Library Guides: ChatGPT, AI, and Implications for Higher Education: Resources for Instructors

Instructors are still developing their thoughts about how to respond to this new technology. Some are eager to incorporate it into their classroom. Others hope to mitigate the impact on traditional learning. Whether you would like to "ChatGPT-proof" your assignments or invite students to engage with it thoughtfully, there are resources from other educators to help you do so.

Practical Resources for Instructors

Update Your Course Syllabus for ChatGPT | Ryan Watkins

A Toolkit for Addressing AI Plagiarism in the Classroom | CommLit.org and Quill.org

Practical Responses to ChatGPT | Office for Faculty Excellence, Montclair State University

Five Ideas for How Professors Can Deal with GPT-3 ... For Now | George Fox University

AI and the Future of Academic Integrity | UNLV Best Teaching Practices Expo

Chat GPT and Student Writing: Some Practical Reflections | Blog of the APA

AI-Generated Content in the Classroom: Considerations for Course Design | Center for Integrated Professional Development at Illinois State University

19 Ways to Use ChatGPT in Your Classroom | Education Week

ChatGPT Advice Academics Can Use Now | Inside Higher Ed

Classroom Activity: How Can AI be exploited to create disinformation? | The News Literacy Project

Unlocking the Power of Generative AI Models and Systems such as GPT-4 and ChatGPT for Higher Education | University of Hohenheim

Explore more resources in the detailed guides linked below.

AI in the Classroom
A guide from Butler University
Chat GPT and Education
A guide from Northern Illinois University's Center for Innovative Teaching and Learning
Faculty Help: ChatGPT's Impact on Higher Education
A guide from Santa Fe Community College
AI Text Generators Sources to Stimulate Discussion among Teachers
A compilation of resources from the Writing Across the Curriculum Clearinghouse.

Professors Turn to ChatGPT to Teach Students a Lesson; The powerful paper-writing chatbot presents an educational challenge: Ban it or build on it?
Belkin, Douglas. Wall Street Journal (Online); New York, N.Y. [New York, N.Y]. 25 Jan 2023.
How Do Students Feel About OpenAI's ChatGPT? The artificial-intelligence chatbot has surprised users with its ability to write colloquial responses
Wall Street Journal (Online); New York, N.Y. [New York, N.Y]. 03 Jan 2023.
How teachers can defeat ChatGPT-using students
McGrath, David. Chicago Tribune; Chicago, Ill. [Chicago, Ill]. 28 Jan 2023: 5.
Scores of Stanford students used ChatGPT on final exams, survey suggests
Mark Allen Cu Sebastian Hochman. University Wire; Carlsbad [Carlsbad]. 22 Jan 2023.
Don’t Blame Students for Using ChatGPT to Cheat When college education is rendered transactional, a generation trained to use technological tools to solve problems is just doing what it’s told.
How ChatGPT robs students of motivation to write and think for themselves
Baron, Naomi S. The Conversation : Education; Boston [Boston]. 19 Jan 2023.
Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others.
Automatic detection of students’ affective states in classroom environment using hybrid convolutional neural networks Predicting the students’ emotional and behavioral engagements using computer vision techniques is a challenging task. Though there are several state-of-the-art techniques for analyzing a student’s affective states in an e-learning environment (single person’s engagement detection in a single image frame), a very few works are available for analyzing the students’ affective states in a classroom environment (multiple people in a single image frame). Hence, in this paper, we propose a novel hybrid convolutional neural network (CNN) architecture for analyzing the students’ affective states in a classroom environment. This proposed architecture consists of two models, the first model (CNN-1) is designed to analyze the affective states of a single student in a single image frame and the second model (CNN-2) uses multiple students in a single image frame. Thus, our proposed hybrid architecture predicts the overall affective state of the entire class. The proposed architecture uses the students’ facial expressions, hand gestures and body postures for analyzing their affective states. Further, due to unavailability of standard datasets for the students’ affective state analysis, we created, annotated and tested on our dataset of over 8000 single face in a single image frame and 12000 multiple faces in a single image frame with three different affective states, namely: engaged, boredom and neutral. The experimental results demonstrate an accuracy of 86% and 70% for posed and spontaneous affective states of classroom data, respectively.
Prediction of Student Performance in Massive Open Online Courses Using Deep Learning System Based on Learning Behaviors Massive open online courses (MOOCs) provide numerous open-access learning resources and allow for self-directed learning. The application of big data and artificial intelligence (AI) in MOOCs help comprehend raw educational data and enrich the learning process for students and instructors. Thus, we created two deep neural network models. The first model predicts learning outcomes on the basis of learning behaviors observed when students watch videos. The second is a novel exercise-based model that predicts if a student will correctly answer examination questions on relevant concepts. The study data were collected from two courses conducted on the National Tsing Hua University’s MOOCs platform. The first model accurately evaluated student performance on the basis of their learning behaviors, and the second model efficiently predicted student performance according to how they answered the exercise questions. In conclusion, our AI system remedies the present-day inability of MOOCs to evaluate student performance. Instructors can use the systems to identify poor-performing students and offer them more assistance on a timely basis.
Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning
ChatGPT vs. Participatory Online Learning? Participatory Learning (Mostly) Wins!
You’re Not Going to Like How Colleges Respond to ChatGPT
Teaching In The Age Of AI Means Getting Creative
ChatGPT and AI Are Raising the Stakes for Media Literacy