ChatGPT & Generative A.I.

ChatGPT is a chatbot powered by artificial intelligence (AI or A.I.) technology. Created by the San Francisco, California-based technology company OpenAI, ChatGPT made global headlines following its November 2022 public release. ChatGPT’s launch was widely characterized as an enormous success for AI technology in general and OpenAI in particular. The chatbot had attracted a reported one hundred million active users by January 2023, approximately two months after its release, leading international media sources to describe it as the fastest-growing consumer software application in the history of computing.

ChatGPT is a chatbot, a specific software designed to simulate interactive conversations with human users. The first chatbot in computing history, ELIZA, was created at the Massachusetts Institute of Technology (MIT) in 1966 by the German-American computer scientist Joseph Weizenbaum (1923–2008). ELIZA used techniques including substitution and pattern matching. Though simple by contemporary standards, ELIZA was novel and highly advanced for its time and was reportedly capable of convincing some users that they were conversing with another person.

OpenAI began developing ChatGPT in 2018 after it released its initial generative pre-trained transformer (GPT) neural network machine learning model. ChatGPT uses a version of the technology known as GPT-3.5, which OpenAI combined with AI-assisted natural language processing (NLP). NLP combines multiple deep machine-learning models with principles of computational linguistics to understand and interpret text-based prompts in ways that simulate human language processing. ChatGPT is the first chatbot in computing history to integrate GPT and NLP (Source: EBSCO, 2023).

Artificial intelligence is the design, implementation, and use of programs, machines, and systems that exhibit human intelligence, with its most important activities being knowledge representation, reasoning, and learning. Artificial intelligence encompasses several important subareas: voice recognition, image identification, natural language processing, expert systems, neural networks, planning, robotics, and intelligent agents. Artificial intelligence researchers, including classical search, probabilistic search, and logic programming, have enhanced several essential programming techniques.

The first activity of artificial intelligence is understanding how multiple facts interconnect to form knowledge and represent that knowledge in a machine-understandable form. The next task is to understand and document a reasoning process for concluding. The final component of artificial intelligence is to add, whenever possible, a learning process that enhances the knowledge of a system.

In 2022, the AI research group OpenAI released ChatGPT, an AI chatbot capable of generating complex responses to user prompts. In the months following its release, ChatGPT went viral for its potential real-world applications, including business, research, and classroom uses. However, several flaws were soon identified with the program, including potential factual unreliability in its responses. In January 2023, NBC News reported that the New York City Department of Education had banned the use of ChatGPT in the classroom due to the program's potential impact on student learning. (Users discovered that ChatGPT could be used to write essays, solve complex problems, and generate computer code, among other uses, which caused fears among many that students could use the program to complete their coursework.) The following month, OpenAI announced ChatGPT Plus, a subscription to an enhanced chatbot version with expanded features (Source: EBSCO, 2023).

Generative artificial intelligence is a type of artificial intelligence (AI) technology that can make content such as audio, images, text, and videos. It involves algorithms such as ChatGPT, a chatbot that can produce essays, poetry, and other content requested by a user, and DALL-E, which generates art. AI emerged gradually over more than half a century. Generative AI is a type of machine learning , which involves using data and algorithms to imitate how humans learn and become more accurate. While machine learning can perceive and sort data, generative AI can take the next step and create something based on the information it has. However, it remains expensive, and only a few well-financed companies, including OpenAI, DeepMind, and Meta , have built generative AI models.

AI has several subfields, including deep learning , machine learning, and neural networks . Deep learning is a subfield of neural networks, which is a subfield of machine learning. Machine learning is a neural network with at least three layers. Neural networks try to function in the way that the human brain does so they can learn from available data. Additional layers help to increase accuracy. Deep learning is commonly used to improve automation, such as by assessing processes to increase efficiency. This technology is used by digital assistants, self-driving vehicles, and voice-enabled devices such as television remotes. Deep learning can use unstructured data such as images and texts. For example, if tasked with organizing images of vehicles, a deep learning algorithm would decide what features are most important to consider, such as size. Machine learning uses structured data or data that it can organize into a format that it can use to make predictions. In other words, a person would decide what features are most important and create a hierarchy for the machine learning algorithm to use. A deep learning algorithm evaluates its accuracy and, if presented with a new photo, can more precisely evaluate the subject.

Machine learning and deep learning models can learn in different ways. The types of learning are supervised, unsupervised, and reinforcement. Supervised learning uses datasets that a person has labeled, and the model’s education is guided by a human. Unsupervised learning, or self-supervised learning, finds patterns in the data and groups the data accordingly. Reinforcement learning uses feedback to refine its actions and become more accurate (Source: Salem Press Encyclopedia of Science, 2023).

Understanding the Frameworks (also called Frames)

The Framework offered here is called a framework intentionally because it is based on a cluster of interconnected core concepts, with flexible options for implementation, rather than on a set of standards or learning outcomes, or any prescriptive enumeration of skills. At the heart of this Framework are conceptual understandings that organize many other concepts and ideas about information, research, and scholarship into a coherent whole. These conceptual understandings are informed by the work of Wiggins and McTighe,² which focuses on essential concepts and questions in developing curricula, and also by threshold concepts³ which are those ideas in any discipline that are passageways or portals to enlarged understanding or ways of thinking and practicing within that discipline. This Framework draws upon an ongoing Delphi Study that has identified several threshold concepts in information literacy,⁴ but the Framework has been molded using fresh ideas and emphases for the threshold concepts. Two added elements illustrate important learning goals related to those concepts: knowledge practices,⁵ which are demonstrations of ways in which learners can increase their understanding of these information literacy concepts, and dispositions,⁶ which describe ways in which to address the affective, attitudinal, or valuing dimension of learning. The Framework is organized into six frames, each consisting of a concept central to information literacy, a set of knowledge practices, and a set of dispositions. 

ACRL FRAMES

Authority Is Constructed and Contextual
Information resources reflect their creators’ expertise and credibility, and are evaluated based on the information need and the context in which the information will be used. Authority is constructed in that various communities may recognize different types of authority. It is contextual in that the information need may help to determine the level of authority required.

Information Creation as a Process
Information in any format is produced to convey a message and is shared via a selected delivery method. The iterative processes of researching, creating, revising, and disseminating information vary, and the resulting product reflects these differences.

Information Has Value
Information possesses several dimensions of value, including as a commodity, as a means of education, as a means to influence, and as a means of negotiating and understanding the world. Legal and socioeconomic interests influence information production and dissemination.

Research as Inquiry
Research is iterative and depends upon asking increasingly complex or new questions whose answers in turn develop additional questions or lines of inquiry in any field.

Scholarship as Conversation
Communities of scholars, researchers, or professionals engage in sustained discourse with new insights and discoveries occurring over time as a result of varied perspectives and interpretations.

Searching as Strategic Exploration
Searching for information is often nonlinear and iterative, requiring the evaluation of a range of information sources and the mental flexibility to pursue alternate avenues as new understanding develops.

Source: Association of College and Research Libraries, 2015

Source: Pew Research Center, 2022.

Image Credit: ChatGPT? We need to talk about LLMs, by Rebecca Sweetman and Yasmine Djerbal, 2023.

Creative Commons: Plagiarism in Higher Education: Tackling Tough Topics in Academic Integrity (2021) by Dr. Eaton