Foundations of AI for Business
Foundations of AI for Business
QQI Level 5 | Dublin College Dundrum | 2023–24
A non-technical course on artificial intelligence and machine learning tools for business students. The goal was to build critical literacy rather than technical skill — understanding how these tools work, what they claim to do, and how to evaluate those claims.
Introduction
Over the course of the year, we explored a series of concepts, tools, and best practices surrounding three distinct forms of what is generally called "Artificial Intelligence" — but which are better understood as specific categories of machine learning application. This involved dispelling myths, uncovering biases, and developing both practical facility and critical literacy: understanding how ML systems work, how they are sold to businesses, and how they are depicted in media.
The goal was to be better equipped to sort fact from fiction, use these tools effectively, ethically, and safely, and avoid the typical pitfalls of a field with an unusually high proportion of hype.
Session 1: Introductions
Getting to Know Each Other
Students wrote a short piece covering:
- Full name and preferred form of address
- Background (professional or personal)
- What brought them to this class
- What they hoped to learn
- An unexpected application or use of AI or machine learning they had seen in recent years — not the one that grabbed the most headlines, but the one that surprised, unsettled, or delighted them
They then converted their bullet points into a short paragraph, and then into a poem (limerick, sonnet, or simple rhyme scheme). The question running through this exercise: why am I asking you to write this poem? How does the task feel?
An alternative for those not comfortable with poetry: translate the text into another language.
Some Questions for Us to Consider
- How do we determine a tool is fit for purpose?
- What data questions might we ask of machine learning applications?
- What concerns might we have of new AI/ML tools?
- What resources might we use to better educate ourselves on this and related topics?
- How do we know if a source is credible?
- What kind of media literacy did we need to navigate the digital world before AI/ML? What types of literacy are now more, or less, important?
- How can an AI/ML application save time or effort? Where should we be sceptical of time-saving promises?
- How can we gain the computer literacy needed to use these tools?
Session 2: Exploring Basic Tools
Before discussing how AI and ML work, we explored several tools together. Many applications students use daily have AI/ML components they may not be aware of.
When examining any tool, three lenses are useful:
- The right context in which to use it
- What difficulties and pitfalls might arise
- How to engage with it safely and conscientiously
Key Questions for Each Tool
- What does the AI portion of this tool claim to do? How fast or easy is it to use?
- What data is collected?
- How is that data used?
- What concerns might arise?
- What are the tool's limitations?
Tools Explored
- Google Docs (summarise, shorten, bullet points, convert to table, simplify language, elaborate)
- Google Sheets
- Grammarly
- Coda
- Notion
- Canva
- Figma
Example: Establishing Baselines
Before using any summarisation or generation tool, students wrote their own version of a text first. Then the tool's output was compared to what they produced. This established a baseline and made the tool's outputs legible as transformations of something, rather than as authoritative outputs.
Course Structure
Class summaries, reflection questions, and assignments were posted to the course page. Students were invited to email with questions or requests for additional feedback.
Material was supplemented with LinkedIn Learning, introduced once students had built a solid conceptual foundation.
For a sense of how this course relates to the AI workshops for teachers, see AI Workshops for Teachers.
