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Abstract

The idea is to frame a critical relationship with AI tools within our undisciplinary school. Students will look at different AI systems (Copilot, ChatGPT, image generators, data analysis tools) to identify gaps, biases and assumptions as well productive potential. I would like to co-design a structured activity to push for a conscious, expert and critical assessment of when, how and why AI could be engaged within SCFS learning journey and beyond.

Working in small groups, students focus on bias detection (missing perspectives, methodological flaws, cultural assumptions, AI hallucinations, oversimplifications, generalised statements and much more鈥�); then, they identify sectors where AI provided genuine value (in a critical thinking/SCFS/undisciplinary context), for example spotting patterns, generating frameworks, suggesting connections, accelerating tasks or challenging assumptions correctly. Finally, students use their findings to create their own version of 鈥淐ritical AI use鈥� guidelines.

This is scalable: at UG level, the teacher provides a template with a prompt and bias categories; the students can compare AI responses to the same prompt across different cultural or theoretical frameworks (e.g., "explain unequal access to health" through economic vs. anthropological vs. public health lenses), to show AI privileges or erases. At PGT level, students can make up their own categories and reflect on whether the tool enables or hinders undisciplinarity.

PGRs can use AI to challenge assumptions, generate counterarguments and conduct meta-analysis across multiple tools.

Key questions to guide the co-design of the activity: what are we teaching students if their work can be effectively conducted by AI? How can we teach/learn judgment to know when AI's effectiveness is illusory? How do we address inequities in AI access? How do we assess the risks of generative AI and the use of AI to critique AI? Where do we draw lines on what is pedagogically and ethically valuable as part of an undisciplinary learning journey?

Audience: all staff across the school as it would feed into a School-level AI policy to address lack of guidance from Faculty/University. PGRs most welcome, both as future teachers and as contributors bringing research-level perspectives on AI's impacts.

Active workshop format: Discuss the ethical implications and aspects of critical engagement with AI (acknowledging that "critical engagement" still means engaging, so what are the sustainability, equity, and pedagogical costs?); try a simplified scenario immersion planning (as I tell students I would never ask them to do something I haven鈥檛 done before). And/or I could present an example based on my teaching.

Preparation: think about a case study or problem/question that is part of current teaching; where might AI struggle with it? What would a student need to know to recognise the struggles and limitations/benefits?

Outcomes:

1. Shareable structure for a 鈥渂ias and benefits of AI鈥� activity that can be adapted to different courses and levels
2. Discussion feeding into broader academic AI/integrity discussions: What principles should underpin our undisciplinary approach to AI?
3. Feedback from PGRs: how does AI impact their research practices?
4. Insights on how AI can manifest (?) itself in our undisciplinary learning. Does AI's training privilege certain disciplines? How does this affect students learning to work across boundaries?

Caveats: is it sustainable? Is it ethical? And is it needed, regardless? This idea recognises that our undisciplinary context creates unique challenges and opportunities. Students must already navigate epistemological differences between disciplines, and AI could become another interlocutor in that navigation. By making critical AI engagement explicit, we prepare students for the messy realities of undisciplinary inquiry in an AI-saturated world, and open to School policies grounded in pedagogical practices rather than reactive prohibition.