ChatGPT, an AI program capable of writing in a human-like format, brings uncertainty in higher level education, specifically with its ability to replicate educated responses. The AI has been used in recent months by students attending universities to write academic papers without so much as typing a paragraph. Schools have been made aware of this and are taking action based on each school's plagiarism policies and honor codes. Although having an AI to write papers for you may seem convenient, there are multiple issues that have been brought to light that insinuate the software does not come without drawbacks. ChatGPT was created by the start-up company OpenAI, a San Francisco based company with a close relation with Microsoft, on Nov. 30 2022. Schools across the country are combating the use of AI by blocking the software from school networks, such as the institutions' WiFi networks and school issued computers.

Higher education already employs artificial intelligence in a number of effective ways--course and facilities scheduling, student recruitment campaign development, endowment investments and support, and many other operational activities are guided by AI at large institutions. The programs that run AI--algorithms--can use big data to project or predict outcomes based on machine learning, in which the computer "learns" to adapt to a myriad of changing elements, conditions and trends. Adaptive learning is one of the early applications of AI to the actual teaching and learning process. In this case AI is employed to orchestrate the interaction between the learner and instructional material. This enables the program to most efficiently guide the learner to meet desired outcomes based upon the unique needs and preferences of the learner. Using a series of assessments, the algorithm presents a customized selection of instructional materials adapted to what the learner has demonstrated mastery over and what the learner has yet to learn.

Artificial intelligence adoption is increasing in higher education for both academic and research purposes. Too often, though, universities lack the IT infrastructure needed to sustainably power these systems. "To do AI at scale, you need data, but you also need compute power, networking, storage and software," says Cheryl Martin, director of global business development for higher education and research at NVIDIA. "Universities need a platform to bring all those things together." Modern AI requires purpose-built infrastructure that can handle its massively parallel computational demands.

From chatbots to discussion platforms, artificial intelligence (AI) is popping up at campuses all over the globe. In fact, the recent AI in Education Market Research Report from Research and Markets predicts that the global AI in education market will reach $25.7 billion in 2030, up from just $1.1 billion in 2019. The report shows that the largest demand for AI has been for learning platforms, mainly because of the increasing preference for remote and online education courses--even before the pandemic. It predicts that the next AI area to explode will be intelligent tutoring systems applications. A chatbot is a computer program that imitates human conversation and continually learns from every conversation it has, improving the efficiency of its responses.

Universities have long been a source of talented leaders for industry, but an accelerating exodus of professors with expertise in artificial intelligence has caused concerns. A recent Bloomberg op-ed asked, "If industry keeps hiring the cutting-edge scholars, who will train the next generation of innovators in artificial intelligence?" This article analyzes the problem and suggests solutions. The brain drain of AI experts out of academia can be explained in simple economic terms. The demand for experts has outpaced supply, leading to sharply increased prices.

I recall the advent of the ILLIAC computer, ARPANET (that grew into the internet), the personal computer, the mobile phone, the smartphone, and other advancements in technology that have had such a huge impact on our society. Yet these are mere drops in the ocean compared to the impact we will see from the advent of quantum computing. Earlier this year, Google's 53-qubit computer reached computing supremacy, and from now on the world will never be the same. Google's quantum computer was reportedly able to solve a calculation -- proving the randomness of numbers produced by a random number generator -- in 3 minutes and 20 seconds that would take the world's fastest traditional supercomputer, Summit, around 10,000 years. This effectively means that the calculation cannot be performed by a traditional computer, making Google the first to demonstrate quantum supremacy.

Last week, I reviewed Thomas Siebel's surprisingly good Digital Transformation: Survive and Thrive in an Era of Mass Extinction. The components that makeup Siebel's digital transformation are: cloud computing, big data, IoT (internet of things), and AI (artificial intelligence). In that review, I asked if any books examine the impact of these technologies on the future of higher ed? But maybe that is the wrong question. A better approach might be to ask: what are the academic analogs of each of the components of digital transformation?

Will artificial intelligence (AI) trigger a dystopic future (re: Skynet)? Will machine learning help cure cancer? Theories abound in the AI conversation. What's not debatable, however, is AI's popularity. Consumers are adopting it at a rapid clip and manufacturers are investing heavily in AI research and development.

The first time I mentioned chatbots (or bot-based technology) on this blog was back in 2016 in a post titled "Messaging is the Past, Present, and Future." There are a lot of non-HE chatbots in operation at the moment. For example, the Transport for London TravelBot within Facebook Messenger is daily go-to for anyone who uses the Tube. And the chatbots from Duolingo are a great way to practice learning a new language. However, as with a lot of buzzword-driven technology (and this isn't a bad thing), chatbots are increasingly becoming part of the mobile-app landscape for higher education.

From introductory gen-ed classes to advanced graduate seminars, wherever classes online or on campus include more than a couple of students, we have struggled with finding ways to assure that all students are given personalized attention to meet their learning needs. This has led to differentiated learning models in which students are presented materials based on assessments conducted prior to the class. But that approach too often fails to adapt to progress during the semester and misses opportunities for exchanges and synergies among all learners. It is also most practical only when there are enough classes to support multiple sections at the differentiated levels or multiple groups within a single class. As expert systems and AI technologies have developed, the promise of personalized learning is now being tested.