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.

Enhancing Mental Health and Wellbeing through AI Innovations

Within PK-12 and higher education, there is the need to address mental health and well-being concerns of students and educators as US society struggles with how to address historic systemic inequities, environmental destruction and the lack of socially just political outcomes. This interactive session will first provide a visual overview of publication trends and research on AI, education, and well-being. Then attendees will engage in a thoughtful discussion and reimagining of two trends within an educational context with the goal of building next steps and collective engagement on the possibilities of creating schools, colleges and universities that advance equitable well-being for all.

Improving Learner Engagement: AI and Humanization

The proliferation of artificial intelligence (AI) has predominantly been driven by adult-dominated, profit-motivated, and secretive individuals and organizations. Conversely, a focus on learner engagement (especially with younger learners) can align incentives towards the humans and world(s) for which AI is supposedly designed.

 The three brief readings that form a foundation for June’s conversation present perspectives of young adolescents and adults who work closely with them. The examples include proofs of concept and liberatory stances on how AI can promote more equitable futures, if (co-)led by young people.

Synergizing AI with Teaching Strategies for Classroom Excellence

Participants in this session will explore generative AI tools like ChatGPT to see how they can support educators in routine tasks. They'll delve into a brief history of AI's development and limitations,  learn to access and register for ChatGPT, employ effective prompting strategies, and avoid pitfalls. Additionally, they'll discover how to integrate ChatGPT into instruction, fostering a discussion on academic integrity. The content aligns with Harvey Silver's "Thoughtful Classroom Tools for Instruction," illustrating how ChatGPT aids in Organization, Rules and Procedures, Engagement, and Creating a Culture of Learning.

New Ways to Interact with AI: Virtual, Augmented and Mixed Reality

In this panel discussion we discuss some of the promise and pitfalls of applications of AI in virtual reality and ways to mix what we see and feel between computer generated and real-world inputs. We discuss some of the key conceptual differences between ways to extend reality through technology, and highlight questions that such technologies leave us with. Finally, we discuss some of the ways that people might critically examine when these technologies support learning, and when they might cause more problems than they solve. 

Chris Hoadley, PhD

Professor, Learning and Instruction and Computer Science

Xintian Tu, PhD

Postdoctoral scholar, UB Institute for Learning Sciences

Erin Kearney, PhD

Department Chair, Learning and Instruction

Bridging Formal and Informal Learning via AI

Learning happens everywhere, all of the time. Learning in higher education involves student learning in formal settings (e.g., in the classroom or through online lectures) and informal settings (e.g., independent self-driving learning, study groups). While new AI technology is revolutionizing education, it is not perfect. In this session, we will explore ways to leverage the power of generative AI, while exploiting its vulnerabilities to improve formal and informal learning across a wide range of educational settings. 

The Song Need Not Remain the Same: AI Literacies in the Lives of Youth

This presentation explores the concept of AI literacy in education, highlighting the urgency driven by policymakers and media to address the AI challenge for youth. It critiques the tendency to treat AI literacy as a universal set of skills and argues for a more situated approach that considers the social and ideological contexts in which youth engage with AI. Drawing on over 40 years of literacy studies, the presentation advocates for understanding AI literacies as diverse and context-dependent practices rather than as a standardized, autonomous skill set. It concludes by suggesting ways to reframe AI literacy research and practice through ethnographic and critical lenses.

Recommended Reading

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

Responsible AI in K-12 Education

Responsible AI in K-12 education refers to the development, deployment, and use of AI technologies that are guided by ethical, social, and educational principles to ensure a positive impact on students' learning experiences and outcomes. This presentation will begin by reviewing AI policies and guidelines at international, national, and local levels, and identifying gaps in responsible AI. Following this, participants will engage in a discussion around the challenges and ethical considerations of AI deployment and usage in K-12 classrooms. Finally, the presenter will introduce the newly funded national Center for Early Literacy and Responsible AI (CELaRAI) and outline how it plans to address responsible AI in K-12 settings, offering opportunities for participant involvement.