# Constructing Dreams using Generative AI

arXiv: https://arxiv.org/abs/2305.12013 · HTML: https://mlsanity.com/paper/2305.12013/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 5 of 5)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education](https://mlsanity.com/paper/2305.00290/) (2023)
- [Designing AI Learning Experiences for K-12: Emerging Works, Future Opportunities and a Design Framework](https://mlsanity.com/paper/2009.10228/) (2020)
- [Identifying Ethical Issues in AI Partners in Human-AI Co-Creation](https://mlsanity.com/paper/2204.07644/) (2022)

## Built on by (most cited first, 8 of 8)

- [DREAM: Scalable Red Teaming for Text-to-Image Generative Systems via Distribution Modeling](https://mlsanity.com/paper/2507.16329/) (2025)
- [From Guidelines to Governance: A Study of AI Policies in Education](https://mlsanity.com/paper/2403.15601/) (2024)
- [JANUS: A Lightweight Framework for Jailbreaking Text-to-Image Models via Distribution Optimization](https://mlsanity.com/paper/2603.21208/) (2026)
- [Future You: Designing and Evaluating Multimodal AI-generated Digital Twins for Strengthening Future Self-Continuity](https://mlsanity.com/paper/2512.06106/) (2025)
- [Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems](https://mlsanity.com/paper/2507.21157/) (2025)
- [Thematic and Task-Based Categorization of K-12 GenAI Usages with Hierarchical Topic Modeling](https://mlsanity.com/paper/2508.09997/) (2025)
- [Simulating Life Paths with Digital Twins: AI-Generated Future Selves Influence Decision-Making and Expand Human Choice](https://mlsanity.com/paper/2512.05397/) (2025)
- [Where Does AI Leave a Footprint? Children's Reasoning About AI's Environmental Costs](https://mlsanity.com/paper/2603.27376/) (2026)

## Abstract (arXiv)

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.