AI & Society College

Project Title: Incorporate AI-assisted Tools into Teaching Public and Nonprofit Financial Management

Project Summary:

I am working on a Faculty Innovation Fellowship project that integrates AI tools into the teaching of public and nonprofit financial management, aiming to prepare students to use AI tools for financial analysis effectively, responsibly and ethically.

It emphasizes both the ability of AI tools to support financial management tasks and the importance of using human judgment to verify AI-generated outputs, ensure transparency, and document assumptions, limitations, and procedures. 

The project includes four phases:

• The research phase uses practitioner surveys and interviews to identify emerging AI applications and skill needs in public-sector budgeting and financial management. 
• The learning phase explores the capabilities of AI tools for budgeting, forecasting, financial modeling, and data visualization. 
• The design and implementation phase will redesign teaching materials and assignments for RPAD 501: Public and Nonprofit Financial Management, a core MPA course. Through these materials, students will practice using AI to analyze financial data and communicate results. 
• The assessment and sharing phase will examine students’ learning progress and challenges and share the results and teaching materials with colleagues and peer instructors.

My project involves creating unique experiential learning opportunities using an AI tutor that I’ve created in select courses. These experiential learning opportunities come in two formats.

Collaborative projects are intended to give students the opportunity to work as a team with their AI tutor on interesting and motivating projects and scenarios. The AI tutor handles the peripheral or rote work, leaving the student to focus exclusively on a specific subset of high-value learning objectives and the end result is something they can be proud of and excited about. 

The second format, dynamic scenarios, place students directly into a simulated scenario, where they can directly apply concepts from the course. 

In both cases, the student gets practical experience working with AI in a way that mimics their future workplace, and they do so in a pedagogically-sound way that ensures they learn the skills they need to succeed.

This project aims to create a computer simulation that will help students practice cold case analysis techniques.

This project examines the human side of AI use in the workplace. As AI becomes part of everyday work, people need to understand not only how AI can support decisions, but also when to question it, how to recognize risks, and how to remain responsible for AI-supported work. 

The project focuses on how AI shapes judgment, risk awareness, and accountability in organizational settings, with the goal of informing more responsible and human-centered AI use at work.

The aim of this project is to develop a learning opportunity for social work students to explore the use of artificial intelligence (AI) in human services and healthcare settings, with a particular focus on mental health support and therapeutic practice. 

The meaning and powerful potential of AI, as well as the cautions required to use it, will be considered and infused into the social work curriculum. 

Project Title: Integrating AI-Assisted Coding into Graduate Epidemiology Education

Project Summary:

This project redesigns a core graduate statistics course, EPI 553 (Principles of Statistical Inference II), to thoughtfully integrate AI coding tools such as Claude Code into the teaching of regression methods. 

Using a scaffolded model, students move from AI-free coding fundamentals, to guided AI-assisted analysis, to independent AI-augmented projects. Along the way they learn not only statistical methods but also how to critically evaluate AI-generated code and use these tools responsibly as professional analysts. 

The work includes a revised curriculum, a study of student learning outcomes and a cross-departmental faculty workshop on adopting AI-assisted coding in quantitative courses across UAlbany.

Examination of how AI translation tools shape mental health literacy in refugee and immigrant communities and co creation of culturally responsive multilingual resources through direct community engagement.

Mini conference, “The Audio-Visual Record in the Age of AI” for media makers, historians and the public, with additional support from SUNY’s Conversations in the Disciplines initiative.

AI and Democracy: Critical Questions Speaker Series, in collaboration with Dr. Haesol Bae.

Partnering with New York public school communities to foster AI critical literacy. Through reflective reading and writing practices, the project engages teachers, students, and community members to become critical consumers of AI ecologies and harness AI infrastructures to support social justice education.

About Daniel R. Coutu 

Daniel R. Coutu is a PhD candidate in Philosophy at UAlbany, researching the philosophy of artificial intelligence, ethics and phenomenology. 

His work approaches the philosophy and ethics of AI in government through a phenomenological lens, with particular attention to authority, responsibility, self-deception and the limits of delegating human judgment to machines. 

