Artificial Intelligence (AI) in Academia

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Artificial Intelligence (AI) refers to technology that looks like human intelligence, but operates through complex algorithms and data processing. While the technology has been present in some capacity for decades, recent advances in AI have sparked a new age in technology that affects not only education, but also the evolving landscape of the workforce for years to come.

 

Types of AI

Theories of Artificial Intelligence generally fall into one of three major categories:

Narrow

Performs a single narrow task like facial/speech recognition, driving a car, or internet searches. This is the only type of AI that currently exists.

General

Called Strong or Deep AI, mimics human intelligence and can learn/apply its intelligence to solve any type of problem. Does not currently exist.

Super

This purely theoretical type of AI goes beyond mimicking humans and becomes self-aware, with the ability to surpass human intelligence and ability.

Narrow AI  is the only type of intelligence that is successfully being realized today. Because every type of AI that is currently in use falls under the Narrow Intelligence AI category, all references to ‘AI’ in this resource refer to that type.

AI Frameworks

This taxonomy illustrates the hierarchical relationships between AI categories.

Taxonomy diagram showing five circles within each other, gradually decreasing in size to show how each topic fits into the larger one. The taxonomy starts with Large Language Models, which fits under Generative AI, which fits under Deep Learning, which then fits under Deep Learning, which then fits under Machine Learning. All circles and taxonomies fit under the category of Artificial Intelligence.
Select the image to read "AI in a Nutshell: A Practical Guide to Key Terminology" by Tobias Zwingmann.
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  • Commonly Used Terms

    • Machine Learning (ML) is a subset of AI where computers use algorithms to recognize patterns from data and apply that learning to other problems and complex tasks. Machine learning continuously learns and improves from its past experiences so that a programmer does not have to constantly apply a new “rule” (i.e. code) every time a problem needs to be solved. Machine learning is a method to which other types of AI function and/or produce content/data—it is the how of the following two terms. Here’s a short video on how Machine Learning works. 

    • Deep Learning (DL) is an advanced technique of Machine Learning that uses artificial neural networks—layers of algorithms and computing units—modeled after the human brain to mimic the learning process of humans. Deep learning is better suited for large, complex processing that involves unstructured data.  

    • Generative AI is a broad subset of AI—under Narrow AI—that uses machine learning techniques to create new content such as text, images, audio, codes, videos, or entire applications. Generative AI platforms are trained through large datasets to predict and generate content based on patterns that it observes in the training data it receives. 

      Examples of Generative AI include Copilot (Microsoft, institutionally supported), ChatGPT, DALL-E, and Gemini.

    • A Large Language Model (LLM) is a specific form of Generative AI that creates human-like text. As with other models that are developed with Generative AI, LLMs are trained using Machine Learning to recognize patterns and relationships from a dataset to produce text generation, language translation, text completion, and more. 

    • Keeping up with the ever-evolving terminology in AI can be challenging. AIPRM offers a comprehensive glossary of Generative AI terms to help you stay informed.

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  • Gamma.app: Presentation and visual AI generator
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  • Turnitin: AI Integrity Application
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  • Scite: Qualitative citation analysis AI

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Frequently Asked Questions About AI

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  • Engaging with these technologies firsthand allows you to:

    • Gain Practical Experience: Hands-on practice helps you understand the functionalities, limitations, and potential applications of AI in educational and workplace contexts.
    • Enhance Teaching Methods: By experimenting with AI tools, you can discover new ways to enhance student engagement, streamline administrative tasks, and personalize learning experiences.
    • Stay Current with Technological Advances: Regular interaction with AI platforms ensures that you remain up to date with the latest developments in AI, preparing you to effectively incorporate these innovations into your curriculum.

    • Administrative efficiency: AI can automate repetitive tasks like scheduling, grading, and data analysis, freeing up more time for teaching and research.
    • Student engagement and personalization: AI can tailor educational content and interventions to individual learning styles and needs, enhancing the learning experience and outcomes.
    • Accessibility: AI offers the potential to create more inclusive learning environments by providing tools for students with disabilities, such as text-to-speech or speech-to-text technologies, and by adapting content to diverse learning preferences and abilities. However, there are also accessibility challenges with AI that include performance inconsistencies that affect access. 

    • Data and Privacy: AI poses a concern regarding data and privacy issues because of the security and ethical implications of storing and analyzing large amounts of sensitive student and institutional data.
    • Potential for Data Bias: There is a concern with AI regarding biased algorithms that generate unfair outcomes and perpetuate inequalities, posing challenges to creating equitable learning environments.
    • Lack of Connection: The lack of human "touch" is a concern among those who value the interpersonal connection and nuanced understanding that human instructors bring to the learning process, and there is worry that over-reliance on AI may diminish the quality of education and student support due to a lack of human connection.

  • AI Tools Directory and Futurepedia are AI directory websites that compile a list of known AI tools, models, and technologies.

  • AI detection tools, while useful, are not always reliable for evaluating student work in higher education. These tools can often produce false positives and false negatives, potentially misidentifying original work as plagiarized or missing actual instances of academic dishonesty. Additionally, they may struggle with nuanced writing, creative expression, and discipline-specific terminology. If you decide to use detection tools in your class, it is recommended that you use them as a supplement to, rather than a replacement for, your professional judgment and understanding of your students' work.

    Some commonly used AI platforms for faculty are Trinka.ai, GPTZero, Scribbr, Writer AI, and Turnitin. Faculty Development cannot confirm the validity and accuracy of these platforms.

  • What are the ethical considerations of AI?

  • How can I prepare to use AI for research?

    • AI tools are robust and continually evolving, expanding, and improving every day. However, there are still many limitations and gaps in AI tools that must be understood and carefully considered when utilizing such platforms.

      • Firstly, AI systems can perpetuate existing biases present in the data they are trained on, leading to skewed and inaccurate results. 
      • Secondly, the ambiguity of many AI algorithms, often referred to as the "black box" problem, can make it difficult to understand how AI conclusions are drawn, raising concerns about transparency and accountability. 
      • Another concern is the reliance on AI that may result in overconfidence in the technology, potentially overlooking errors or anomalies that human oversight could catch. 
      • There are also ethical considerations, such as ensuring the privacy and security of sensitive data—especially involving human research participants and AI. 
      • Lastly, the rapid pace of AI advancements requires researchers to stay continuously updated on best practices and emerging issues, demanding a significant investment of time and resources.

    • Inform your IRB when you use AI in your research to interact with or obtain data by or from human research participants.

      When using AI in or to assist with any portion of a paper or article for journal submissions, check author guidelines and their permitted uses and restrictions of AI.

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