Background

In the past few years, an explosion in the capabilities of artificial intelligence (AI) and deep generative models have given rise to a new genre of creativity and making---AI-generated art. Despite the growing body of literature surrounding AI education and creativity, as well as the emergent literature on the AI art tools themselves, there exists a research gap for work that has been conducted that evaluates the design and feasibility of such tools for an educational audience. As AI continues to dominate the computing landscape and become further integrated in society, educational methods that promote AI literacy, creativity, and inclusion in this field are paramount. In this work, we present a curation of presently-available tools and platforms which support the creation of AI-Generated Art. We evaluate the design and feasibility of these tools from the perspective of an educational user. Through this work, we hope to provide a grounding for future research and development of both tools and curricula incorporating AI-generated art.

Related Work

Prior work involving AI literacy and children focused on developing training models for teaching and developing AI literacy. In this section we discuss recent research that supports our explorations and motivations to further investigate this concept model.

Artificial Intelligence Education

Prior work has discussed the benefits of developing education models for children to encourage interest in the STEM fields. Previous literature has discussed the need to introduce fundamental concepts at varying ages. Amershi et al. [ 1] discusses that involving people more actively in the early stages can lead to a high acceptance rate. Research has shown that students in higher education levels have basic and fundamental skills in programming, suggesting that AI education usually targets children in the elementary level[ 2].

AI Tools and Resources for Children

AI has become a increasingly present aspect of our everyday life. From smartphones to voice assistants, AI has been an important topic to teach in education settings. These devices have become accessible to children of all ages and educating children of the fundamentals and mechanics of these systems has been at the forefront of AI literacy research.Developing curriculum to support AI literacy has generated various education models. Research has explored creative ways such as tinkering to encourage interest in the field and to develop fundamental concepts of AI.

Accessible AI Education Tools in
Underrepresented Communities

Prior work has proven that the need to develop fundamental AI concepts in children is important.It is important that such curriculum and education models be inclusive and accessible to all, such as for persons in underrepresented communities such as people of color and women in STEM and from all demographics and socioeconomic statuses.

Tinkering with AI

Recent work has discussed the effectiveness of tinkering with AI to develop familiarity with fundamental concepts. Tinkering is defined as an experiential learning approach used to develop thinking, creativity, collaboration and communication skills. This term is used to describe learning undertaken by students able to gain and apply knowledge, skills, and feelings by being involved in a “direct encounter with the phenomena being studied rather than merely thinking about the encounter” [ 3 ]. Tinkering with digital/physical computing systems has gained significant attention in recent years [4] with children having access to many digital products.

References

[1] Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. 2014. Power to the People: The Role ofHumans in Interactive Machine Learning. AI Magazine 35, 4 (Dec. 2014), 105–120. https://doi.org/10.1609/aimag.v35i4.2513

[2] Randi Williams, Hae Won Park, Lauren Oh, and Cynthia Breazeal. 2019. PopBots: Designing an Artificial Intelligence Curriculum for Early Childhood Education. Proceedings of the AAAI Conference on Artificial Intelligence 33, 01 (July 2019), 9729–9736. https://doi.org/10.1609/aaai.v33i01.33019729 Number: 01.

[3] Rubin L. B. Borzak, L. 1981. Field study: A sourcebook for experimental learning

[4] Dennis Reidsma, Haruhiro Katayose, and Anton Nijholt (Eds.). [n.d.]. Advances in Computer Entertainment. https://link.springer.com/book/10.1007/978-3-319-03161-3