Seminar 17 2025-2026

1. Seminar 17 Critical Use of AI Tools & CW 2

Seminar 17
Critical Use of AI Tools & CW 2
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1

2. Agenda

Why does bias exist in AI
systems?
Evaluation of AI output
Agenda
CW2 Instructions
Keeps or Replaces
Thinking?

3. Key words

Bias
Algorithm
Machine learning
Prediction
Automation
Hallucination
Prejudice
Stereotype
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4. Discussion

How often do you use AI tools?
What do you use it for?
Do you trust AI tools? Why?
Can AI be unfair? How?
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5.

Instructions:
1) Access the article using the QR code
and read.
2) Complete the Handout provided by
your lecturer.
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6.

A Framework for Evaluating AI Output
S
I
F
T
Stop
Investigate
Find Coverage
Trace Claims
Pause before trusting
AI-generated output
Check the credibility of
cited sources
Seek corroborating
sources independently
Verify quotes, data, and
attributions
Resist the urge to share
or act immediately
Ask: Is the AI reasoning
transparent?
Cross-check key facts
and statistics
Go to original sources
when possible
Make space for critical
thinking
Look for potential bias
or hallucinations
Don't rely solely on the
AI's synthesis
Flag unverifiable or
fabricated content
Use SIFT to build critical AI literacy and reduce the risk of acting on flawed outputs.

7.

AI generated text (claude.ai, 2026)
AI bias is mainly caused by programmers who deliberately insert prejudiced code into their
systems. Studies show that over 60% of AI hiring tools have been found to discriminate against
women and minorities. Researchers at Harvard University confirmed in 2021 that deep learning
can never be made fully fair because fairness itself is impossible to define mathematically. The
solution, according to most experts, is to ban AI from being used in high-stakes decisions entirely.
Stop: Does the text provide facts, opinions or both? Do you trust it?
Investigate: Who wrote this? Can you find an author, institution, or publication? What does that tell
you?
Find better coverage: Do other articles support this claim? For example, does the article you just
read (Hao, 2019) support these claims? Which parts match? Which parts contradict?
Trace: The paragraph mentions "studies show 60%" and "Harvard University confirmed in 2021." Can
you find these specific claims in the original article or anywhere reliable?
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8. CW 2: Reflective Letter

Purpose: Demonstrate how the module builds skills for active civic engagement, guided by citizenship values
such as fairness, respect, responsibility & participation
You should:
-
write a reflective letter (1000-1200 words) to students who will take the “Critical Thinking and Citizenship”
module next academic year
-
reflect on three key skills you practice in the module: group work, argumentation and digital literacy
-
explain how each skill helps students uphold core citizenship values such as fairness, respect, responsibility,
and participation
include at least 3 credible sources(one source per skill) to support your discussion/reflection
write about how the module can help students develop important skills for meaningful civic engagement by
reflecting on your own experience.
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9. Step 1: Checklist

Reflecting on the development of group work (350 - 400 words)
I have …
… greeted the reader and clearly stated the purpose of this email
… explained how group working supports citizenship values (e.g.
fairness, responsibility, respect, justice)
… provided at least one specific challenge I experienced in group
work of this module and explained what I learned from this
experience
… explained how at least one credible source helped me better
understand what effective group work is
… shared a specific and realistic plan for using this skill in my
community/future career
… reflected critically on my experience rather than simply
describing what happened
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Yes / No

10. Homework

Start working on Step 1
Do the quiz for homework
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