In this section, we will examine how generative AI (GenAI) tools may assist with academic reading and research. Examples of content generated by ChatGPT will show how GenAI may be incorporated into a classroom setting. Each section offers suggestions for use and various strategies that could be incorporated for those who wish to allow the use of these tools for assignments. Included throughout are suggestions on how to promote students’ ethical and effective use of these tools and to possibly limit their use if desired. By the end of this section, you should be able to use GenAI to support reading practices.

Author: Jonahs Kneitly
Contributors: Gwendolyn Inocencio, Mary Landry, C. Anneke Snyder
Designers: Irene AI, Sweta Kailani
Supervisors: Terri Pantuso, Sarah LeMire

In this section, illustrative examples from ChatGPT show how to incorporate Large Language Models (LLMs) into the writing process while considering ethical concerns associated with such tools, namely avoiding plagiarism or exploitation of AI-generated content. The advent of public access to LLMs means they are now a critically important aspect of digital information literacy. As such, this technology must be addressed in the composition classroom with guided instruction. We recommend a strategy that models application of a modified version of stasis theory to all LLM-generated content.

After reading this section you should be prepared to teach stasis theory as a strategy for continual interrogation that helps rhetors discern whether generative-AI content exhibits appropriate depth, scope, and quality, along with the appropriate next steps in argumentation, writing, or research.

Author: Gwendolyn Inocencio
Contributors: C. Anneke Snyder, Mary Landry, Jonahs Kneitly
Designers: Irene AI, Shweta Kailani
Supervisors: Terri Pantuso, Sarah LeMire

Addresses trends in the Information Technology (IT) industry, with an emphasis on modern communications and Internet technologies and database and Web technologies, and their role in supporting the integration of information systems. Presents framework for understanding integrating concepts and the strategic and organizational factors impacting success of IT in business. The strategic importance of information technology is now widely accepted. It has also become increasingly clear that the identification of strategic applications alone does not result in success for an organization. A careful coordination of strategic applications, information technologies, and organizational structures must be made to attain success. This course addresses strategic, technological, and organizational connectivity issues to support effective and meaningful integration of information and systems. This course is especially relevant to those who wish to effectively exploit information technology and create new business processes and opportunities.

Introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. Applications of rule chaining, heuristic search, constraint propagation, constrained search, inheritance, and other problem-solving paradigms. Applications of identification trees, neural nets, genetic algorithms, and other learning paradigms. Speculations on the contributions of human vision and language systems to human intelligence.

Presents the main concepts of decision analysis, artificial intelligence, and predictive model construction and evaluation in the specific context of medical applications. Emphasizes the advantages and disadvantages of using these methods in real-world systems and provides hands-on experience. Technical focus on decision analysis, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks), and techniques to evaluate the performance of such systems. Students produce a final project using the methods learned in the subject, based on actual clinical data. (Required for students in the Master's Program in Medical Informatics, but open to other graduate students and advanced undergraduates.)

MASLab (Mobile Autonomous System Laboratory) is a robotics contest. The contest takes place during MIT's Independent Activities Period and participants earn 6 units of P/F credit and 6 Engineering Design Points. Teams of three to four students have less than a month to build and program sophisticated robots which must explore an unknown playing field and perform a series of tasks. MASLab provides a significantly more difficult robotics problem than many other university-level robotics contests. Although students know the general size, shape, and color of the floors and walls, the students do not know the exact layout of the playing field. In addition, MASLab robots are completely autonomous, or in other words, the robots operate, calculate, and plan without human intervention. Finally, MASLab is one of the few robotics contests in the country to use a vision based robotics problem.

Relationship between computer representation of knowledge and the structure of natural language. Emphasizes development of the analytical skills necessary to judge the computational implications of grammatical formalisms, and uses concrete examples to illustrate particular computational issues. Efficient parsing algorithms for context-free grammars; augmented transition network grammars. Question answering systems. Extensive laboratory work on building natural language processing systems. 6.863 is a laboratory-oriented course on the theory and practice of building computer systems for human language processing, with an emphasis on the linguistic, cognitive, and engineering foundations for understanding their design.

Neural structures and mechanisms mediating the detection, localization, and recognition of sounds. Discussion of how acoustic signals are coded by auditory neurons, the impact of these codes on behavorial performance, and the circuitry and cellular mechanisms underlying signal transformations. Topics include temporal coding, neural maps and feature detectors, learning and plasticity, and feedback control. General principles are conveyed by theme discussions of auditory masking, sound localization, musical pitch, speech coding, and cochlear implants, and auditory scene analysis.

This section is designed to build confidence about what Generative Artificial Intelligence (GenAI) means for the future of education by closely studying the operations, limitations, and theoretical value of a Large Language Model (LLM) like ChatGPT. To this end, this section seeks to explain what language modeling is and how this process contributes to an LLM’s tendency to generate inaccurate information. Additionally, this section considers how the design of an LLM—specifically, the collective knowledge it is trained upon—can contribute to the perpetuation of biases. Lastly, this section encourages critical thinking about the value of an LLM from a theoretical standpoint regarding the writing process and collaborative learning. By the end of this section, you should be able to articulate how an LLM like ChatGPT operates, as well as the value and limitations of this design within the evolution of learning.

