AI Confrontation Pedagogy: Learning to Think WITH and AGAINST AI in Francophone African Universities

Team name: MILIA

Use of AI tools :

Which tool(s) did you use?

We used several artificial intelligence tools, including:

• ChatGPT

• Google Gemini

• Claude

For what purpose(s)?

These tools were used to:

• Structure and clarify our ideas;

• Draft and improve sections of our contribution;

• Compare AI-generated responses to culturally specific Senegalese questions;

• Identify cultural biases and epistemological limitations in AI-generated content;

• Prepare our policy proposal and video pitch content.

How did these tools support your work?

AI tools allowed us to directly experience and investigate the problem we are addressing. By comparing their responses on topics related to Senegalese law, history, and local knowledge systems, we were able to document concrete examples of cultural bias in AI-generated outputs.

They also helped us organize our thinking, improve the clarity of our arguments, and transform our lived student experience into a structured, realistic, and actionable policy proposal.

External feedback & contributions : Please list the people who supported you during the process AI Grand Challenge mentors – Feedback, comments, and recommendations during Phase 2

Initial contribution: AI Confrontation Pedagogy: Teaching African Students to Think Against AI

Final contribution: Our contribution proposes a new university assessment protocol called AI Confrontation Pedagogy.

Rather than banning artificial intelligence or attempting to detect its use, we propose transforming AI use into an object of critical learning.

The protocol is based on three steps:

1. Generate

The student uses an AI tool to produce a first answer to an academic question.

2. Confront

The student critically analyzes the AI-generated response by identifying:

• factual errors;

• cultural biases;

• missing African references;

• epistemological assumptions embedded in the response.

3. Defend

The student presents and defends their critique, then proposes an improved answer grounded in their own cultural and academic context.

The goal is to develop a new essential competency: critical AI literacy.

We identified three major tensions:

1. Academic integrity

Traditional assessment methods can no longer reliably distinguish student-produced work from AI-generated content.

2. Cognitive development

Students who outsource their reasoning to AI risk progressively losing their capacity for critical thinking and independent argumentation.

3. Cultural sovereignty

Only 0.2% of AI training data originates from Africa. Uncritical AI use exposes Senegalese students to the gradual replacement of local knowledge systems by Western-centered epistemologies.

Our policy proposal includes three concrete measures:

1. A national AI assessment standard for Senegal
We propose that the Senegalese Ministry of Higher Education (MESRI) adopt a national directive requiring universities to integrate critical AI competency into their assessment systems by 2026.

2. A teacher training module
A short, fully offline-compatible training program covering:

• how generative AI works;

• the limitations of AI systems;

• how to design AI confrontation assessments;

• AI cultural bias in African educational contexts.

3. An AI Literacy Passport for students
A 90-minute self-paced module enabling students to understand:

• how AI works;

• how to identify AI bias;

• how to transparently document AI use.

Reflection on the process

How did your contribution evolve during Phase 2?

At the beginning, our project mainly focused on the issue of AI cultural bias in Senegalese universities.

Through peer discussions, mentor feedback, and expert inputs, we expanded our analysis to also address:

• academic integrity challenges;

• the long-term cognitive impact of AI dependency;

• the need for a practical institutional policy response.

What feedback influenced your work?

The most valuable feedback emphasized:

• the need for a directly actionable solution;

• the importance of including a pilot testing methodology;

• clearer implementation conditions;

• strengthening the policy dimension of our proposal.

What changes did you make as a result?

As a result, we:

• structured our solution into a clear three-step protocol;

• added a pilot workshop with pre/post data;

• developed an official policy recommendation for MESRI;

• strengthened the cultural sovereignty dimension of our argument.

How did this process strengthen your final proposal?

This process allowed us to transform an initial student concern into a more rigorous, realistic, and persuasive proposal.

Our project is now:

• grounded in field-based investigation;

• tested with real students;

• supported by a concrete implementation strategy;

• directly addressed to a specific public institution.