Constructing Dreams Using Generative AI
Open accessAbstract
Generative AI tools introduce new and accessible forms of media creation for youth. They also raise ethical concerns about the generation of fake media, data protection, privacy and ownership of AI-generated art. Since generative AI is already being used in products used by youth, it is critical that they understand how these tools work and how they can be used or misused. In this work, we facilitated students’ generative AI learning through expression of their imagined future identities. We designed a learning workshop - Dreaming with AI - where students learned about the inner workings of generative AI tools, used text-to-image generation algorithms to create their imaged future dreams, reflected on the potential benefits and harms of generative AI tools and voiced their opinions about policies for the use of these tools in classrooms. In this paper, we present the learning activities and experiences of 34 high school students who engaged in our workshops. Students reached creative learning objectives by using prompt engineering to create their future dreams, gained technical knowledge by learning the abilities, limitations, text-visual mappings and applications of generative AI, and identified most potential societal benefits and harms of generative AI.
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Many studies report enhanced critical thinking, reflection, and problem-solving with GenAI.
“More importantly , many studies report enhanced critical thinking [70,77,99,100,103,111,114,119,132,142,145,150], reflection, and problem-solving [70,128,141].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [132]
The Workshop Design and Discussion sections describe guided reflection and report that students considered AI’s biases and expressed more nuanced views about its classroom use. This supports the reflection component, but the paper does not report enhanced critical-thinking or problem-solving outcomes. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAI literacy and ethical reasoning objectives in the reviewed studies develop students' understanding of how AI works, their ability to assess truthfulness, detect bias, and recognize the limits of creativity.
“This objective develops students' understanding of how AI works, their ability to assess truthfulness, detect bias, and recognize the limits of creativity.”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [132]
The Technical Learning Objectives section aims to teach students about text-to-image algorithms and their limitations; the Ethical Learning Objectives section includes identifying bias and fake media that can spread misinformation (page numbers unavailable). The paper does not establish that students developed an ability to assess truthfulness, and its discussion of limitations concerns text-to-image generation rather than creativity generally. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAI literacy and ethical reasoning objectives address deficits in technical understanding and resistance to deception.
“It addresses deficits in technical understanding and resistance to deception [132-134].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [132]
The Technical Learning Objectives section targets students’ understanding of generative AI’s abilities and limitations. The Ethical Learning Objectives and Ethical Impact sections address fake media, misinformation, and the difficulty of distinguishing generated media from real data. The paper does not show that the activity improved students’ resistance to deception. (LLM, conf=0.83)
Experimental — accuracy not yet measuredIn visual arts, learners employ Midjourney, DALL·E, and related text-to-image tools, repeatedly adjusting prompts to refine visual detail and aesthetics.
“In visual arts, learners employ Midjourney, DALL·E, and related text-to-image tools, repeatedly adjusting prompts to refine visual detail and aesthetics [111-116,132,143]”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [132]
The Constructing Dreams section reports that high-school students used Dream Studio, a Stable Diffusion text-to-image tool, and edited prompts until satisfied. The section on expressing imaginations says they repeatedly changed prompts, style options, and visual details to reach their goals. The paper does not report learners using the specifically named tools Midjourney or DALL·E. (LLM, conf=0.88)
Experimental — accuracy not yet measuredWithin the AI-empowered framework, students actively set prompts and control content generation, while AI, functioning as an autonomous collaborator, continuously analyzes learner preferences, behaviors, and project progress to adaptively optimize its models for individual needs.
“Within this framework, students actively set prompts and control content generation, while AI, functioning as an autonomous collaborator, continuously analyzes learner preferences, behaviors, and project progress to adaptively optimize its models for individual needs [70,96,108,132,144].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [132]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Learner differences-cultural background, personal interests-and concerns about academic integrity further shape uptake.
“Learner differences-cultural background, personal interests [129,132]-and concerns about academic integrity [85] further shape uptake.”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [132]
The Discussion (page unavailable) reports different classroom-use attitudes among the US and India workshop groups, supporting a link between learner context and uptake. The paper uses students’ personal aspirations in its learning activity, but does not show that personal interests shape uptake. Academic-integrity concerns are cited separately to [85], not attributed to this source. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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Students as makers using technologies
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- Authors
- Ali, S., Ravi, P., Williams, R., DiPaola, D., & Breazeal, C.
- Affiliations
- Massachusetts Institute of Technology (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2024-03-24
- Volume
- 38
- Issue
- 21
- Pages
- 23268-23275
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W4393145628
- Cited by
- 69
- References
- 24
- Citations by year
- 2026: 15 · 2025: 30 · 2024: 19 · 2023: 4 · 2021: 1
- FWCI
- 24.37
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- generative AI, text-to-image generation, high school students, prompt engineering, AI ethics education, creative learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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