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Artificial intelligence presents both exciting opportunities and complex challenges for the world, profoundly affecting the ways we live, work, learn and relate to one another.


At the University of Delaware, our faculty, researchers and students are at the nexus of these rapidly evolving issues every day. This interdisciplinary academic work and cutting-edge research draws on the University’s expertise and resources in data science, computer science, public policy, business, engineering and more. The University also aims to be a leader in the application of AI in the higher education classroom through an innovative tool under development with national partners. The AI for Teaching and Learning Working Group provides thoughtful guidance as the University creates and updates its policies and practices related to artificial intelligence.

Current Initiatives

Created in May 2023, the AI for Teaching and Learning Working Group represents the broad educational landscape of the University. The working group meets monthly to help guide the University of Delaware in the development of innovative practices and sound policies regarding the use of emerging AI tools.


Michael David Evans, Director of Computing Operations, Lerner College of Business and Economics

Meg Grotti, Associate University Librarian for Learning, Engagement, and Curriculum Support

Erin Sicuranza, Director, Academic Technology Services

Joshua Wilson, Associate Professor, College of Education and Human Development


Charge from the Provost


The arrival of open-source artificial intelligence (AI) tools presents both fundamental challenges and exciting new opportunities for institutions dedicated to teaching and learning. Traditional modes of undergraduate instruction and assessment are the most obvious areas for reconsideration, given AI’s powerful generative capacities. Research methods and ethics are also impacted by AI, including the applied research and creative endeavors pursued by our faculty across all disciplines and the graduate students they train. This dynamic and rapidly evolving situation demands careful thought, creativity, and agility as we develop practices that harness the power of AI for teaching and learning while maintaining our commitments to academic integrity in the production and dissemination of knowledge.


The purpose of the AI for Teaching and Learning Working Group is to help guide the University of Delaware over the next two academic years as we develop innovative practices and propose sound policies regarding the use of emerging AI tools. The Working Group will play a key role in fulfilling the University’s partnership with the Ithaka S+R two-year initiative, “Making AI Generative for Higher Education,” but its scope is not limited just to that project. In addition, the Working Group is charged with exploring and providing specific guidance on


  • pedagogy

  • curriculum development (including general education and First-Year Seminar goals)

  • the assessment of student learning and program educational goals

  • academic integrity

  • research ethics


The Working Group will be formed in Summer 2023 and will operate during the 2023-2024 and 2024-2025 academic years. A key part of its work should be collaborative outreach to faculty, staff, and students beyond the membership of the Working Group, particularly with UD’s AI Center for Excellence, academic senior leadership, the Faculty Senate, the Graduate Council, and the Ithaka S+R “Making AI Generative for Higher Education” initiative. Regular dissemination of the Working Group’s progress should be provided via the monthly Provost’s Digest, a dedicated Web page, and a formal report to the Provost and the Faculty Senate in May 2024 and May 2025.


— May 24, 2023

Working Group Membership


Cory Bart, Associate Professor, Computer and Information Sciences

Stephan Bohacek, Associate Professor, Electrical and Computer Engineering, Computer and Information Sciences

Sunita Chandrasekaran, D&B Mills Career Development Chair, Computer and Information Sciences, and Co-Director, Artificial Intelligence Center of Excellence

Susan Conaty-Buck, Assistant Professor, School of Nursing

Rachel Coppola, Director, Life Design and Career Integration, Career Center

Trevor Dawes, Vice Provost for Libraries and Museums and May Morris University Librarian

Phil Duker, Associate Professor, School of Music

Michael David Evans, Director of Computing Operations, Lerner College of Business and Economics

Mike Fernbacher, Assistant Director, Community Standards and Conflict Resolution

Eric Greska, Associate Professor, Kinesiology and Applied Physiology

Meg Grotti, Associate University Librarian for Learning, Engagement, and Curriculum Support

Kevin Guidry, Associate Director, Office of Educational Assessment, and Assistant Professor, School of Education

Jevonia Harris, Senior Digital Media Programmer, Academic Technology Services

Amy Hicks, Associate Professor, Art and Design

Fred Hofstetter, Professor, School of Education and School of Music

Ethan Kempista, master’s student, College of Engineering

Matt Kinservik, Vice Provost for Faculty Affairs

Julius Korley, Associate Vice President, Office of Economic Innovation and Partnership

