Artificial Intelligence in Higher Education
The courses below are written to support both teaching-focused participants and higher education professionals in broader academic and administrative roles.
Course 1 provides a shared foundation and previews the areas explored more deeply in the remaining courses; Courses 2 and 3 are most directly focused on teaching, learning, assessment, and course design; Courses 4 and 5 are written for a broader higher education audience with role-specific applications.
Course Details
Course 1: Introduction to Artificial Intelligence (AI) for Higher Education
Course Date: October 13, 2026 – November 23, 2026
This introductory course helps higher education professionals build a practical foundation for understanding generative AI and related AI innovations. Participants examine how AI tools function, how different types of AI are used across disciplines and professional contexts, and how institutional policies shape responsible use. The course offers an introductory preview of assessment, course design, critical AI literacy as part of information literacy, and professional workflow topics that are explored in greater depth in the other certificate program courses.
Instructor: Allison Hosier
Recommended audience: All higher education professionals seeking a shared foundation in AI use and implications.
Learning Objectives:
- Understand the history and evolution of artificial intelligence tools.
- Distinguish generative AI from other types of AI, including predictive AI, discipline-specific AI applications, and emerging agentic AI tools.
- Explain, at a foundational level, how common AI tools work and identify key limitations, risks, and opportunities for higher education.
- Identify campus, system, or institutional policies that shape appropriate AI use, including privacy, transparency, and approved-tool considerations.
- Evaluate potential AI uses in teaching, learning, student support, academic work, and organizational practice with attention to ethical, accessibility, social, and environmental considerations.
- Identify AI-related topics which align with personal, disciplinary, or professional goals.
Course 2: Generative AI in Assessment, Student Learning, and Academic Integrity
Course Date: To be Announced
This course explores how generative AI is reshaping assessment, student learning, and academic integrity. Participants consider how AI can support or interfere with learning, including cognitive offloading, user agency, and responsible use. The course emphasizes assessment design that prioritizes learning outcomes, process, authenticity, higher-order thinking, and disciplinary purpose while reducing incentives for misuse. Participants also develop strategies for communicating about AI in learning, and the appropriate use or non-use of AI across teaching modalities.
Instructor: To be Announced
Recommended audience: Faculty and professionals who support teaching and assessment across in-person, online, hybrid, and blended modalities.
Learning Objectives:
- Analyze how generative AI may support, disrupt, or obscure learning, including the potential effects of cognitive offloading.
- Redesign assessments to emphasize metacognition, process, higher-order thinking, and disciplinary alignment.
- Develop transparent assessment guidelines that address responsible AI use, user agency, and options for those who do or do not want to use AI for all teaching modalities.
- Facilitate meaningful conversations about academic integrity, information authority, privacy, and responsible AI use.
Course 3: Generative AI for Course Design, Content Creation, and Teaching Practice
Course Date: To be Announced
This course helps participants integrate generative AI into course design, course content creation, and teaching practice. Using instructional design and inclusive teaching principles, participants explore how AI can support the development of learning activities, instructional materials, quizzes, practice opportunities, and interactive learning experiences. The course emphasizes responsible use of AI to enhance engagement, creativity, critical thinking, and accessibility while maintaining academic rigor, disciplinary alignment, and human judgment. Strategies are applicable across all teaching and learning modalities.
Instructor: To be Announced
Recommended audience: Faculty and teaching-support professionals designing courses, learning activities, instructional materials, or student-facing resources.
Learning Objectives:
- Apply instructional design principles to identify where AI may appropriately support learning outcomes, assessments, activities, and course materials.
- Use generative AI to draft, adapt, or refine instructional materials, activities, practice opportunities, and student-facing resources.
- Evaluate AI-generated or AI-supported course materials for accuracy, bias, accessibility, inclusivity, and alignment with course goals.
- Explore AI-supported learning experiences that promote critical thinking, creativity, engagement, and academic rigor while accommodating student preferences for AI use.