Author: Mary Landry
Contributors: Gwendolyn Inocencio, C. Anneke Snyder, Jonahs Kneitly
Designers: Irene AI, Shweta Kailani
Supervisors: Terri Pantuso, Sarah LeMire

Increasingly, we are realizing that to make computer systems more intelligent and responsive to users, we will have to make them more sensitive to context. Traditional hardware and software design overlooks context because it conceptualizes systems as input-output functions. Systems take input explicitly given to them by a human, act upon that input alone and produce explicit output. But this view is too restrictive. Smart computers, intelligent agent software, and digital devices of the future will also have to operate on data that they observe or gather for themselves. They may have to sense their environment, decide which aspects of a situation are really important, and infer the user's intention from concrete actions. The system's actions may be dependent on time, place, or the history of interaction. In other words, dependent upon context. But what exactly is context? We'll look at perspectives from machine learning, sensors and embedded devices, information visualization, philosophy and psychology. We'll see how each treats the problem of context, and discuss the implications for design of context-sensitive hardware and software. Course requirements will consist of critiques of class readings [about 3 papers/week], and a final project [paper or computer implementation project].

This book provides an overview of the field of natural language processing and recently developed methods, presuming only knowledge of computing with data structures.

In this section, you will gain insights about privacy and confidentiality concerns related to a form of Generative Artificial Intelligence (GenAI) known as Large Language Models (LLMs) and, specifically, OpenAI’s policies about ChatGPT.

The full extent of privacy and confidentiality risks in relation to ChatGPT, which relies on collective intelligence for information gathering and dissemination, has not been fully realized. Users should be mindful of OpenAI’s terms of use, particularly as those terms are subject to change. Though OpenAI claims to not share private user information, the language around such statements is vague and contradictory, and there is a strong possibility that personal information may be monitored by human proctors. Moreover, educators who are bound to the legal obligations outlined in FERPA should be particularly concerned about how student privacy could be potentially violated by using ChatGPT and other GenAI technologies.

After reading this section, you should be able to articulate and discuss OpenAI’s significant terms of use and privacy policy, consider the potential privacy and intellectual property violations contained within the collective intelligence paradigm, and communicate your own concerns about privacy and confidentiality in relation to GenAI technologies.

Author: C. Anneke Snyder
Contributors: Gwendolyn Inocencio, Mary Landry, Jonahs Kneitly
Designers: Irene AI, Sweta Kailani
Supervisors: Terri Pantuso, Sarah LeMire

This course is an introduction to the theory that tries to explain how minds are made from collections of simpler processes. It treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality. It incorporates ideas from psychology, artificial intelligence, and computer science to resolve theoretical issues such as wholes vs. parts, structural vs. functional descriptions, declarative vs. procedural representations, symbolic vs. connectionist models, and logical vs. common-sense theories of learning.

A graduate-level introduction to artificial intelligence. Topics include: representation and inference in first-order logic; modern deterministic and decision-theoretic planning techniques; basic supervised learning methods; and Bayesian network inference and learning.

The nature of human identity - how we think of ourselves, how we perceive others - is a mutable concept, changing with the rise and fall of religious beliefs, social mores, philosophical theories. Today, we live in a world in which science and technology are among the most powerful forces reshaping our culture - and thus our definitions and perceptions of identity. In this seminar, we will examine the impact of science and technology on identity.

Social media, digital devices, and networked communication systems have entered our collective bloodstream. This e-book touches upon the human experience of contemporary trends that affect how we perceive ourselves, others, and society.

This resource offers student-focused tutorials that demonstrate how ChatGPT can augment the writing process for assignments commonly given in a rhetoric and composition course. These tutorials cover the evaluation essay, rhetorical analysis, Rogerian argument, annotated bibliography, and research essay—all while promoting the responsible and ethical use of AI in writing and research. With this comprehensive resource, instructors and students can not only build confidence in their understanding of generative AI within academia, but also build digital literacy that will serve them in the world beyond.

Author: Mary Landry

By the end of this tutorial, you will be able to utilize a specific formation of generative AI (GenAI)—the prominent Large Language Model (LLM) ChatGPT—as an aid within the annotated bibliography writing process to

explore, evaluate, and refine a research question
brainstorm and determine effective search components and keywords
decipher complex ideas within academic articles

Additionally, you will critically reflect on ChatGPT’s place within the citation practices of an annotated bibliography. Specifically, you will consider why and how ChatGPT should be cited according to both MLA and APA.

This resource offers student-focused tutorials that demonstrate how ChatGPT can augment the writing process for assignments commonly given in a rhetoric and composition course. These tutorials cover the evaluation essay, rhetorical analysis, Rogerian argument, annotated bibliography, and research essay—all while promoting the responsible and ethical use of AI in writing and research. With this comprehensive resource, instructors and students can not only build confidence in their understanding of generative AI within academia, but also build digital literacy that will serve them in the world beyond.

Author: Mary Landry

By the end of this tutorial, you will be able to utilize a specific formation of generative AI (GenAI)—the prominent Large Language Model (LLM) ChatGPT—as an aid within the evaluation essay writing process to

develop specific assessment criteria
maintain a professional, unbiased tone
articulate the sociohistorical context of a subject

Additionally, you will be able to identify specific limitations with using ChatGPT for an evaluation essay, including its limited ability to perform evaluations itself.

This resource offers student-focused tutorials that demonstrate how ChatGPT can augment the writing process for assignments commonly given in a rhetoric and composition course. These tutorials cover the evaluation essay, rhetorical analysis, Rogerian argument, annotated bibliography, and research essay—all while promoting the responsible and ethical use of AI in writing and research. With this comprehensive resource, instructors and students can not only build confidence in their understanding of generative AI within academia, but also build digital literacy that will serve them in the world beyond.

Author: Mary Landry

By the end of this tutorial, you will be able to utilize a specific formation of generative AI (GenAI)—the prominent Large Language Model (LLM) ChatGPT—as an aid within the research paper writing process to

survey the ongoing discourse of research on a given topic
draft with different reasoning strategies
integrate sources and quotes

Additionally, you will critically reflect on the possible pitfalls in regards to originality and time management when using ChatGPT as an aid for composing a research paper.