Agnes Ly, Associate Professor, Psychological and Brain Sciences

Charissa Powell, Head of Student Success and Curriculum Partnerships Department, Library, Museums and Press

Tom Powers, Associate Professor, Philosophy

Teya Rutherford, Associate Professor, School of Education

Erin Sicuranza, Director, Academic Technology Services

Matt Trevett-Smith, Director, Center for Teaching and Assessment of Learning

Dana Veron, Associate Provost, Professor, School of Marine Science and Policy

Joshua Wilson, Associate Professor, College of Education and Human Development

The purpose of this series is to help UD faculty and staff start to grapple with how AI might change the nature of teaching and learning in higher education by understanding foundational concepts related to AI and large language models (LLMs), implications of AI and LLMs within different higher education disciplines, and implications of AI and LLMs on the future of work. 


Multiple in-person and virtual seminars will provide insight into the broad topics of:

  • AI: Strengths, Limitations, Ethics, and Accessibility

    • This first part of the series will inform the audience of foundational concepts related to understanding and contextualizing AI in life and society. These concepts relate to understanding what AI and LLMs are and how they work, their strengths and limitations, and ethical considerations related to their construction and utilization.

  • Implication of AI for Teaching and Learning and the Future of Higher Education

    • This part of the series will engage the audience in considering how AI and LLMs may change how we teach and, considering the topics in Part 2, what the future of higher education looks like. 

  • Preparing Students for the Future of Work

    • These sessions will engage the audience in considering how AI and LLMs are changing the nature of work in major industries, and the implications of those changes on how we prepare students to enter those fields. Sessions in this part of this will illustrate current initiatives taken in certain disciplines to provide AI-related education and training to UD students and/or sharing debates and concerns within a field/program/department. 


Most seminars are hybrid events, and registration is required. When registering, please denote whether you plan to attend virtually or in person.


Upcoming seminars:

Information about upcoming seminars will be posted here when it becomes available.


Previous seminars:


Large Language Models — Challenges and Opportunities

Sept. 26, 2023 — Large language models (LLM) such as ChatGPT are changing many aspects of our lives, such as how we access online information and how we learn. This talk explored the mystery of LLMs and discussed the challenges and opportunities in this new era of AI.  

Watch the recording of the seminar here


Ethical Considerations in AI

Oct. 19, 2023 — This seminar presented two engaging talks. One delved into ethical and conceptual issues surrounding Generative AI, particularly Large Language Models (LLMs), in the context of research misconduct. The second explored the duty of teachers to support students in learning to use AI and to resist the temptation to limit teaching to the tools that happened to be available in the past.

Watch a recording of the seminar here.


Pitfalls and Promise: Generative AI, Disability, and Access

Nov. 6, 2023 — This talk considered the duality of two realities disabled people face in the context of their everyday lives, and specifically, generative AI. The first, considerations of people with disabilities and our experiences are often left out of decision-making processes which produce policy, like those being made within the university and individual classrooms. The second, we are often the "test market" and expert users of many tools used by the masses to gain more physical and electronic access to the world around us, like closed captioning, descriptive audio, voice to text, and other predictive and generative AI tools. 

Watch a recording of the seminar here.


Navigating the AI Landscape: A Framework for Evaluating Assessment Tools in Higher Education

Nov. 29, 2023 — Advanced automated tools, including generative AI open a world of new possibilities for assessing student learning. They can provide immediate, personalized feedback. But determining if a particular tool should be used is complex with many potential questions and decisions. The presenters shared a draft framework that will help faculty make well-informed decisions about the use of AI tools to assess student learning.

Watch a recording of this seminar here. Download the slides here.


AI and the Future of Higher Education

Dec. 7, 2023 — The integration of AI into academia presents unparalleled opportunities for enhancing core university missions of teaching and research, but it also poses significant challenges, from misinformation threats to concerns about equity, intellectual property, and undermining critical thinking. The presenters surveyed the national landscape to see how public research universities are responding to the moment and consider how UD can promote the AI literacy of our students and harness AI's transformative power responsibly.

Watch a recording of this seminar here.


