Workshop held in classroom at Mary Lin Elementary
Overview
Children know their own places in ways that generative AI does not. Community Futures uses that difference as a starting point for critical AI literacy: children imagine futures for a creek behind their school, generate images and stories with AI, then build puppets, edit scripts and perform their own alternatives.
The contribution is an adaptable workshop framework that brings together local knowledge, tangible making and design futuring. It positions children as authors who can question, revise and reject AI outputs.
The challenge
AI literacy often happens inside a chat window, separated from the places, relationships and materials that matter to children. At the same time, generic AI narratives can smooth over ecological problems, disagreement and local experience.
We asked two questions: How does a familiar local environment help children recognize AI’s limitations? And how can making and performance help them resist AI-generated narratives and express more complex futures?
A familiar creek as the starting point
The workshop was developed with teachers at Mary Lin Elementary School in Atlanta. The creek was already part of the children’s school activities and everyday experience, giving them a shared reference for judging AI outputs.
58 students attended across two days, with a different cohort each day. The research included 37 children with consent and assent; 31 completed both surveys. Each workshop lasted about five hours, including breaks, with children working in groups of four or five.
workshop Design
01 — Establish local knowledge. After an introduction to AI, children documented what they knew and valued about the creek: its habitats, pollution, play, memories and community life.
02 — Imagine a future. Groups proposed a “Big Idea” for the creek, then generated good and bad news headlines from 2050. Their own creek photographs anchored AI-generated visual scenarios.
03 — Make and question. Each child made a puppet with a position on the imagined future: support, disagreement or mediation. Groups generated a skit, printed it, crossed out lines and rewrote dialogue.
04 — Perform and reflect. Children staged their revised stories, improvised where needed and discussed what they changed, what AI missed and whose future the story represented.
Making and performance
Tools and research approach
The toolkit combined MagicSchool AI’s chatbot, image generator and skit generator with a guided Community Futures booklet, printed scripts, cardboard, felt, markers and recycled materials.
We analyzed workbooks, AI outputs, edited scripts, puppet photographs, field notes and audio reflections. Two researchers developed a shared codebook through discussion, tracing resistance, acceptance and improvisation. Paired pre/post surveys captured self-reported familiarity with AI concepts.
Findings - Quantitative
Across 31 paired surveys, mean self-reported familiarity increased on a five-point scale: large language models from 1.87 to 3.32; image generation from 3.87 to 4.68; and AI bias from 2.19 to 3.42. The paper reports p < .001 for all three comparisons.
These results describe perceived familiarity, not demonstrated learning. The stronger qualitative story is the children’s ability to question outputs, revise narratives and take ownership through material making and performance.
Findings - QUALITATIVE
Rooted in Local Concerns
Children evaluated generated futures against a creek they had visited, heard, smelled and played beside. They recognized ecological damage and social realities that polished images and generic stories left out.
Their experience acted as a reference point for critique. Rather than treating the model as the authority, they could point to specific differences between its outputs and the place they knew.
Children resisted Cheesy endings
16 of the 37 participants described generated narratives as “cheesy.” Children challenged tidy resolutions and character behavior that did not fit their stories, including a group hug that made little sense for a fish puppet.
Editing and performance gave that critique a practical outlet. Children removed lines, added detail and humor, and preserved disagreement instead of accepting a neatly resolved AI ending.
Children’s revisions
Beautiful did not mean truthful
AI-generated creek images often looked more colorful, enchanted or pristine than the children intended. Some wanted pollution and ecological harm to remain visible in their imagined futures.
This aesthetic mismatch made the model’s limitations concrete: an appealing image could still misrepresent a place. Children used their local knowledge to ask for less idealized, more specific representations.
Design implications
Leveraging Local Knowledge- Start with knowledge learners already hold. A familiar place creates a meaningful basis for evaluating what a model gets wrong.
Tangible Engagement - Make revision tangible. Printed scripts, physical characters and performance turn disagreement with AI into visible, collaborative action.
Children’s Hand-On Making
Design Futuring as Scaffolding - Keep multiple futures open. Support conflict, uncertainty and competing ecological and social viewpoints rather than treating one polished output as the answer.
Children’s envisioning Creek Futures as Backdrop
Reflection and next steps
The project suggests that critical AI literacy can be hybrid (physical+digital) and situated. The design opportunity is to create situations where learners can bring their own expertise into the interaction and act on their disagreement.
The study was short, based at one site and did not include a control group. Next steps include performance-based assessments, longer-term follow-up and workshops in other settings to examine learning, retention and transfer.
Funding
Supported by US National Science Foundation award 2335974 and the Foundations and Applications of Generative AI award from Georgia Tech’s Institute for Data Engineering and Science (IDEaS). With thanks to Mary Lin Elementary School and Ms. Carter.
Publication
Community Futures: Hybrid and Situated Critical AI Literacy. AIED 2026. Authors: Supratim Pait, Sylvia Janicki, Yuhan Hou, Michael Nitsche and Noura Howell.