AI and Teaching (Pedagogy) - Discussion Summary

July 14, 2026 Opening Remarks: Dr. Meghan McInnis-Domínguez, Associate Professor of Spanish

Classroom use, academic integrity, guidance and guardrails.

A. Current State: What is already happening?

● Pedagogy is strategy vs. teaching is technique

● Very divided – embrace and repel CTAL resources and information for best

● practices (evolving)

● Challenged to completely rethink the way professors teach

● Course vs. curriculum perspective – it’s ad hoc

● Many use AI because resources are limited

● UD had not defined AI, there is not definition of AI

● Varying capacity to prioritize discussion and implementation

● Use of AI could increase if resources (e.g., journals) not available

● Students crave AI experience

B. Opportunities: Where can AI strengthen UD’s work?

● Administrative time saving (reinvest time into intentional work)

● Instructional design support

● Purpose/value proposition and need for clearer articulation

● Accessibility

● Assessment and shift to more in-person teaching

● Research advancement and data application

● Emphasize firsthand, experiential learning that helps students recognize what generative AI cannot fully capture, including experiences such as study abroad.

C. Risks and Guardrails: What concerns, risks, or guardrails require attention?

● Tools

○ Professors wanting different tools

○ Teachers trying to keep up with evolving methods and products

○ Paid vs. free

○ Privacy

○ Ownership of content    

● Who sets the standards?

○ Departments

○ University

○ Professors

○ Students

● Liability issues

● Environmental dangers

● How to teach thought processes

○ Cognitive processing

● Alternative assessment

○ Presentations (time sync)

○ In-class assessments

● Uncontrolled vendor expansion

● Students are AI Guinea pigs

● Evaluating student output

○ AI plagiarism

○ Slop

● Disclosure of use of AI

○ Professors and students should disclose when using AI

● Subsidized now, what about the future?

D. Support Needed: What training, policies, resources, or capabilities are needed?

● What is the institutional guidance on which LMs to use/not use and for what?

● Solve for today and into the future

● Google Notebook:

○ Who will teach us how to use/training

○ Who will have access

○ What are the limitations

○ Departmental reps who train/teach their departments

● Central places for training ○ Hub for students/faculty

○ Resources

○ Application use

● Assessment: Where is it in our core curriculum? Gen Ed?

● Policy support

○ AI updates and changes too fast; problem keeping up

○ Policy focused on what NOT/CANNOT do with AI at the University

○ Policy surrounding transparency

● Data security

● Desire for an institutional repository

○ Sanctioned tools

○ Training

○ Course materials and AI integration ideas

○ How to on prompts

○ Link prompts associated with course exerciseLessons

○ Syllabi language

○ Activities

● Departments/colleges need to have their own discussions

○ Accreditation: what is required for the units

● Ethics and Family Educational Rights and Privacy Act

○ What is ethical? What is not ethical? (grading learning, assignments)

● UD needs to be able to defend pedagogical understanding with AI

● Teaching in many tools of pedagogy

● Understand the current AI literacy

● Student level needs

○ Heat map of curricula

○ Matrix of when/how AI is being used

E. Next Steps: What actions, pilots or decisions should UD consider?

● Develop a shared working definition of AI for teaching and learning while recognizing faculty authority over course design and instruction.

● Create a centralized AI teaching hub with tools, training, guidance, assessments, and effective classroom practices.

● Pilot an assessment of faculty and student AI literacy to identify guidance, training, range, and support needs.

AI and Student Learning - Discussion Summary

July 21, 2026

Opening Remarks: Erin Sicuranza, Director, Academic Technology Services

Durable skills, career readiness and academic program evolution.

A.  Current State: What is already happening?

●      Very siloed within UD

●      Copilot and Gemini chat

●      Library has AI Literacy program

●      Handshake (one-stop shop for off-campus jobs and other career resources)

○      AI Fellowships in the last 6 months (90 students)

●      First State AI: Working group for students

●      Student facing chat bot currently with Admissions, IT, Library

●      ATS (Academic Technology Services) - UD study aide

●      Coursera: UD Google AI certificate program

●      Canvas has the capacity for AI training - not yet activated

●      MEEG one of the only department with AI communication in the curriculum.

●      Paul J. Rickards, Jr. Teaching Innovation Grant for teaching innovation

●      Considerations for Integrating AI Within Teaching and Learning

B.  Opportunities: Where can AI strengthen UD’s work?

●      Is AI a pedagogical tool or curriculum topic?

●      Is this about student learning or student success?

●      How do we create an equitable learning environment for AI?

●      When introducing AI what assessments can be used to evaluate what students learned?

○      Reinvestment in durable skills (Information Literacy)

○      Skills transcript: AI skills learned with undergrad

●      Make AI Literacy apart of the First Year Experience

○      Have intro course a part of new student orientation

○      How are students exposed to AI Literacy?

●      UD has the opportunity to serve the community by partnering with high schools to help combat AI literacy gap

●      Bridge the ‘arms race’ gap. Students will find a way to use AI with or without UD.

●      Determine what sources UD will use to gain AI information

●      Learn more about ethical implications

●      Provide opportunities to learn more about how AI algorithms work; learn more about prompting.

●      Create more individual AI courses

●      How is the uptake of AI the same as vs.different from the introduction of calculators

●      Now opportunity to conduct a survey to understand how AI is being used now within the UD community

●      Value in plotting AI readiness along these dimensions:

AI and Research, Innovation, and Workfoce Development — Discussion Summary

July 28, 2026

Opening Remarks: Sunita Chandrasekaran, Associate Professor, Computer and Information Sciences
Discovery, translation, competitiveness, industry needs and workforce alignment.


