How did this project start? And why does it matter?
Introduction by Erin, Development Editor
Introduction by Malcolm, Concept Editor
Introduction by Reina, Industry Advisor
The Assignment
The Experiment

The Assignment

01 The students began by forming small groups, each choosing a single misconception about generative AI to debunk.  

02 From there, they picked from themes like truth, empathy, authorship, or agency, big concepts that shape how we trust machines. 

03 They turned their argument into a short video essay, split across three or four Instagram Reels that ran five to eight minutes in total.  

04 To give their critique teeth, they committed to a specific storytelling genre, mockumentary, parody, deconstructive tutorial, PSA etc.  

05 They were required to use generative AI but only as a strategic critic, never as a content creator: no AI scripts, no AI images, no AI theories. 

06 Their use was limited to eight prompts, all contained within a single conversation thread, and they screenshotted every exchange as evidence. 

07 The real proof of their argument came from the AI's own failures, screenshots of its contradictions, nonsense, or shallow answers. 

08 Every technical flaw was grounded in a human concern: a moment of frustration, a lost creative opportunity, an ethical blind spot.  

09 Before filming, they presented their plan in class, walking through their catalyst moment, key AI failures, the theory behind it, and their narrative blueprint. 

10 Finally they wrote a detailed report proving they had pursued a truly human-centric critique – ending with a complex question, not a tidy answer.

The Experiment

01 Can students command AI as a subordinate analyst rather than a creative crutch? The assignment tests whether they can use AI strategically for SWOTs, blind spots, and devil’s advocacy while resisting the temptation to let it write, imagine, or decide for them. 

02 Can they turn an AI’s failure into evidence? The real proof is not a polished output but the machine’s contradictions, non-sense, or shallow answers. Students must learn to see error as data. 

03 Can they translate a technical limitation into a human dilemma? Every flaw must land as a lived concern: frustration, lost creativity, ethical unease. No abstract lectures allowed. 

04 Can they commit to a genre with integrity, not as a costume? Whether mockumentary or deconstructive tutorial, the genre must serve the argument. No ironic distance, no style without substance. 

05 Can they show the seams of their own thinking? The process is part of the product. Students must reveal dead ends, revisions, and unresolved questions, ending in a web rather than a tidy clearing.
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