Search for: All records

A goal of the AIED community is to create equitable systems; yet, we lack a cohesive viewpoint on how to do so. In the present work, we propose power as this organizing principle. We utilize the data feminism framework to showcase how we might balance power, focusing on learner engagement. We utilize multimodal data from ten middle school girls in a virtual computer science camp to discuss how the AIED community might create systems of equity that support all learners. 

Critical AI literacy enables understanding of the limitations of AI. In this work, we investigated how Black girls (N=11, ages 9-12) critically engaged with generative AI (genAI) through exploring ChatGPT’s limitations. Learners used various approaches and leveraged their funds of knowledge (e.g., knowledge of pop culture) to investigate where genAI did not perform satisfactorily. We discuss how taking an asset-based approach can support critical AI literacy. 

Youth regularly use technology driven by artificial intelligence (AI). However, it is increasingly well-known that AI can cause harm on small and large scales, especially for those underrepresented in tech fields. Recently, users have played active roles in surfacing and mitigating harm from algorithmic bias. Despite being frequent users of AI, youth have been under-explored as potential contributors and stakeholders to the future of AI. We consider three notions that may be at the root of youth facing barriers to playing an active role in responsible AI, which are youth (1) cannot understand the technical aspects of AI, (2) cannot understand the ethical issues around AI, and (3) need protection from serious topics related to bias and injustice. In this study, we worked with youth (N = 30) in first through twelfth grade and parents (N = 6) to explore how youth can be part of identifying algorithmic bias and designing future systems to address problematic technology behavior. We found that youth are capable of identifying and articulating algorithmic bias, often in great detail. Participants suggested different ways users could give feedback for AI that reflects their values of diversity and inclusion. Youth who may have less experience with computing or exposure to societal structures can be supported by peers or adults with more of this knowledge, leading to critical conversations about fairer AI. This work illustrates youths' insights, suggesting that they should be integrated in building a future of responsible AI. 

The importance of considering local context and partnering with target users is well established in co-design. Less common is an examination of the adaptations needed when deploying the same co-design program across heterogenous settings to maximize program efficacy and equity. We report on our experience co-designing educational games with six culturally and socioeconomically diverse afterschool sites over two years, and insights from interviewing ten program administrators across all sites. We found that even within the same afterschool program network, site differences in organizational culture and resources impacted the effectiveness of co-design programs, the co-design output, and expectations for student engagement. We characterize our afterschool partners into different archetypes – Safe Havens, Recreation Centers, Homework Helpers, and STEM Enrichment Centers. We provide recommendations for conducting co-design at each archetype and reflect on strategies for increasing equitable partnerships between researchers and afterschool centers.