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Webinar Series Production Plan

Challenges and Opportunities: Using AI in the University Context

1. Purpose

To explore the main problems, risks, and dilemmas universities face in adopting artificial intelligence (AI) for teaching, learning, and administration — and to spark dialogue on ethical, pedagogical, and institutional solutions.

2. Target Audience

  • University faculty

  • Administrators and policy-makers

  • Students and academic support staff

  • Educational technologists

3. Duration

60–90 minutes (recommended breakdown below)

4. Webinar Structure

5. Core Problems to Discuss

A. Academic Integrity and Assessment

  • Risk of plagiarism and “AI-assisted writing.”

  • Difficulty distinguishing human vs. AI-generated work.

  • Overreliance on detection tools that are inaccurate or biased.

  • Need to redesign assessment (e.g., oral exams, process-based tasks).

B. Ethical and Legal Concerns

  • Privacy issues (student data used by AI systems)

  • Copyright and intellectual property — who owns AI-generated content?

  • Algorithmic bias and fairness in automated grading or admissions

C. Pedagogical Challenges

  • Faculty uncertainty about how to integrate AI into teaching.

  • Unequal access and digital divides between disciplines or regions

  • Risk of reduced critical thinking if students depend on AI too heavily

  • Lack of clear institutional guidance on responsible use

D. Equity and Accessibility

  • Not all students have equal access to AI tools or the same language advantages.

  • Commercial AI platforms may reinforce global inequalities.

  • Need for inclusive AI literacy that considers cultural and linguistic diversity.

E. Institutional Readiness

  • Universities may lack clear AI governance policies.

  • Faculty training and digital literacy gaps.

  • Need to balance innovation with academic tradition and ethics.

6. Possible Solutions / Recommendations

  • Develop AI Policies – Clear guidelines for acceptable use in teaching and research.

  • Promote AI Literacy – Train both staff and students to use AI critically, ethically, and transparently.

  • Redesign Assessment – Focus on process, reflection, and higher-order skills rather than product-based evaluation.

  • Ensure Transparency and Fairness – Select AI tools that are explainable, inclusive, and data-safe

  • Encourage Collaboration, Not Replacement – View AI as a co-pilot, not a substitute for academic expertise.

  • Support Research on AI in Education – Evidence-based policies and shared best practices across institutions.

7. Engagement Ideas

  • Live Poll: “Which AI tool is most used in your university?”

  • Scenario Discussion: “A student submits AI-generated work — what should the lecturer do?”

  • Quick Quiz: “Myth or Fact about AI in education.”

  • Collaborative Padlet: Participants share one AI success and one challenge from their institution.

8. Suggested Readings / Resources

  • UNESCO (2023). Guidance for Generative AI in Education and Research.

  • EDUCAUSE (2024). AI in Higher Education: Promise and Peril.

  • Floridi, L. (2019). Ethics of Artificial Intelligence.

  • JISC (UK). AI in Tertiary Education: Principles for Responsible Use

  • OECD (2023). AI and the Future of Teaching and Learning.

9. Optional Closing Reflection

“AI will not replace teachers — but teachers who know how to use AI will replace those who don’t.”

Invite participants to share one action step they’ll take to promote ethical AI use in their institution.

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