AI + Education Learning Community Series

Introduction to AI and Its Use in Educational Settings

To kick off the AI + Education Learning Community Series, this talk will provide a high-level introduction on artificial intelligence (AI) for the audience. Sample topics include some of the most fundamental concepts and principles behind those fancy AI (and machine learning) terminologies, including deep neural networks and ChatGPT. We will then provide a few concrete use cases for AI in the educational setting, including those to be developed at the National AI Institute for Exceptional Education.

Recommended reading list:

1. The History of Artificial Intelligence
2. Watson, ‘Jeopardy!’ Champion
3. AlexNet and ImageNet: The Birth of Deep Learning
4. Intro to Large Language Models

Dr. Jinjun Xiong
SUNY Empire Innovation Professor in the Department of Computer Science and Engineering and Director of the Institute for Artificial Intelligence and Data Science, School of Engineering and Applied Sciences, University at Buffalo

Dr. Brian Graham
Superintendent, Grand Island Central School District

4 to 5 p.m.

Leveraging Machine Learning for Personalized Education/Special Needs

This talk provides an introduction to how machine learning technologies such as generative AI can be used to power innovative applications for personalized education, especially for children with special needs. We explore cutting-edge foundational models (large language models, vision-language models), text-to-image generation models, and multimodal retrieval models, focusing on their use for creating custom educational tools for personalized learning, interventions for speech-language therapies, and screening and intervention for developmental language disorders such as dyslexia and dysgraphia. Join us to discover how advances in AI are being leveraged to open new frontiers for special education at the National AI Institute for Exceptional Education.

Recommended reading list:

1. Text-to-Image Generation
2. How Diffusion Models Work
3. Speech Sound Disorders
4. Contrastive Therapy

Navigating Ethical Implications of AI in Education

Artificial Intelligence (AI) has provided new personalized opportunities for education. The development of tools that have the promise to benefit education continues to develop rapidly. On the other hand, our understanding of the ethical and societal risks associated with AI technologies has not kept pace. This discussion will dive into some ethical issues educators confront when using AI in the classroom. Privacy, bias and discrimination, surveillance and autonomy will be discussed in the context of K-12 and higher education.

Rachael Hageman Blair
Associate Professor, School of Public Health and Health Professions, University at Buffalo

Rachel Kent

Career & Technical Education Teacher

Buffalo Public Schools Career & Technical Education

Gregory Conley

Instructional Technology Coach

Buffalo Public Schools

Dr. Loretta Frankovitch

Assistant Director of the Office of Academic Integrity

University at Buffalo

Ensuring Data Privacy and Security 

The first half of this session focuses on data security, while the second half centers on privacy implications. In turn, Hu and Shankar will explore policies striving to uphold the ethical use of young people’s data. The facilitators will look towards strategies a wide range of stakeholders can employ to strengthen their care for young peoples’ data. The session closes with resources for hosting data ethics dialogues and engaging in decision-making.

Christopher Proctor
Assistant Professor in the Department of Learning and Instruction, Graduate School of Education, University at Buffalo

Ryan Rish
Associate Professor in the Department of Learning and Instruction, Graduate School of Education, University at Buffalo