Introducing AI in Academia (1 of 4): Using AI to Enhance Learning
- Allen Karsina
- Jan 16
- 5 min read
Updated: Jan 19
Defining Our Terms
This is the first in a series of blog posts written by the Artificial Intelligence Faculty Learning Community Group (AI FLC) at Regis College. Although the posts are primarily intended for the Regis community, the conversation is open to everyone. This post was written by Allen Karsina.
I had been meeting with a small group of Regis College faculty about artificial intelligence (AI) for about a year when the idea of creating a blog first emerged. The rationale was straightforward: despite the growing "buzz" around AI, faculty members’ experiences and levels of expertise with the technology varied widely. While some had started integrating AI into their teaching practices, others had yet to explore its potential. To foster informed and productive discussions about incorporating AI into the classroom, we realized the value of sharing practical, accessible strategies. By showcasing specific examples and providing video demonstrations, the blog could serve as a resource to inspire and guide faculty in leveraging AI effectively in their teaching.
But before diving into practical examples, it’s important to step back and consider the bigger picture. Five key topics deserve thoughtful discussion:
What is AI?
How is AI currently being used in education?
What are the benefits and opportunities of integrating AI into education?
What are the challenges and risks of integrating AI into education?
What are the best practices for integrating AI into education?
This blog post explores the first of these questions briefly, providing essential context for the practical examples that will be discussed in future posts. Separate posts exploring the remaining questions will be published at or near the same time.
What is AI? - Defining our Terms
The term "artificial intelligence" was first coined by John McCarthy during a landmark workshop at Dartmouth College in 1956. This gathering brought together several future pioneers of the field and aimed to explore the potential of creating machines capable of intelligent behavior. At the time, AI was defined as "the science and engineering of making intelligent machines" (ChatGPT; Mitchell, 2019). The workshop was marked by both great optimism about the possibilities of AI and significant divergence in views on how these possibilities might be realized.
Since that seminal event, the development of AI has unfolded in cycles of "springs" and "winters." Periods of enthusiasm, characterized by bold promises, intense funding, and rapid progress, have often been followed by "winters"—times of disillusionment and stagnation when the technology failed to meet inflated expectations (Mitchell, 2019). These alternating phases reflect the challenges of translating ambitious visions into practical, scalable solutions. In the current "spring", AI has at last lived up to at least some of the expectations.
But before going on to the next question and describing some of the ways AI is being used in education, it may be useful to look at a few more terms. Chan and Colloton (2024) identify three broad categories of AI:
Artificial Narrow Intelligence (ANI). Sometimes referred to as "weak" AI, this term refers to AI that is designed to excel at a specific task, but nothing else. When IBM's Deep Blue defeated Garry Kasparov in 1997, it marked a milestone in ANI. Other uses of ANI include text prediction, recommendations ("you might also like..."), fraud detection, autonomous vehicles, navigation systems, and much more.
Artificial General Intelligence (AGI). Sometimes referred to as "strong" AI, this term refers to AI that can excel at many related and unrelated tasks, adapting and learning as humans do. Despite some impressively versatile applications like ChatGPT, this type of intelligence has not yet been achieved. Data from Star Trek and Skynet from The Terminator movies are fictional examples of this type of AI. [Update: On January 6th, OpenAI's Sam Altman announced that he expected applications of AGI this year - hype or reality? My bet is on hype, but we will see].
Artificial Super Intelligence (ASI). The final evolution of AI, referring to AI that exceeds human intelligence in every way, including such human qualities as creativity and problem-solving. Whether or not such intelligence will ever be achieved, and what it would mean if it were, are the subject of debate. According to Mitchell (2019), we have probably spent far too much time and energy worrying about ASI and not enough time and energy working on addressing the real concerns of the AI systems we are already using.
If the thought of actually achieving AGI or ASI has you a bit uneasy, there is one more term to consider, and this type of AI is here now:
Generative Artificial Intelligence (GAI, or GenAI) refers to AI systems designed to create new content, leveraging either discriminative or generative modeling. This technology powers a wide range of applications, including image synthesis tools like DALL-E, video generation platforms like Sora, text-to-text and speech-to-speech systems like Siri, and text generation tools such as ChatGPT.
GAI is at the heart of the opportunities and challenges currently reshaping education and many other fields. Unlike earlier AI tools, which were powerful yet predictable, GAI operates in a fundamentally different way. We no longer fully understand its inner workings or the outcomes it might produce, which makes it both revolutionary and unpredictable.
While there is much more to explore about GAI, this introduction sets the stage for deeper discussions.
AI Literacy
A recurring theme in research on AI in education is the pressing need for AI literacy. As AI applications proliferate and become increasingly ubiquitous, it is crucial for students, educators, and indeed everyone, to develop a foundational understanding of AI. This knowledge helps maximize the benefits of AI while minimizing its risks.
This post offers just an introduction to the concept of AI literacy. I plan to explore this topic in greater depth in future posts. In the meantime, I recommend the books listed below for anyone eager to delve into the subject. Additionally, the "exercise" provided at the end of this post offers a practical starting point for exploring AI basics. Stay tuned for more resources in upcoming posts!
Interactive Exercise. Open ChatGPT, Copilot, or the chatbot of your choice. Copy and paste the following prompt: I am interested in learning more about important terms in the field of artificial intelligence. Give me a table with three columns where the first column is an important term in artificial intelligence, the second column is the definition, and the third column is an example. |
The exercise above is really just to get you started. I tried it in ChatGPT4o and got 12 terms with clear definitions and examples. I tried it in Copilot and got only 3 terms, but I also got the options to ask for more terms, explain one of the terms more fully, or see more applications.
With these foundational concepts in mind, we can now move on to the next question.
News and Announcements
Interested in learning more about AI?
Regis faculty are encouraged to self-enroll in the "Using AI in your Teaching" course created by Dr. Diana Perdue. Faculty can access the course here (don't forget to click the "enroll me" button).
Interested in contributing a post or joining our Regis College Artificial Intelligence Faculty Learning Community?
Contact Allen Karsina at allen.karsina@regiscollege.edu
References
Chan, C. K. Y., & Colloton, T. (2024). Generative AI in higher education: The ChatGPT effect. Routledge.
Mitchell, M. (2019). Artificial intelligence: A guide for thinking humans. Farrar, Straus and Giroux/MacMillan.
Acknowledgements
ChatGPT 4o was used to suggest improvements for the text of this post.
The image was created by the author using Dall-E3 in ChatGPT 4o.
The audio was generated using NotebookLM by uploading a draft of the blog and using the "audio overview" function. Only the first 5 minutes of the recording were played as the audio was 15 minutes in length and went beyond the scope of the post (and into the subject of our next post) at the 5 minute mark.
Comments