Curriculum Integration Workshop
Series Overview
As Generative AI (GenAI) becomes increasingly accessible and influential in education, faculty, staff, and students need new skills and strategies to navigate this evolving landscape. The Curriculum Integration Workshop Series is a free professional development opportunity designed to help educators build confidence and competence in using GenAI tools and methods.
This four-part series offers targeted, hands-on workshops that support both foundational learning and advanced application. Whether you’re new to GenAI or looking to deepen your expertise, the series provides practical guidance for integrating AI into teaching, learning, and assessment.
The Curriculum Integration Workshop Series is a free professional development opportunity for students, faculty, and staff who want to expand their competencies and utilization of GenAI tools and methods. The targeted workshops are designed to build foundational knowledge and practical skills in using Gen AI tools.
This series is a collaboration between Texas A&M’s
Institute of Data Science
Center for Teaching Excellence
University Libraries
Workshop Summaries
- GenAI Foundations (Part L): Establishes a foundational understanding of GenAI tools, methods, applications, and limitations.
- Mapping AI Learning Outcomes (Part A): Helps educators define discipline-specific AI competencies, align GenAI use with learning goals, and develop evaluation criteria and best practices.
- Designing AI-Enhanced Learning Experiences (Part B): Explores how to integrate GenAI into existing course content and adapt assignments or activities using appropriate tools and methods.
- Building AI-Resilient Courses (Part C): Focuses on designing assessments that respond to GenAI use, detecting and managing misuse, and anticipating future impacts of GenAI on curriculum design.
Workshop Series Fall 2025 Schedule
- Sep 19 — Reimagining Assignments: A Partnership Between Instructor and AI [C.2]
- Sep 23 — Gen AI Foundations Workshop for Beginners [L.1]
- Sep 26 — Using Generative AI as a Tool to Assist Building a Course [A.1]
- Oct 3 — Student-Centered Learning Experience Design with Generative AI [B.1]
- Oct 9 — Using Generative AI as a Tool to Align Course Learning Outcomes [A.2]
- Oct 16 — Gen AI Foundations Workshop for Intermediate [L.2]
- Oct22 — Navigating Creative Commons Licensing in the Age of AI [C.1]
- Oct 23 — Gen AI Foundations Workshop for Beginners [L.1]
- Nov 4 — Using Generative AI as a Tool to Assist Building a Course [A.1]
- Nov 7 — Purposeful Personalized Learning with Generative AI [B.2]
- Nov 11 — Navigating Creative Commons Licensing in the Age of AI [C.1]
- Nov 17 — Using Generative AI as a Tool to Align Course Learning Outcomes [A.2]
- Nov 19 — Student-Centered Learning Experience Design with Generative AI [B.1]
- Dec 3 — Purposeful Personalized Learning with Generative AI [B.2]
GenAI Curriculum Integration Certification
The Generative AI Curriculum Integration Certificate is a professional development opportunity for faculty and staff who complete the Curriculum Integration Workshop Series. To earn the certificate, participants must attend one GenAI Foundations (Part L) workshop and one workshop from each of the three advanced modules: Mapping AI Learning Outcomes (Part A), Designing AI-Enhanced Learning Experiences (Part B), and Building AI-Resilient Courses (Part C).
This certification empowers educators to thoughtfully and responsibly integrate GenAI into teaching, research, and academic workflows. Participants will gain practical skills, ethical insights, and strategic approaches to foster innovation while upholding academic integrity in an AI-enhanced learning environment.
GenAI Foundations
The GenAI Foundations workshops are offered in both Beginner and Intermediate levels to accommodate varying experience with AI and technical backgrounds. Each session introduces core AI concepts, explores the strengths and limitations of Generative AI tools, and provides hands-on practice using no-code tools and methods to leverage GenAI effectively.
Faculty and staff are encouraged to select the workshop level that best matches their current familiarity with AI. Completion of both levels is not required; participants only need to attend one Foundations workshop to progress through the Curriculum Integration series.
Workshop for Beginners [L.1]
This interactive workshop explores how generative AI can support the course design process from start to finish. Participants will examine the course design cycle, identify key opportunities for AI integration, and consider the value these tools bring to course planning. Attendees will leave with strategies for using AI to design a course. Organized by the Institute of Data Science.
Learning Objectives
- Define Generative AI (GenAI) and distinguish it from traditional AI approaches.
- Identify commonly used GenAI tools (e.g., ChatGPT, DALL·E, Copilot) and describe their practical applications in everyday tasks.
- Explain the basic strengths and limitations of GenAI, including issues like hallucinations and bias.
- Recognize key ethical considerations and discuss principles for responsible use of GenAI in educational contexts.
- Demonstrate how to use no-code GenAI tools to complete simple tasks such as summarizing text or generating images.
- Apply basic prompt engineering techniques to improve the quality and relevance of GenAI outputs.
Fall 2025 Date:
Spring 2026 Date:
TBD
Workshop for Intermediate [L.2]
For faculty and staff with a technical background who are interested in deepening their understanding of Generative AI (GenAI) and its applications. Participants will explore the distinctions between GenAI and traditional AI, critically evaluate popular tools, and analyze their strengths and limitations. Organized by the Institute of Data Science.
