teaching & Learning blog. 

When generative AI came rolling out of development lab obscurity last year, most of us were surprised – even shocked. AI has always been a path to streamline production and distribution. In the past, it had come to replace blue collar jobs. When I first tried it out in August, I wrote in this column, “Higher Ed, Meet GPT-3: We Will Never Be the Same!” I was astounded at the speed and cogency of GPT in answering complex questions with multiple paragraphs in the blink of an eye. Half a year later, it is time for an update as I prepare to travel to the UPCEA Annual Conference in Washington DC where AI will be high among the topics of conversation.

First, know that this technology is nuanced, it is not just OpenAI’s ChatGPT. Generative AI covers a wide range of applications and tools. I now keep shortcuts to three different versions of generative AI on my laptop and phone home screen. I use all three daily. There are many hosts for the basic technology using various versions of knowledge bases and configured to present results with multiple features. Yes, I sprang for the $20/month for ChatGPT Plus. It is ready for me on a click’s notice. Yet, another app I have found useful every day is Perplexity. I am most taken with the auto-embedded citations of sources in the response, much like we do in research papers. This is most useful for deeper digging into topics. Its knowledge base is not delimited to 2021 as is ChatGPT. A somewhat flashier app is You.com. This is also free as of now and has the benefit of the added “/imagine” extension that opens an image generator. Refining prompt inputs to the text-to-image generator is worthwhile to have copyright free images designed to your specifications.

A fourth application that holds great potential to those of us in higher ed is ChatPDF! It is what you might imagine, a tool that allows you to load a PDF of up to 120 pages in length. You can then apply the now-familiar ChatGPT analysis approach to the document itself. Ask for a summary. Dig into specifics. This will be a useful tool for reviewing research and efficiently understanding complex rulings and other legal documents.

In these examples, the tools are mostly using GPT 3.5 with some advanced features promised to come in the full release of GPT 4.0, expected in the near future. OpenAI CEO Sam Altman hints at the soon-to-come multimodal GPT ahead of the 4.0 release, possibly allowing for moving smoothly among text, images and other modes but not yet video, which he says will come later. Not to be forgotten is the Google product, “Bard” that launched to a stumbling start by giving a wrong answer in the very public reveal of the LaMBDA (Language Model for Dialogue Applications) – powered application. Now in restricted Beta release, it is expected to provide a credible competition to ChatGPT. 

Much has been made of the possibility of cheating by students using generative AI. Yet, it seems that faculty are much more likely to use the products to enhance their teaching and research rather than students are to use it to cheat. 

It is clear that generative AI has enormous potential in higher education. It auto-updates its knowledge base far more frequently and deeply than faculty possibly can. Yes, ChatGPT is delimited by a 2021 cutoff of knowledge, but that doesn’t have to be the case, nor will it be in the future. In a just released study “How will Language Modelers like ChatGPT Affect Occupations and Industries?” by Ed Felten (Princeton), Manav Raj (University of Pennsylvania), and Robert Seamans (New York University) the case is made that generative AI has the potential to impact teaching in many fields. The researchers applied an AI Occupational Exposure (AIOE) measure, developed in 2021, to determine which human requirements for a position most overlapped with generative AI capabilities. In one of the listings reviewed by the researchers of most likely to be affected career fields, 14 of the top 20 occupations were faculty positions in a wide range of fields. A CBS News report on the paper discusses the relative extent to which this predicts job augmentation or rather job substitution.

Longer-term advances in applying AI to learning are coming along, but at a measured – or sometimes a rather stumbling – pace. Neuralink, a subsidiary company owned by Elon Musk, continues its research into direct brain-computer interfacing. However, it has run into repeated regulatory obstacles. The FDA has just blocked a request to begin human trials in implanting chips into human brains. Some pessimism is centered on the company’s understanding of current regulations and attention to regulators. 

Johns-Hopkins researchers are examining the potential of using human stem cells to power a kind of organic computing:

It’s called organoid intelligence, or OI, and it uses actual human brain cells to make computing “more brain-like.” OI revolves around using organoids, or clusters of living tissue grown from stem cells that behave similarly to organs, as biological hardware that powers algorithmic systems. The hope—over at Johns Hopkins, at least—is that it’ll facilitate more advanced learning than a conventional computer can, resulting in richer feedback and better decision-making than AI can provide. 

Of course, there are both technological and ethical considerations to be addressed as this model moves forward.

Much of the interesting work in the coming months will be to design interfaces to adapt the technology to actual work roles in both supportive and possibly in replacement modes. Generative AI already creates lesson plans, grades assignments, advises students, and answers learner questions. Can it competently take on class management and the other associated administrative tasks? And, certainly, generative AI has an important role to play in research. Will it become a formal or informal co-investigator and co-author of research? What status will we give generative AI in higher ed? And, what will happen to the human faculty aspirants who fail to measure up? I am looking forward to discussing these questions as we prepare for our generative AI partnership in higher ed.

This article was originally published in Inside Higher Ed’s Transforming Teaching & Learning