A central aim of his research is to distinguish the many tasks AI can valuably assist. Rather than rejecting the technology, he asks how AI can be used well: matched to the tasks it suits and kept from those it does not.
 

About Daniel R. Coutu's Dissertation

This dissertation examines the ethics of artificial intelligence in government, starting from the view that AI is a genuinely useful tool that can improve public life when used well. The problem it addresses is what happens when AI is treated as more authoritative and transformative than it actually is. This gap between what AI does and what it is said to do encourages people and institutions, especially in government, to treat AI outputs as objective grounds for action, quietly handing over decisions and responsibility that should remain human.

Drawing on real cases of AI used in government decision-making, the project shows how much authority AI is granted in public life regardless of what it can actually do, examines why people are so willing to defer to it, and asks which tasks we can reasonably delegate to AI and which we cannot. The aim is not to reject the technology but to use it wisely: matching its real strengths to the right tasks while keeping responsibility where it belongs.

About Lu Gao

Lu Gao is a PhD candidate in Information Science and a research member of the Leadership Analysis and Influence Operations Laboratory (LA/IO) at the College of Emergency Preparedness, Homeland Security, and Cybersecurity at the University at Albany, SUNY. 

She also serves as a research project assistant on the AI & Misinformation team within the AI Plus Collaborative Research Experience (AI Plus CoRE) at the AI & Society Research Center.

Her research interests lie at the intersection of Information Science and Digital Government, particularly in examining the adoption and impact of AI in state and local governments as well as major societal and ethical challenges using a multidisciplinary approach. 

Her research not only addresses the technical dimensions of AI but also explores how state and local governments implement AI technologies, map essential resources, and create public value. She is also interested in exploring AI governance and legislation, focusing on AI concepts and the state-level regulatory framework in the United States.
 

About Lu Gao's Dissertation

AI in local governments can yield numerous positive effects, including improved public service provision, decision-making and policymaking, and citizen engagement. However, research suggests that value from AI applications and adoption is conditioned to local government capabilities and resources. 

Studies show that local governments are often less prepared and lack the resources and capabilities needed to carry out responsibilities as efficiently as other levels of government. Therefore, more needs to be understood about how public organizations can strengthen their organizational capacity and promptly deploy AI applications. 

My dissertation explores how local governments develop AI capabilities by linking and mapping combinations of internal resources for deploying AI applications. I use two theoretical lenses, including the Resource-Based View (RBV) of the firm theory and AI capability, to assess organizational capacity and readiness to implement AI technologies in county government agencies in New York State through a comparative case study approach. 

The findings will help public managers map important resources to successfully develop AI applications while strategically meeting organizational goals and creating public value.

About Zhongguo Huang

I am a PhD candidate in Environmental Health Sciences at the University at Albany, currently in the final stage of my doctoral research. My work stands at the intersection of artificial intelligence (AI), public health and sustainability, driven by a mission to leverage AI for mitigating hunger and enhancing population health.

Throughout my academic journey, I have been driven by the evolving role of data science in solving public health crises. This path has led me to a crucial realization: AI is no longer just a tool, but a cornerstone of public health and social equity. My commitment to excellence is evidenced by my 19 peer-reviewed publications in top-tier journals, such as Environment International (Impact Factor: 10).

My work is inherently collaborative. I maintain long-standing partnerships with Feeding New York, the Regional Food Bank of Northeastern New York, the New York State Department of Health and the Multiscale RECIPES for Sustainable Food Systems Research Network. By bridging the gap between bench science and frontline policy, I ensure my AI models are grounded in the logistical realities of food banks and urgent public health needs.
 

About Zhongguo Huang's Dissertation

In the United States, 40% of food is lost or wasted, annually costing an estimated $218 billion or 1.3% of the gross domestic product (GDP). Fruits are among significant contributors due to their perishable nature. Food donation reduces food waste and alleviates hunger, ranking as the second most preferred strategy in the food waste management scale suggested by the U.S. Environmental Protection Agency. 

