GenAI Learning outcomes? | 🎓 |
I came across a post on LinkedIn which showed how expectations of GenAI skills are
sneaking into job descriptions. In the screenshot below, you can see such expectations yourselves: |
This job description then gave me an idea: how about reverse engineering our new learning
outcomes using the industry expectations? At the end of the day, one of our main objectives is to have employable
graduates.
At this point, little AI help was necessary. I fed the machine with the job description, then asked it to generate
relevant learning outcomes based on the expectations of the employer. Here are some of the best ones for your consideration:
• Knowledge and Understanding:
– Understand the fundamental concepts and applications of generative AI in business contexts.
– Recognize the potential benefits and limitations of generative AI in driving business outcomes.
– Familiarize yourself with various types of generative AI tools and their uses in different business functions.
• Competence and Skills:
– Develop hands-on experience with generative AI tools and platforms, applying them to real-world business problems
– Analyze and interpret the outcomes and insights generated by generative AI tools, making informed business decisions.
– Troubleshoot common challenges and limitations associated with generative AI, developing creative solutions to overcome them.
• Judgement and Approach:
– Critically evaluate the suitability of generative AI tools for specific business applications, considering factors such as data quality, bias, and ethics.
– Foster a culture of experimentation and innovation, encouraging the exploration of new generative AI applications and use cases.
– Embed a commitment to ongoing learning and professional development, staying adaptable in the face of rapidly evolving generative AI technologies and business landscapes.
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Those learning outcomes actually address the expectations in the
job announcement: practical uses, evaluating outcomes, and addressing challenges. Of course,
these learning outcomes are necessary, but not sufficient. We need to add other ones pertaining
to ethical and responsible use, critical thinking, fact-checking, human-ai collaboration etc.
Still, not bad for a start right? |
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