Artificial intelligence, AI, is everywhere and involves far more than the generative AI and Large Language Models (LLMs) that have recently dominated the conversation. This guide will provide a brief overview of the history of AI, ethical issues, publishing, and AI’s use in classrooms.
Terminology
- Artificial intelligence (AI) – a branch of computer science dedicated to developing programs that can discover patterns, make actions, create results based on input, etc. (Midwestern State University, Moffett Library’s Glossary of Artificial Intelligence Terms)
- Generative AI – content-generating AI platforms used to create text, video, image, or code output based on the large datasets used as training data, from which they find patterns close to the source material for novel responses (CNET – ChatGPT Glossary: 52 AI Terms Everyone Should Know)
- Neural Networks – “a mathematical system that actively learns skills by identifying and analyzing statistical patterns in data; inspired by the neurons in our brain” per (MIT’s Glossary of Terms: Generative AI Basics)
- Large Language Models – neural networks that work by forecasting word sequences (MIT’s Glossary of Terms: Generative AI Basics)
- Deep learning models – a type of neural network capable of recognizing more complex patterns (Midwestern State University, Moffett Library’s Glossary of Artificial Intelligence Terms)
- Natural Language Processing – a subfield of AI studies focusing on getting machines to understand and generate regular human language rather than requiring specific terminology (MIT’s Glossary of Terms: Generative AI Basics)
- Transformer models – a type of language model and neural network, also classified as deep learning model, that use self-attention mechanisms to focus on the important parts of input and output (Midwestern State University, Moffett Library’s Glossary of Artificial Intelligence Terms)
- Hallucinations – incorrect or nonexistent information returned by generative AI platforms (MIT’s Glossary of Terms: Generative AI Basics)
General Information Resources
- An artificial intelligence perspective: How knowledge and confidence shape risk and benefit perception – by Said et al., published in Computers in Human Behavior
- Artificial Intelligence for the Benefit of Everyone – by Hessami et al., published in IEEE
- Gender and content bias in Large Language Models: a case study on Google Gemini 2.0 Flash Experimental – by Balestri, published in Frontiers in Artificial Intelligence
- Artificial Intelligence: Generative AI’s Environmental and Human Effects – from the US Government Accountability Office
- Large language models, explained with a minimum of math and jargon – by Lee & Trott
History & Ethical Issues
History of AI
There is an extensive history of artificial intelligence that extends back to the early 1900s. In 1914, a Spanish engineer named Leonardo Torres y Quevedo developed a fully automated chess-playing machine using electromagnets (IBM’s The History of AI). “A Logical Calculus of the Ideas Immanent in Nervous Activity” by Warren S. McCulloch and Walter Pitts introduced the idea of the brain as a computational system and proposed the concept of artificial neural networks (IBM’s The History of AI). However, Alan Turing is often viewed as the founder of artificial intelligence (Illinois Central College Artificial Intelligence (A.I.)). Turing published an article, “Computing Machinery and Intelligence,” discussing the question of whether machines are capable of thought (IBM’s The History of AI).
A conference of researchers from several disciplines at Dartmouth in 1956, hosted by the school’s mathematics professor John McCarthy, is credited with establishing the field of artificial intelligence (Coursera’s The History of AI: A Timeline of Artificial Intelligence).
The first program likened to today’s chatbots is ELIZA, developed by Joseph Weizenbaum (Coursera’s The History of AI: A Timeline of Artificial Intelligence). This program simulated therapy by responding to natural language inputs. Despite Weizenbaum’s theory that there would be simple back-and-forth, many users “attributed human-like emotions to the program, raising ethical questions about AI and human interaction” (IBM’s The History of AI) that persist to this day.
Ethics and AI
See the AI Literacy guide for more information on the ethical concerns surrounding AI.
- The Ethics of Artificial Intelligence – by Bostrom & Yudkowsky, published by the Machine Intelligence Research Institute
- Ethics of Artificial Intelligence – UNESCO recommendations
- Generative Artificial Intelligence and Copyright Law – from Congress
- Copyright and Artificial Intelligence – US Copyright Office
Publishing
Scholarly Publishing
There is an ongoing conversation around how AI is used in academic writing and publishing. As noted by Washington State University’s Generative Artificial Intelligence LibGuide page, AI and Scholarly Publishing, many journals have been publicizing their AI statements and how they are handling the disclosure of how AI has been used in submissions.