AI and AI – The Intersection of Artificial Intelligence and Academic Integrity

Jan. 17, 2024 — This session addressed how students can use AI while still upholding standards of academic integridy and how faculty can define and encourage appropriate use of AI. This session also covered how faculty may identify inappropriate use of AI as well as the range of response options when AI-assisted academic dishonesty occurs.

Watch a recording of this seminar here.


Ethical Use of AI Tools for Research and Publishing 

Feb. 15, 2024 — New tools for quickly and easily finding research publications are exploding in popularity and are being used extensively on campus right now. AI can be controversial: Is it ethical? Does it replicate bias? Can using it infringe copyright? Sometimes the answers to these questions are complicated. This seminar covered effective ways for thinking through these questions when you are exploring particular tools. 

A video recording of this seminar will be posted here when it becomes available.


AI-Powered Career Advancement: Navigating Career Preparation, Recruitment, and Lifelong Learning

Feb. 28, 2024 — Lifelong learning and career preparedness are crucial in our fast-paced, technology-driven economy. Predictions about job security have rapidly shifted over the past few years, highlighting the importance of adaptability, skill development, critical thinking, and problem-solving for both traditional and emerging roles. This session explored the best practices for coaching students in their use of Generative AI in career exploration and job/internship searching as well as how employers utilize Generative AI for recruitment. The session covered how we can support students to be enterprising in their learning for career exploration and reskilling throughout their lives.

Watch a recording of this session here.


Virtual AI Information and Demo Session for Faculty

April 24 and 29 and May 3, 2024 — This seminar explored the opportunities and challenges of integrating generative AI into your teaching practice. This virtual event highlighted insights gathered throughout an AI seminar series held this past year and featured lightning talks by UD faculty who have firsthand experience implementing these tools into their teaching. 

Watch a recording of Phil Duker, Associate Professor in the School of Music, here


Implications of AI on Training the Next Generation of Human Resources Professionals 

May 2, 2024 — Generative AI is profoundly impacting Human Resource (HR) development. Using a framework drawn from the presenter's co-edited book project on this topic, this seminar delved into the evolving role of AI in HR, highlighting both the opportunities and challenges it presents. 

Watch a recording of this seminar here


AI Demo and Information Session

May 3, 2024 — Following an introduction by Matt Kinservik, Vice Provost for Faculty Affairs, a series of lighting talks are offered by Xuan Cai of IT- Academic Technology Services, Ju-a Hwang Assistant Professor of English, and Persephone Braham, Professor of Spanish and Latin American & Iberian Studies. 

Watch a video recording of this seminar here

The AI Literacy Tutorial was developed by the UD Library, Museums & Press as a resource for students and faculty to start a conversation about appropriate use of artificial intelligence.

The tutorial provides an overview of how large language models (LLM) like ChatGPT work, the limitations of their use, and ethical considerations for the appropriate use of LLMs. Specifically, it describes hallucinations (i.e., when the LLM generates false information), asks you to consider bias embedded within AI, and the appropriate uses for AI tools.

The tutorial takes about 30-40 minutes to complete.


How can faculty use the tutorial?

  • To spur a discussion between you and your students acknowledging AI tools and education

  • To help you and your students collaboratively craft a policy about appropriate and inappropriate uses of AI tools within your specific course

  • To introduce content about bias

  • To introduce content about ethics


How can students use the tutorial?

  • To learn the basic concepts of AI

  • To increase awareness of the appropriate and inappropriate uses of AI

  • To consider bias, ethics and other considerations related to AI

AI Literacy & Tools: Managing AI Use in Your Classroom

Nov. 16, 2023 — This workshop featured common AI terminology, development of assignments that promote the ethical use of AI, syllabus template language for the purposes of using or not using chatbots for learning, and faculty- and student-centered resources for understanding and teaching with chatbots.

The University of Delaware's AI for Teaching and Learning Working Group is part of a two-year partnership with 19 universities to assess AI’s impact on higher education and evaluate institutions’ readiness to implement the technology. 



This resource provides educators with guidance regarding ways to utilize AI within teaching and learning in ways that are effective, ethical, and equitable and that will empower educators and students alike.  


These are considerations, not requirements. Educators do not have to teach with or about AI, but there are good reasons to do so, and should someone wish to, here are some things to consider.

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