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Generative Artificial Intelligence (GenAI)

This guide provides an introduction to Generative Artificial Intelligence, originally created in Fall 2023 and updated in Winter 2024 by Librarian Greg Bem. Content based on Fall 2023 and Winter 2024 workshops developed by Greg Bem.

AI Guide Disclaimer

This guide was designed to support student learning at Spokane Community College. Its primary goal is to provide an introduction to understanding Generative Artificial Intelligence (GenAI)​. Consider this guide a steppingstone in a journey of Digital Literacy / technology education​. Note that this guide was created by a librarian and provides a general approach to learning about GenAI. This guide was not co-created with college administration or any specific academic programs or departments. Note that this guide was not created with the intention to promote GenAI as a universal technology solution. Students should critically think about technology tools and consider issues of use.

GenAI Basics

What is Artificial Intelligence (AI)?

"Artificial intelligence is a field of science concerned with building computers and machines that can reason, learn, and act in such a way that would normally require human intelligence or that involves data whose scale exceeds what humans can analyze. ​

"AI is a broad field that encompasses many different disciplines, including computer science, data analytics and statistics, hardware and software engineering, linguistics, neuroscience, and even philosophy and psychology."​

(Google)

What is Generative Artificial Intelligence (GenAI)?

"Generative AI refers to deep-learning models that can generate high-quality text, images, and other content based on the data they were trained on."

(IBM)

What is Generative AI? Video

(Oracle)

  • Integration of technology into more apps​/platforms, including various AI technologies brought into single interfaces (e.g. Bing Chat)
  • Consolidation of Language Learning Models (LLMs) into single apps
  • Input advancements, including Vision and Speech​
  • Apps available for offline access, including GPT4All
  • Costs are increasing with more advanced features

GenAI Images have many of their own problems and issues, and often those connected intellectual property and copyright law are centered. However, bias and stereotyping is rampant across many GenAI tools. Issues include:

  • Trained datasets hold significant bias and sustain stereotypes (race, gender, body type, etc)​
  • Image stereotypes can be blatant or subtle​
  • Stereotypes can be in the background/periphery of the image, not always main subject​
  • Ethical safeguards tend to be manual and sometimes are reactive​
  • Jailbreaking as civic action (example)​
  • Deep fakes can be created by training GenAI tools on sample images

Additional Resources to Learn about Bias in Technology

Ethical Use of Technology

Ethics in the context of technology typically means using technology in a way that aligns with community values and standards. Ethics is a large topic with many branches. Occasionally in academia, ethics is connected to academic integrity. The Spokane Community College (SCC) Student Handbook describes another related area of thought, student conduct:

"Students are expected to conduct themselves as responsible members of the academic community. This includes obeying the law, complying with policies, procedures and rules of the district, the colleges and their departments, and maintaining a high standard of integrity and honesty and respecting the rights, privileges and property of others."

(SCC Student Handbook)

As members of the academic community, we should apply this way of thinking to how we interact with technology tools, including those that use GenAI.

Examples of Ethical Use of GenAI Tools in Higher Ed

  • Brainstorming / topical idea generation​
  • Editing / review of documents​
  • Assistance with creative writing​
  • Checking for problems in code​
  • Learning how GenAI works / operates in culture​

Examples of Unethical Use of GenAI Tools in Higher Ed

  • Entering assignments as prompts to have GenAI do all of the work for you​
  • Using GenAI information as credible and authoritative ​
  • Using GenAI tools and providing output information without including a student statement​Copying output from GenAI tools without adding any citations​

Many writing styles have proposed language around GenAI citations and/or statements of use. Due to GenAI tools emerging in between official releases of the style guides, we have yet to see formal adoption in many of the official manuals, but the organizations who manage the styles are contributing resources to support students and researchers. Additionally, citation methods and strategies are being created locally at various schools and organizations. The following are links to two popular styles used at SCC:

You might also find the following guide from Grammarly helpful:

As GenAI becomes more prominent and used across disciplines and contexts, problems and issues with the technology emerge. Some of these problems are ethnical in nature and have to do with the values of the community, including legal constraints. Other problems may be concerned with the hardware and software of the technology. In many cases the lines are blurred and the issues overlap. Below are a few common issues.

  • Hallucinations and lack of citeable sources in some GenAI output​
  • Lack of governmental oversight / common rules​
  • Barriers to access tools, including fiscal costs for best features​
  • Developer safeguards may limit research potential​
  • Bias in data sets and bias in design lead to bias in GenAI output​
  • Violations of intellectual property in data sets, especially visual data; copyright limbo

GenAI: What is it Good for? Video

(The Economist)

A Selection of Popular GenAI Tools

Updated March 2024

Qualities of GenAI Tools

  • "Trained" on large sets of data (millions+ of documents)​
  • Utilizes "Large Learning Models (LLM)" (eg. "Generative Pre-Trained Transformer 3")​
  • Requires user input to create some form of output​
  • Developed by for-profit companies, non-profit organizations, and individuals​
  • Platforms can be free, paid, or both​
  • Mostly available through web browsers, mobile apps​

Common GenAI Output Types

  • Plain text​
  • Stylized text
  • Computer code​
  • Original image​
  • Edited image​
  • Animated image​
  • Video​
  • Music​

Prompts and Iterations

  • GenAI interfaces are similar to search engines​
  • The prompt is the phrase or keywords entered into search box​
  • Some tools have advanced features​
  • Iterative design offers a conversational approach to search​
  • Iterations can be limited (platform control, cost, etc) (eg. Bing Chat = 30 iterations in a single chat)​
  • And "Prompt engineer" is an emerging role / skill set​ that centers expertise in using prompts in a GenAI platform