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AI in Higher Education: Why I Had to Become an Expert (and Why We All Should)

  • lys8854
  • Aug 15
  • 2 min read

AI is not something I deliberately chose as a main professional focus. It chose me. In my work across instructional design, technology integration, quality assurance, legal AI frameworks, and digital credentials, AI simply surrounds me from all sides. At some point I realized: if I don’t fully understand it and use it strategically, I will be left behind.



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That's why I have completed several courses and workshops on AI and continue to actively develop my expertise, and share that knowledge with others.


Recently, I led another AI workshop, this time designed specifically for administration and office staff. This group plays a critical role in making AI adoption work in practice, but their perspective is often overlooked in AI strategies.


Workshop Focus

We discussed AI applications and implications that matter most to administrative and operational roles, including:

  • Process automation: scheduling, document processing, and reporting.

  • Data management & privacy: handling sensitive student and staff information in compliance with GDPR and the upcoming EU AI Act.

  • Workflow integration: embedding AI tools into everyday platforms (e.g., LMS, CRM, HR systems).

  • Content creation & communication: drafting templates, FAQs, and routine correspondence with AI assistance.

  • Quality & oversight: ensuring that AI outputs meet institutional standards and are checked by humans.

  • Change management: preparing for shifts in job roles and responsibilities as AI takes over repetitive tasks.


My Role and Contribution

As the workshop facilitator, I:

  • Created a framework and roadmap tailored to the needs of administrative teams.

  • Designed a visual "AI in Admin" map of potential applications, risks, and priorities.

  • Facilitated group discussions to capture practical insights from participants.

  • Mapped out learning paths and course plans for follow-up workshops, based directly on the exchange of ideas in the session.


Outcomes

  • A draft framework for responsible AI use in administrative and operational contexts.

  • Identification of quick wins where AI can save time without sacrificing quality.

  • Agreement on priority areas for further training and testing.

  • Strong participant engagement (from 30 attendees), feedback described the workshop as "eye-opening" and "urgently needed".


 
 
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