However, donated food often has a limited remaining shelf life or is close to expiration, and its nutritional value may decrease over time, especially for fresh and perishable items. The central challenge of food donation system is the difficulty in maximizing environmental and recipients’ health benefits, when collecting and redistributing the perishable and nutritious food. 

This dissertation develops an AI-driven framework using electronic nose (E-nose) and computer vision (CV) to assess donated fruit quality, evaluate the environmental and health impacts of food decay, and optimize decentralized redistribution networks to minimize waste and maximize societal benefits. 

This research explores AI’s potential to alleviate hunger. Moving beyond technical metrics, it advocates for recipient dignity and positions AI as a vital tool for public health, environmental sustainability, and social justice. 

About Anastasios Karnazes

Anastasios Karnazes is a PhD candidate in English Studies at UAlbany, researching the material transformation from book technologies to artificial intelligence (AI) systems and its effects on aesthetic production.

He is the author of Rainbow Sonnets 20, published by The Song Cave, and founder of Theaphora, an experimental book and game publisher. His work has been covered in Artforum, ARTnews, Spike Magazine, and Forbes.
 

About Anastasios Karnazes' Dissertation

This dissertation positions artificial intelligence as the exhaustion point of book logic — the moment when Enlightenment systems of knowledge production, replication, and distribution culminate in computational infrastructure and become fully legible as a unified financial, political, and aesthetic project.

The work traces how information compression functions transformed through the printing press, encyclopedia, digital storage, networked infrastructure, and cloud computing to the memory systems undergirding AI development, arguing that this transformation refracts into dual domains: the collapse of financial systems into speculative bubbles and the aesthetic-cultural reconfigurations that respond to this trajectory.

Drawing on both historical analysis of dissemination infrastructures and documentation of experimental work in publishing and text editor development, the dissertation demonstrates the prefiguring relationship between literary production mechanisms and computational intelligence systems, positioning AI not as revolutionary break but as the terminal form through which the book's historical trajectory becomes visible.

About Yingru Zhao

Yingru Zhao is a PhD Candidate in Curriculum and Instruction, Department of Educational Theory and Practice (ETAP), School of Education at the University at Albany, SUNY.

She is also a member of the Critical AI Learning Lab in ETAP, where her work aligns with the lab’s mission to support educators in developing critical AI literacy and responsible approaches to AI-supported teaching and learning.

Her research examines critical AI literacy, epistemic agency, teacher learning and human-AI collaboration in education. Her current work explores how teachers critically engage with AI tools in lesson design and how AI-mediated learning environments can support pedagogically meaningful, ethically grounded and socially responsible educational practice.
 

About Yingru Zhao's Dissertation

This dissertation examines how K-12 teachers develop and exercise epistemic agency when working with generative AI. As AI becomes increasingly involved in lesson planning, knowledge production, and classroom interaction, teachers must make critical decisions about when, how, and why AI should participate in learning. 

This study positions teachers as epistemic designers who govern how knowledge, responsibility, and judgment are distributed among teachers, students, and AI systems. Through a multi-layered mixed-methods design, the dissertation explores teachers’ critical AI literacy development, AI-infused lesson design practices, and classroom orchestration of student-AI interaction. The goal is to develop a framework for pedagogically meaningful and epistemically responsible AI integration in K-12 education.

The College encourages undergraduate students interested in artificial intelligence to explore the following courses:

• UUNI 118: Introduction to Artificial Intelligence
• CINF 135: Concepts of Artificial Intelligence

Visit the Schedule of Classes to explore upcoming course offerings.

Microcredentials are a collection of courses and certificates that help learners build in-demand skills and competencies.

• AI Plus Fundamentals: This for-credit program includes undergraduate courses, which can stack towards a degree.
• Artificial Intelligence for Business: This for-credit program includes graduate courses, which can stack towards a degree. 

Visit the Professional and Continuing Education (PaCE) website to learn more about microcredentials. 

Google and SUNY have partnered to give SUNY students, faculty and staff free access to Google AI learning opportunities.  

Visit the Free Google AI Certificates at SUNY webpage for more information.