Learning Objectives
- Differentiate Generative AI (GenAI) from traditional AI approaches by analyzing underlying architectures and learning paradigms (e.g., transformers vs. rule-based systems).
- Evaluate commonly used GenAI tools (e.g., ChatGPT, DALL·E, Copilot) and assess their suitability for specific tasks.
- Analyze the strengths and limitations of GenAI, including technical challenges such as hallucinations, bias, and model interpretability.
- Implement no-code GenAI tools to automate or augment tasks such as summarization, image generation, and data transformation, with attention to reproducibility and transparency.
- Design and refine prompts using principles of prompt engineering to optimize GenAI performance across varied contexts (e.g., teaching, research, grant writing).
Fall 2025 Date:
Spring 2026 Date:
TBD
Mapping AI Learning Outcomes
Focuses on defining discipline-specific AI competencies for students, aligning AI use with learning goals, developing rubrics or criteria for evaluating AI-related work, and best practices for teaching about AI.
Using Generative AI as a Tool to Assist Building a Course [A.1]
This interactive workshop explores how generative AI can support the course design process from start to finish. Participants will examine the course design cycle, identify key opportunities for AI integration, and consider the value these tools bring to course planning. Attendees will leave with strategies for using AI to design a course. Organized by the Center for Teaching Excellence.
Learning Objectives
- Describe the course design cycle.
- Identify potential integration points of generative AI in the course design cycle.
- Discuss the value of using these tools in building a course.
Fall 2025 Date:
Spring 2026 Date:
TBD
Using Generative AI as a Tool to Align Course Learning Outcomes [A.2]
This workshop highlights the importance of aligning course learning outcomes with content and demonstrates how generative AI tools can support this process. Participants will review the purpose of learning outcomes, evaluate alignment strategies, and explore practical ways to use generative AI to enhance alignment. Participants are encouraged to bring a syllabus to use during the session. Organized by the Center for Teaching Excellence.
Learning Objectives
- Describe the purpose of course learning outcomes.
- Evaluate the alignment of learning outcomes with course content.
- Use generative AI tools to align learning outcomes with course content.
Fall 2025 Date:
Spring 2026 Date:
TBD
Designing AI-Enhanced Learning Experiences
Focuses on identifying opportunities to integrate GenAI into existing course content, which tools or methods best fit those opportunities, and how to design or adapt assignments or activities using GenAI.
Student-Centered Learning Experience Design with Generative AI [B.1]
This workshop introduces instructors to the principles of student-centered learning experience design, with a focus on integrating generative AI tools available at Texas A&M University. Participants will explore how generative AI can support personalized, engaging, and enhanced learning environments, and will apply these insights to design a student-centered learning module using TAMU-supported generative AI platforms. Organized by the Center for Teaching Excellence.
Learning Objectives
- Explain key features of student-centered learning experience design.
- Explore how generative AI can support student-centered approaches
- Design a student-centered learning module utilizing TAMU AI available tools.
Fall 2025 Date:
Spring 2026 Date:
TBD
Purposeful Personalized Learning with Generative AI [B.2]
This hands-on workshop guides instructors through the principles of personalized learning, emphasizing intentional design and learner agency. Participants will explore strategies for engaging students as co-designers in their learning journeys and discover how generative AI tools, especially those available through Texas A&M University, can support differentiated instruction and adaptive learning. By the end of the session, participants will design a personalized learning module that integrates generative AI to meet diverse learner needs with clear instructional intent. Organized by the Center for Teaching Excellence.
Learning Objectives
- Discuss the principles of personalized learning.
- Explore strategies for engaging students as co-designers in personalized learning experiences using generative AI
- Design a personalized learning module that integrates generative AI to meet diverse learner needs with clear instructional intent
Fall 2025 Date:
Spring 2026 Date:
TBD
Building AI-Resilient Courses
Focuses on how GenAI affects traditional assessments, designing assessments that either integrate and respond to AI use appropriately, detecting and managing AI misuse, and how future developments of GenAI will impact courses.
Navigating Creative Commons Licensing in the Age of AI [C.1]
This workshop introduces instructors to open licensing through Creative Commons and details the six licenses and their permissions that allow use, reuse, and revision without violating copyright laws. Participants will also see how these licenses allow material with Creative Commons licenses to be ingested into AI tools to create ancillary materials for use in their courses. Organized by the University Libraries.
Learning Objectives
- Discuss Creative Commons Licenses and how they work with copyright.
- Explore strategies for using Creative Commons licensed material to create AI-integrated assignments that promote critical thinking.
Fall 2025 Date:
Spring 2026 Date:
TBD
Reimagining Assignments: A Partnership Between Instructor and AI [C.2]
Join our workshop to discover the transformative potential of AI in education. You’ll learn to recognize the value of integrating AI into classroom assignments, review effective methods for adapting these assignments, and explore practical examples that foster critical thinking. This session is designed to equip educators with the tools and insights needed to enhance student engagement and learning outcomes through AI integration. Organized by the Center for Teaching Excellence.
Learning Objectives
- Recognize the value of adapting classroom assignments with AI integration in mind.
- Review methods to adapt classroom assignments for AI integration.
- Explore examples of AI-integrated assignments that promote critical thinking.
Fall 2025 Date:
Spring 2026 Date:
TBD
Program Contact
Drew Casey
Associate Director, Texas A&M Institute of Data Science