A. Current State: What is already happening?
Discovery:

●      AI development and research are happening across individuals, labs, institutes, and departments, but these efforts are often siloed and not consistently shared

Translation:

●      AI research and tools are not yet easily translated across internal units or from higher education into practical workforce applications

Industry Needs:

●      Impact on entry level positions and job availability.

●      Due to the need in industry, UD needs to change how students are prepped for employment

●      Industry wants demonstrable abilities not certificates. Ability to compete within workforce

Workforce Alignment:

●      Students without AI skills are behind

●      Students need fundamental skills and understanding

●      DuPont is currently using AI to reduce R&D by 10x

●      MDavis uses AI in their network behavior analysis tools

●      Fundamental knowledge is more important for job success that AI literacy

●      Students are more effective when they have the skills to direct AI

B.  Opportunities: Where can AI strengthen UD’s work?

Discovery:

●      Expanded AI-enabled research opportunities for students and faculty across disciplines

●      Use of AI to analyze, organize, and synthesize large datasets and information

●      Baseline findings to support grant applications and research scaling

●      Greater interdisciplinary collaboration across colleges, departments, institutes, and research centers

●      Showcasing innovative applications such as the Poison Book Project and other research collaborations

Translation:

●      Mechanisms to share AI-related research, tools, pilot programs, and best practices across campus

●      A central hub or communication network for AI projects, resources, expertise, and events

●      Campus-wide events such as AI in Action to encourage interdisciplinary engagement and knowledge sharing

●      Translation of AI research and innovation into practical industry and workforce applications

Competitiveness:

●      A secure AI sandbox with access to multiple models and guardrails for privacy, security, and cost

●      Clear measures of when and why AI should be used, how it adds value, and how success is defined

●      Strategies for managing token costs, institutional demand, and responsible scaling

●      Processes that allow UD to respond quickly to changing technologies and workforce needs

●      An integrated institutional strategy connecting teaching, research, innovation, and workforce development

Industry Needs:

●      Stronger engagement with industry partners and alumni to align UD’s AI strategy, programs, and research with evolving needs

●      More opportunities for students to learn directly from industry professionals and alumni

●      (reverse) Mentorship connecting students with professionals across generations

●      Integration of technical AI knowledge with human-centered workplace skills

●      AI-powered simulations and role-playing tools for communication, decision-making, and workplace scenarios

Workforce Alignment:

●      Greater student understanding of how AI is affecting careers, job opportunities, and workforce expectations with an understanding that it is a rapidly changing environment

●      Understanding of the responsible, effective, and ethical use of AI in professional settings

●      Continued development of critical thinking, communication, problem-solving, and other durable human skills

●      Clearer distinction between AI-enabled critical thinking and uniquely human skills

●      Guidance on appropriate and inappropriate uses of AI

C.  Risks and Guardrails: What concerns, risks, or guardrails require attention?

Concerns:

●      UD’s capacity to keep pace with rapidly changing AI tools, demand, and training needs

●      Erosion of research, communication, critical-thinking, problem-solving, and judgment skills

●      Lack of clarity around ethical use, citation, research quality, and platform-specific risks

●      Difficulty keeping coursework and institutional guidance current

Risks:

●      Use of unauthorized AI tools or personal subscriptions without appropriate review

●      Exposure of sensitive data or intellectual property

●      Inability to fully assess or control data, security, compliance, and cost risks

●      Poor prompts, limited user knowledge, and inadequate review of AI-generated information

●      Adoption of AI solutions that are unreliable, impractical, or difficult to scale

Guardrails:

●      Provide clear guidance on what data may and may not be entered into AI systems

●      Establish standards for IRB-, HIPAA-, FERPA-, and other protected information

●      Tailor security and compliance requirements to the needs of different units

●      Require appropriate review and verification of AI-generated information

●      Assess whether AI tools improve learning outcomes and strengthen critical-thinking skills

Questions for Consideration:

●      How much can UD trust AI platforms with internal information and data?

●      Should UD develop a secure, university-supported AI environment?

●      How many AI platforms can UD responsibly support?

●      How can instructors identify inappropriate AI use?

●      Do users understand the costs associated with AI?

●      What skills are needed now and in the future?

●      Could overly restrictive guardrails limit research and innovation?

D.  Support Needed: What training, policies, resources, or capabilities are needed?

Trainings:

●      Training on all the tools and tool usage

●      High tailored training for all the different subgroups within the University — based on experience

●      Create an ambassador program within UD to help support large group training

Policies:

●      Could UD present a position statement? Difference between a position statement and policy statement.

●      Does every college and/or department need to have its own position statement umbrella under the UD policy?

Resources:

●      Infrastructure

●      Criteria to determine whether AI appropriate to use in situations

○      Criteria to determine which AI tools are appropriate to use

What Is Needed:

●      The development of a secure, university-supported AI environment that faculty, staff, and students can use confidently

E.  Next Steps: What actions, pilots or decisions should UD consider?

●      Pilot a secure internal AI sandbox with approved tools and guardrails for privacy, security, compliance, and cost.

○      This should contain a crowd-sourced repository of prompt templates and techniques organized by research discipline to facilitate safe adoption of AI assistance in research where appropriate.

○      Should also include citation templates to acknowledge use.

●      Expand industry and alumni engagement to align student preparation and university research with evolving workforce needs

●      Develop a university-wide position statement (vs. policy statement) on responsible AI use, with flexibility for colleges and departments to establish specific guidance

Topics: Administrative modernization, efficiency, and faculty/staff support

Topics: Monitoring AI progress, staying current and adapting responsibly as the technology evolves