Research

Here, we have compiled a selection of emerging studies on Generative Artificial Intelligence. By providing a meticulously curated and accessible database, we hope to offer a reference point for researchers and professionals looking to learn more from credible sources. We hope this resource serves the Georgetown community by deepening awareness of AI’s ethical, societal, and practical implications. While studies evolve and quickly become outdated, they form an important history to the scholarly conversation about the developments AI has wrought in education. By highlighting significant studies, we aim to promote a well-rounded understanding of Generative AI, including both its potential and its challenges in today’s world.

Theoretical Foundations

Foundational studies that cover generative AI’s core principles and theoretical frameworks, including essential research that shapes the foundation of AI.

ImageNet Classification with Deep Convolutional Neural Networks (2012) by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton
Attention is All You Need (2017) by Vaswani et al.
Learning Representations by Back-Propagating Errors (1986) by Rumelhart, Hinton, and Williams
Long short-term memory (1991) by Hochreiter and Schmidhuber
Generative Adversarial Nets, (2014) by Goodfellow et all
A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955 (1955) by John McCarthy, Marvin L. Minsky, Nathaniel Rochester, and Claude E. Shannon
Playing Atari with Deep Reinforcement Learning (2013) by Volodymyr Mnih et al.
A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) by Warren McCulloch and Walter Pitts
Computing Machinery and Intelligence (1950) by Alan Turing
Some Studies in Machine Learning Using the Game of Checkers (1959) by Arthur Samuel
Neural Probabilistic Language Models (2003) de Yoshua Bengio et al.

Impact in STEM

Publications demonstrating the application and impact of generative AI across Science, Technology, Engineering, and Mathematics.

Assessment

Addresses AI’s role in enhancing evaluative practices, including automated grading, feedback personalization, and support for various assessment types.

Includes discussions on AI’s influence on privacy, emotional well-being, societal structures, and the development of responsible AI practices that promote equity and transparency.

Support for Academic Research and Data Analysis

Covers AI’s role in enhancing research methodologies, optimizing data processing, and enabling researchers to generate valuable insights and efficient solutions across disciplines.

Grading, Feedback, Personalization, and Creativity

Resources highlight how AI can support individualized education by adapting content to students’ unique needs and inspiring creative thinking.

Tutoring Resources, Generating Course Materials, Hypothetical Scenarios, and Multilingual Content

Includes resources on tutoring, creating course materials, developing hypothetical scenarios for experiential learning, and generating multilingual content.