Authors are including AI-generated or supplemented content in their articles and research, which raises concerns about authorship (AI in Scholarly Publishing: A Study on LIS Journals’ Guidelines and Policies from the International Journal of Librarianship). Since AI content is generated by previously-compiled work, it is necessary that any AI-generated content be appropriately cited. However, it can be challenging to identify what content is from an LLM versus what conclusions an author has written themselves.
Copyright
Copyright is a challenging issue when it comes to AI. Large Language Models scrape the internet for their datasets, meaning that they pull copyrighted material without consideration of copyright. There have been several cases regarding potential copyright infringement for AI training, and the legal landscape is actively changing as regulation continues to develop (AI, Copyright, and the Law: The Ongoing Battle Over Intellectual Property Rights)
There is also discussion about whether AI-generated content is eligible for its own copyright and how that is treated in the legal system. The US Copyright Office has released their Copyright and Artificial Intelligence report to provide clarification on their treatment of AI-generated content and how to gauge human authorship.
Resources
Resources for Faculty
Clarkson does not have an institution-wide AI policy; each faculty member is able to determine how to use AI in their courses. Given the number of courses each student takes, it is very important to clearly establish your AI policies for every class. Determine the use cases, preferences, and limitations you want your students to take. See the AI in Research guide for tools centered around the research process that may be more appropriate for your own research and for students than general LLMs.
Scholarly articles on artificial intelligence in higher education are available through the library’s catalog. Other articles include:
- A Scoping Review of the Strategic Integration of Artificial Intelligence in Higher Education: Transforming University Excellence Themes and Strategic Planning in the Digital Era – by Abulibdeh et al., published in the European Journal of Education
- Critical Thinking in the Age of AI: A Systematic Review of AI’s Effects on Higher Education – by Melisa et al., published in Educational Process: International Journal
- Teachers’ early uptake of genAI in teaching and learning: important questions and answers – by Collie & Martin, published in Social Psychology of Education
- Exploring students’ AI literacy and its effects on their AI output quality, self-efficacy, and academic performance – by Becirovic, Polz, & Tinkel, published in Smart Learning Environments
- Workload perception in educational resource recommendation supported by artificial intelligence: a controlled experiment with teachers – by Machado et al., published in Smart Learning Environments
The following online articles discuss ways to integrate AI into your classrooms and the pros and cons.
- Embracing Artificial Intelligence in the Classroom – from the Harvard Graduate School of Education
- How Artificial Intelligence is Transforming Higher Education – from the American College of Education
- How is Artificial Intelligence Impacting Higher Education? – from the Rutgers-New Brunswick School of Communication and Information
- Bloom’s Taxonomy Revisited – from Oregon State University’s AI Tools for Faculty Support
- Teaching with AI: How college professors are redefining the classroom – from Ohio Today
- Teaching Tip: Navigating AI in the Classroom – from Inside Higher Ed
- Using AI in the Higher Education Classroom – from the University of North Texas’s Division of Digital Strategy and Innovation
Resources for Students
Plagiarism and Academic Honesty:
- Clarkson University Libraries’ Plagiarism Guide
- Clarkson Student Regulations
- Citing AI Generated Writing by University of South Florida
- Citing AI in APA style
- Citing AI in MLA style
General Guidelines:
If you are going to use LLMs or other forms of AI as a student, make sure you carefully review the policies established by your instructors for each class. Instructors have their own methods for integrating artificial intelligence into their courses and you are responsible for following the policies established for each course.
The AI Literacy guide focuses on understanding artificial intelligence, its use cases, concerns around its use, and how to use it effectively. Clarkson’s Writing Center also has a Generative AI Literacy Moodle course that may be helpful in evaluating your current level of AI literacy and any areas of improvement.
See the How to Study guide for suggestions on how to integrate AI into your study routines and the AI Literacy guide for research-oriented tools that may be more effective in the research process than ChatGPT or other standard chatbots.