Course 4: Critical Approaches to Generative AI Literacy and Ethics in Higher Education
Course Date: To be Announced
This course takes a broad, critical approach to AI literacy and ethics in higher education. Participants examine how information literacy encompasses AI and digital literacy within responsible professional practice. Participants will explore the ethical boundaries of AI use through the lenses of the following topics: bias and fairness, accessibility, reliability, data privacy and security, institutional policy, intellectual work and originality, user agency, environmental impact, social justice. Assignments allow participants to tailor coursework to their role, audience, and campus context, whether they are supporting students, colleagues, programs, services, or institutional projects.
Instructor: To be Announced
Recommended audience: All higher education professionals seeking a shared foundation in AI use and implications.
Learning Objectives:
- Analyze how the ethical use of AI connects bias and fairness, accessibility, reliability, data privacy and security, institutional policy, intellectual work and originality, user agency, environmental impact, and social justice.
- Critically evaluate AI tools, outputs, and use cases in higher education for bias, fairness, accessibility, reliability, transparency, and ethical implications.
- Identify personal, professional, student-facing, and institutional boundaries for AI use, including policy, data privacy and security considerations.
Course 5: Generative AI for Professional Workflows in Higher Education
Course Date: To be Announced
This course focuses on using generative AI in professional practices and workflows across higher education. Participants explore how AI can support writing, communication, planning and organizing, research-related tasks,[FJ2.1] data-informed work (decision-making), administrative processes, and other responsibilities. The course moves beyond basic prompting to consider workflow improvement and implementation, custom tools or bots, and agentic capabilities where appropriate. Throughout the course, participants apply ethical and responsible use principles, including data privacy, transparency, institutional policy, accessibility, and the continued importance of human agency, expertise, and experience.
Instructor: To be Announced
Recommended audience: Higher education professionals interested in using generative AI to support professional practice, communication, research support, administrative tasks, or workflow improvement.
Learning Objectives:
- Analyze tasks where AI may appropriately support efficiency, quality, creativity, or decision-making to improve professional workflows.
- Explore workflow improvement generative AI capabilities, such as reusable prompt libraries, custom bots, or agentic tools, where appropriate.
- Evaluate professional AI use for data privacy and security, accessibility, tool transparency, use disclosure, policy compliance, and potential consequences.
Meet the Instructors
Allison Hosier
Course Pricing
How to Pay
Available payment methods are:
- Credit Card (Mastercard or Visa)
- CPD General Points
- SUNY Online+ Points
- Campus Check
- Journal Transfer
CPD General/Technical Points: To pay with CPD General or Technical Points, your campus must be a CPD Member. Check if your campus is a member. It is the responsibility of the registrant to determine if enough points are available to use BEFORE completing the registration process. Please contact your Campus Points Contact to determine points eligibility. If points are denied, the registrant is responsible for the payment.
SUNY Online+: To pay with SUNY Online+ Points, you must receive prior approval and you must submit a request via the SUNY Online+ CPD Points Approval Form
Campus Check: Prior campus approval is required. Make check payable to SUNY Center for Professional Development. Mail to SUNY CPD at the address below.
Journal Transfer (State Operated Campuses Only): Prior campus approval is required. An account number with authorizing signature for Journal Transfers is required within 48 hours. You must print and return the invoice that is included with the registration confirmation email.
Important: FULL payment is required 30 days from the date of registration. For more information click CPD Payment Terms and Conditions.
Registration
Fall 2026 Course Dates
Course 1: Exploring AI in Higher Education
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- October 13, 2026 – November 23, 2026
Contact Us
For programming questions, please contact Jamie Heron, SUNY Online Program Manager at jamie.heron@suny.edu. For registration questions, please contact Nick Krasoski, SUNY CPD Program Coordinator at nicholas.krasoski@suny.edu
The SUNY Center for Professional Development (CPD) supports a wide range of professional development opportunities for the academic, technical, and leadership communities across the SUNY System.